Smart Sunscreen Vending Machine Design Project PDF

Smart Sunscreen Vending Machine with Advanced Face Skin Analysis A Design Project Presented to The Faculty of Trimex Colleges In Partial Fulfillment Of the Requirements for the Degree Bachelor of Science in Computer Engineering...

Smart Sunscreen Vending Machine with Advanced Face Skin Analysis A Design Project Presented to The Faculty of Trimex Colleges In Partial Fulfillment Of the Requirements for the Degree Bachelor of Science in Computer Engineering by: Gayoba, Hershey S. Llovia, Nemuel L. Ocenar, Glyza L. December 2024  Certification This Design Project/Thesis entitled “Smart Sunscreen Vending Machine with Advanced Skin Analysis” prepared and submitted by Gayoba, Hershey S., Llovia, Nemuel L., and Ocenar, Glyza L., in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Engineering, has been examined and recommended for Oral Examination. Marvin O. Mallari, PhD Adviser _________________________________________________________________________ APPROVAL SHEET Approved by the Oral Defense Panel on ____________ ___________________ with a rating of _____________. RITO A. CAMIGLA Jr., EdD Chairman KIERVEN R. DE MESA, PhD CRISTOPHER JAY A. CAPILI, ECT Member Member Accepted in partial fulfillment of the degree of Bachelor of Science in Computer Engineering. KIERVEN R. DE MESA, PhD RITO A. CAMIGLA Jr., EdD Department Head, Vice President for College of Engineering Academics and Students Services ii  Abstract Thesis Title: Smart Sunscreen Vending Machine with Advanced Face Skin Analysis Authors: Gayoba, Hershey S.;Llovia, Nemuel L.; Ocenar, Glyza L. Degree: Bachelor of Science in Computer Engineering Institution: Trimex Colleges Academic Year: 2024 – 2025 Number of Pages: 1 _________________________________________________________________________ The Philippines, a tropical country with high sun exposure, faces significant public health concerns related to skin protection, particularly due to ultraviolet (UV) radiation. Skin cancer and other UV-related conditions, such as premature aging, are prevalent due to inadequate access to effective sun protection products. To address this gap, the development of a Smart Sunscreen Vending Machine equipped with AI- powered skin analysis has been proposed. This vending machine aims to make sunscreen more accessible while providing personalized recommendations tailored to the user’s unique skin type and UV exposure needs. The study's objectives focus on three main areas: creating a manual payment system for sunscreen dispensing, designing an AI-powered facial analysis system to categorize users into specific skin types, and optimizing algorithms to recommend suitable sunscreen based on skin type and environmental conditions. The machine’s strategic placement in outdoor areas within Biñan, Philippines, will ensure accessibility and convenience, particularly for individuals with limited knowledge of skin protection. This initiative is poised to positively impact public health by encouraging better sun protection practices and reducing risks associated with UV exposure. Furthermore, the study provides a unique intersection of public health, technology development, and environmental awareness, offering valuable insights for dermatologists, technology developers, and environmental agencies. Keyword: sunscreen, vending machine, AI-powered, face skin analysis, skin types, customization, recommendation algorithm, UV protection, customer needs, payment system iii  ACKNOWLEDGEMENT We extend our heartfelt gratitude to Dr. Marvin Mallari, our esteemed thesis advisor, whose guidance, expertise, and unwavering support have been instrumental in shaping this research endeavor. Their valuable feedback and encouragement have been invaluable throughout every stage of this journey. Special appreciation is extended to Ma’am Analyn Lopez Ocenar , our Editor, for their pivotal contributions to the quality of our work. Your expertise and dedication significantly enhanced the quality and depth of our work. We are deeply thankful to Dr. Kierven de Mesa and Dr. Rito Camigla Jr., Engineering Faculty Member, for imparting their expertise and knowledge, both of which played a crucial role in the completion of this thesis. Our gratitude also goes to Trimex Colleges for giving us the opportunity to showcase our research effort, which greatly facilitated our research efforts. We would like to express our heartfelt thanks to our families and friends for their unwavering support, understanding, and encouragement iv throughout this academic journey. Their patience and belief in us have been a constant source of strength and inspiration. Finally, we extend our appreciation to all those who have contributed directly or indirectly to the completion of this thesis. Your support and assistance are deeply appreciated. The Researchers v  Dedication This thesis is a tribute to the unwavering support, guidance, and camaraderie that have shaped our academic journey. With profound appreciation, we dedicate this work to: Our Families. Your steadfast encouragement, sage advice, and unwavering belief in our potential have been the guiding lights illuminating our path. Through every challenge and triumph, you've provided the foundation upon which we've built our aspirations. This achievement stands as a testament to your enduring influence. Our Mentors and Advisors. Your expertise, mentorship, and relentless pursuit of excellence have challenged and inspired us to reach new heights. Your guidance has not only expanded our knowledge but also sharpened our critical thinking and analytical skills. We are deeply grateful for your invaluable contributions to our intellectual development. Our Friends and Loved Ones. Your steadfast support, camaraderie, and encouragement have been the catalysts for our perseverance and determination. In moments of doubt and uncertainty, your belief in our abilities has fueled our resolve to overcome obstacles and pursue our goals relentlessly. This journey has been enriched immeasurably by your unwavering presence. vi  To All Those Who Have Contributed. Whether through a word of encouragement, a thoughtful gesture, or a shared vision, your support has been instrumental in our success. Each interaction, no matter how fleeting, has left an indelible mark on our journey. This thesis is a testament to your collective impact and a reflection of our profound gratitude. May this work stand as a testament to the power of dedication, perseverance, and collaboration. We dedicate it to each and every one of you with heartfelt appreciation and enduring respect. HSG GLO NLL vii  Table of Content Title Page.……………………........................…………..………….….…… i Table of Content ……………………….…….….………….…………..…..…viii List of Appendices ……………….……….………….…………….…….…… xi List of Figures ………….…………….…….……………………….……..…….xii List of Tables ……………………………………….…………..……….……. xiv I. Chapter 1: The Problem and Its Setting Introduction 1 Background of the Study 4 Conceptual Model 6 Statement of Objectives 7 Scope and Limitation 8 Significance of the Study 10 Definition of Terms 12 II. Chapter 2: Review of Related Literature and Studies Related Literature 15 Foreign Literature 15 viii  Local Literature 20 Related Study 25 Foreign Studies 25 Local Study 31 Synthesis of the Study 35 III. Chapter 3: Methodology Research Design 38 A. Developmental Research Method 38 B. System Development Method 40 Population of the Study 42 Sources of Data 43 Respondents of the Study 44 Research Instrument 45 Evaluation and Scoring 47 Data Gathering Procedure 50 Statistical Treatment of Data 51 System Planning and Requirement Analysis 53 System Hardware and Software Requirements 53 System Design 75 System Block Diagram 76 ix  System Flow Diagram 78 System Dataflow Diagram 80 System Development 83 System Hardware Development 83 System Software Development 93 System Architecture 96 System Structural Architecture 96 System Hardware Architecture 97 System Software Architecture 98 System Integration and Testing 101 References 104 x  List of Appendices Appendix A Research Letter 108 B Figure and Table 110 C Curriculum Vitae 132 xi  List of Figures Figure 1 Conceptual Model of the Study………. 110 2 Evolutionary Prototyping Model 110 3 ISO/IEC 25010 Quality Model 111 4 Quality in use Model ISO/IEC 25010 111 5 System Block Diagram 112 6 System Flowchart 112 7 System Data Flow Diagram Level 0 113 8 System Data Flow Diagram Level 1 114 9 System Data Flow Diagram Level 2 114 10 Raspberry Pi 4 Model B 114 11 Raspberry Pi Camera Module V2 8 Megapixels 115 12 Multi Coin Acceptor Programmable 115 13 Bill Acceptor 115 xii 14 Arduino UV Sensor using the VEML6075 116 15 Tactile Pushbutton Switch Momentary 116 16 Load Cell HX711 Weight Sensor 116 17 HC-SR04 Ultrasonic Distance Sensor 116 18 DHT 22 Sensor 117 19 DHT11 Temperature and Humidity Sensor 117 20 Python 117 21 Visual Studio Code 117 22 Arduino IDE 118 23 Setup of Developed System 118 24 Actual System Hardware Architecture 118 25 CNN General Architecture 119 26 Python IDE 119 27 Arduino Uno IDE 119 28 Level of Testing 120 xiii  List of Table 1 Likert Scale of the Level of Accuracy 120 2 Likert Scale of the level of Functionality and Effectiveness 120 3 System Software Requirements 121 4 System Hardware Requirements 122 5 Raspberry Pi Camera Module v2 Technical Specification 123 6 Raspberry Pi 4 Model B Technical Specifications 124 7 Python/C++ System Specifications 128 8 Visual Studio Code System Specifications 128 xiv  CHAPTER 1 THE PROBLEM AND ITS SETTING Introduction The Philippines is a country where sun protection is a critical public health concern. According to Richard (2022), consistent daily use of SPF 15 sunscreen can reduce the risk of squamous cell carcinoma (SCC) by approximately 40% and lower the risk of melanoma by 50%. Sunscreen helps prevent premature skin aging caused by the sun, such as wrinkles, sagging, and age spots. To effectively protect your skin, it is recommended to choose a broad-spectrum sunscreen with an SPF of 30 or higher. Since the Philippines is a tropical archipelago receiving constant sunlight throughout the year, it faces a significant risk of skin cancer due to the high levels of UV radiation exposure. Following the findings of Vergara & Blanco(2022)which investigates the impact of surface urban heat islands (UHIs) on health risks in the Philippines, the analysis revealed that urban areas experience significantly higher temperatures than surrounding rural areas, particularly during the hot dry season. This phenomenon, combined with high population density in urban centers, increases the vulnerability of residents to heat-related health risks. The study of Danilo V. (2024) confirms the hypothesis about the urgent health implications of rising temperatures in the Philippines. It highlights how extreme heat events, exacerbated by climate change, pose significant risks to public health, particularly among vulnerable populations. The analysis of Anika B & Eva P. (2023) aligns with the understanding that increased ultraviolet radiation from the sun, caused by ozone depletion, is linked to a higher risk of skin cancers. Rising air pollution contributes to conditions like atopic dermatitis, psoriasis, and acne. Higher temperatures disrupt the skin's microbiome, exacerbating skin diseases and increasing the risk of heat- related illnesses. Extreme weather events, such as floods and wildfires, cause direct skin injuries, infections, and worsen existing skin conditions. These effects are disproportionately felt by vulnerable populations, highlighting the need for dermatologists to understand the connection between climate change and skin health and advocate for policies that mitigate its impacts. The study underscores the critical need for comprehensive urban planning strategies to mitigate the effects of urban heat islands (UHIs) and safeguard public health in the context of climate change. As heatwaves intensify and temperatures rise due to global warming, the risks associated with excessive sun exposure have become increasingly pronounced. Despite growing awareness of skin cancer risks, a significant portion of the Filipino population continues to neglect sun protection. Factors such as limited access to affordable sunscreen, inadequate knowledge about proper application, and cultural perceptions contribute 2 to this alarming trend. The article emphasizes the pivotal role of sunscreen in shielding the skin from harmful UV radiation. It explains that broad- spectrum sunscreens with high SPF are the most effective in providing protection against both UVA and UVB rays. A holistic sun safety approach, encompassing seeking shade, wearing protective clothing, and avoiding sunburns, is strongly recommended. Zekinah E. (2024) clarifies that the FDA has not found any evidence to support claims that certain sunscreen ingredients pose a health risk, it is essential to select a sunscreen that is free of any ingredients to which you may have sensitivities. By incorporating sunscreen into your daily routine and adhering to other sun safety measures, you can effectively protect your skin from the detrimental effects of UV radiation. This is particularly crucial in the Philippines, where the strong tropical sun and rising temperatures pose significant health risks. Investing in sun protection is not only a personal choice but also a proactive step towards safeguarding your well-being in the face of climate change. The Smart Sunscreen Vending Machine is a revolutionary advancement in sun protection, offering a convenient, accessible, and personalized approach. Equipped with sophisticated skin analysis technology, the vending machine can accurately assess skin tone, type, and sensitivity in real-time. This enables it to provide recommendations for appropriate SPF levels and sunscreen formulations, addressing the 3 shortcomings of traditional sunscreen purchasing methods. Unlike traditional methods, which often involve limited availability and a lack of personalized guidance, the vending machine offers a seamless and informative experience. By providing real-time skin analysis and customized recommendations, it empowers individuals to make informed decisions about their sun protection needs. This challenge underscores the importance of ensuring that innovative solutions like the Smart Sunscreen Vending Machine are accessible to all, particularly in the Philippines where the strong tropical sun and rising temperatures pose significant health risks. Background of the Study Investigating public health and dermatological safety in the Philippines, we have identified access to sunscreen as a significant barrier for many individuals, particularly in remote or underserved communities. The limited availability of effective sunscreen products is a critical issue, as many local retailers do not stock reliable options due to a lack of demand or insufficient awareness of the importance of sun protection. This scarcity is compounded by affordability issues, where even when effective products are available, their cost can be prohibitive for low-income households. Awareness levels regarding the necessity of sunscreen and its proper use are alarmingly low, and educational campaigns on skin health are often scarce. Many local stores carry only counterfeit or substandard products, 4 misleading consumers into believing they are adequately protected. Consequently, this situation places a large segment of the population at increased risk of sun-related health issues, such as skin cancer, sunburn, and premature aging, particularly among vulnerable groups like children and outdoor workers. This critical problem emphasizes the need for creative solutions to close the accessibility gap to high-quality sunscreen. Some of these options include improving the supply chain for reasonably priced, dermatologist-tested sunscreen and launching community-based educational programs that raise knowledge of skin health issues. To address these issues, suggest developing a Smart Sunscreen Vending Machine that incorporates cutting-edge skin analysis technology and gives users personalized advice based on their individual skin types and environmental circumstances. This initiative hopes to reduce sun-related health issues and promote overall well-being by utilizing such technological advancements to improve sunscreen's accessibility and convenience, empowering people to take control of their skin health and encouraging a proactive approach to sun protection within these communities. This study seeks to improve sunscreen's accessibility and convenience, provide people the ability to take control of their skin health, and encourage communities to take a proactive approach to sun protection. Our goal is to foster a culture of awareness and responsibility around sun safety, ensuring that everyone can enjoy the outdoors while protecting their skin 5 from harmful UV rays. Through this comprehensive approach, the researchers hope to instill lifelong habits that promote skin health and overall well-being. Conceptual Model Figure 1. The Conceptual Model of the Study Figure 1 illustrates the operational flow of the Smart Sunscreen Vending Machine, which combines a manual input payment system with an AI- powered skin analysis feature. The input phase includes the user entering the payment amount, making a skin analysis request, and capturing microlens images of their skin. During the process phase, the system validates the payment, verifies the amount, processes the transaction, and 6 captures high-resolution microlens images. These images are then analyzed using Convolutional Neural Networks (CNNs), which have been demonstrated to be highly effective in image classification tasks, particularly in dermatological applications. Research indicates that CNNs can achieve superior accuracy in skin type classification by extracting intricate features from images, thereby enhancing diagnostic capabilities (Salinas et al., 2024). Subsequently, the system calculates any change due, approves the transaction, and triggers the dispensing mechanism for the selected sunscreen product. The output phase consists of delivering payment confirmation, returning change to the user, providing the skin type classification result, and dispensing the appropriate sunscreen product tailored to the user’s specific skin type. This integrated system aims to enhance the customer experience by offering personalized skincare solutions based on individual needs. Statement of Objectives General Objective The general objective of the study on the "Smart Sunscreen Vending Machine with Advanced Skin Analysis" is to develop manual input payment system, an AI-powered face skin analysis feature, and tailored sunscreen product recommendations. This integrated system aims to enhance customer experience by offering personalized skincare solutions based on 7 individual skin types and UV exposure needs, ultimately promoting skin health and effective sun protection. Specific Objectives The study has the following specific objectives: 1. To develop a vending machine that enables the user to manually input a payment amount through coin and bill insertion and automatically dispense sunscreen based on an analysis of their skin type. 2. To design and deploy an AI-powered face skin analysis system that scans and analyzes the user's skin, categorizing them into one of five skin types—normal, dry, oily, combination, or sensitive—after successful payment. 3. To develop and optimize algorithms that recommend and dispense sunscreen products tailored to the customer’s specific skin type, condition, and UV exposure needs, with the quantity of sunscreen dispensed corresponding to the amount of payment made. Scope and Limitation This study focuses on the design and deployment of a Smart Sunscreen Vending Machine equipped with an AI-powered face skin analysis feature in the City of Biñan, Laguna, Philippines. It aims to address the limited dermatological care in the region, where Laguna, with a population of over 8 3 million, has only 41 dermatologists—reflecting a national issue where most dermatologists are concentrated in Metro Manila (Genuino et al., 2024). Convenience sampling technique will be use to gather data. This approach is suitable for the study, as it allows for the selection of participants who are readily available and willing to provide feedback after using the vending machine. Participants will complete a survey questionnaire designed to assess their experiences and satisfaction with the vending machine. The scope encompasses the development of a manual input payment system that allows users to input payments through coins and bills, specify their desired sunscreen amount, and receive personalized skincare recommendations based on one of five skin types—normal, dry, oily, combination, or sensitive—categorized by AI in accordance with American Academy of Dermatology standards (2024). To enhance accessibility, the vending machines will be strategically placed in high-traffic areas such as beauty stores and dermatology clinics. The dimensions of the vending machine are 12 inches wide, 18 inches deep, and 36 inches tall. Despite the promising features of the Smart Sunscreen Vending Machine, certain limitations must be acknowledged. Firstly, the reliance on a manual payment system may present challenges for users who are unfamiliar with cash transactions or lack access to cash. This limitation 9 could hinder accessibility, particularly for younger populations accustomed to digital payment methods and for individuals in low-income households who may not have sufficient cash on hand. Additionally, the vending machine's product offerings will initially be restricted to recommended sunscreen brands, limiting consumer choice. Finally, while the AI categorizes users into predefined skin types, it may not account for unique characteristics or conditions of each individual's skin, such as specific dermatological issues or variations within skin types. Significance of the Study The development and implementation of the Smart Sunscreen Vending Machine equipped with AI-Powered Face Skin Analysis hold significant potential benefits for various stakeholders in the city of Biñan, Philippines. The anticipated results of this study are expected to positively impact the following beneficiaries; Consumers. Residents and visitors of Biñan will gain convenient access to personalized sunscreen products tailored to their unique skin types. The AI-powered skin analysis will provide critical insights into their skin conditions, enabling informed decisions regarding sun protection. This service aims to enhance skin health and reduce risks associated with sun- related issues, such as sunburn, premature aging, and skin cancer. Business Owners. Business owners in Biñan will benefit from the introduction of the Smart Sunscreen Vending Machine by attracting more 10 customers to their establishments, such as beauty stores, dermatology clinics, and aesthetic centers. This innovative system can serve as a unique selling point, enhancing the customer experience and promoting sales of personalized skincare products. By offering convenient access to tailored sunscreen options, business owners can capitalize on the growing demand for skincare solutions and increase foot traffic in their locations. Furthermore, the integration of advanced technology may enhance their reputation as forward-thinking establishments committed to customer wellness. Dermatologist. Dermatologists will benefit from the promotion of tailored sun safety practices facilitated by this innovative system. By providing personalized skincare recommendations through the vending machine, the study aligns with dermatologists' efforts to educate the public about proper sun protection and suitable skincare ingredients for consumer needs. This initiative not only encourages responsible sun habits but also supports dermatologists in addressing the rising concerns related to skin health within the community Technology Developers and Researchers. This study serves as a valuable case for technology developers and researchers in artificial intelligence and vending machine innovations. It offers insights into practical applications of AI in consumer products and highlights areas for further research and development in automated skin analysis technologies. 11 The findings may inspire future projects and collaborations aimed at enhancing consumer experiences through technological advancements. Future Researchers. This study offers a valuable starting point for future research in AI-driven consumer technologies and automated skin analysis. Researchers can build on these findings to refine AI accuracy, expand product offerings, and explore new applications of AI in skincare and other consumer products. The project provides insights that can inspire further innovations in personalized, tech-based solutions for everyday needs. Definition of Terms To enhance clarity and comprehension within the scope of this research, the following terms have been defined both contextually and operationally, providing a comprehensive foundation for the study's key concepts and variables. Accessibility. The ease with which individuals can obtain and utilize products or services. In this context, it refers to the availability of sunscreen products through the vending machine, particularly in areas where access to sun protection has been limited. AI-Powered Face Skin Analysis. A process utilizing artificial intelligence algorithms to evaluate facial skin characteristics, such as skin type (dry, oily, combination, or sensitive) and conditions (sensitivity, 12 oiliness, etc.). This analysis informs personalized product recommendations to enhance skincare. Environmental Awareness. The understanding and concern for the environment and its impact on health. In this study, it refers to promoting sun safety practices to help individuals recognize the link between personal health and the environment, particularly in relation to UV exposure. Personalized Recommendations. Tailored suggestions provided to users based on their specific skin analysis results, preferences, and environmental factors. This process involves matching users with appropriate sunscreen products and formulations that best suit their individual skincare needs. Public Health. The science of protecting and improving the health of populations through education, promotion of healthy lifestyles, and research for disease and injury prevention. This study focuses on the implications of sun protection in enhancing community health and mitigating risks associated with UV exposure. Smart Sunscreen Vending Machine. A technologically advanced vending machine equipped with artificial intelligence (AI) capabilities designed to analyze an individual’s skin type and provide personalized sunscreen recommendations based on real-time assessments. This machine aims to improve access to sun protection products in outdoor locations. 13  Sunscreen. A topical product formulated to protect the skin from the harmful effects of ultraviolet (UV) radiation. Sunscreens can be categorized by their sun protection factor (SPF) levels, which indicate the degree of protection they provide against UV rays, with broad-spectrum formulations protecting against both UVA and UVB rays. Sun Protection Factor (SPF). A measure of how well a sunscreen will protect skin from UVB rays, which are the main cause of sunburn and contribute to skin cancer. Higher SPF numbers indicate greater protection, although no sunscreen can provide 100% protection. By providing these definitions, this study aims to establish a clear understanding of the terms and concepts central to the investigation of the Smart Sunscreen Vending Machine and its potential benefits to public health in the Philippines. 14  CHAPTER 2 REVIEW OF RELATED LITERATURE AND STUDY This chapter presents a comprehensive review of relevant literature and studies, derived from articles, case studies, research papers, and reputable online sources contributed by both local and international researchers. It examines key similarities and differences between prior research and the current study, offering a comparative analysis. Furthermore, this study addresses existing gaps in the literature, particularly focusing on areas that have been previously underexplored. The identified limitations of earlier studies have informed the development of this research, ensuring a more thorough and targeted approach. Related Literature This section presents a comprehensive review of existing literature relevant to the current study. By examining both local and international sources, the review aims to establish a theoretical foundation, highlight key findings, and identify gaps in previous research. The integration of these perspectives will support the analysis and development of the present study, ensuring a well-informed approach to the research problem. Foreign Literature The vending machine industry is experiencing remarkable growth, with projections estimating that the global market will reach $25.25 billion by 15 2027, up from $18.28 billion in 2019. This represents a robust annual growth rate of 6.7% (Vending Design Works, 2021). This expansion is driven by the incorporation of advanced features into modern vending machines, which now offer a diverse array of products beyond traditional snacks and beverages. Today's vending machines cater to a variety of consumer needs, including items such as laundromat supplies, car wash accessories, office and medical inventory, and emergency essentials. Notably, the introduction of machines dispensing personal care products, including skincare solutions, reflects the growing consumer demand for personalized and accessible options. The increase in consumer spending at vending machines further underscores the rising interest in these advanced features, as evidenced by industry reports showing a higher average transaction value (Cantaloupe, 2023). While the trend toward digital transactions is notable, cash payments still retain several key advantages. Cash is widely accepted and does not require special infrastructure or technology, making it accessible in various settings. It provides immediate settlement, eliminating processing delays associated with electronic payments, and offers greater privacy and anonymity for consumers. Furthermore, cash transactions typically do not incur transaction fees, making them cost-effective for both buyers and sellers. However, challenges such as security risks, lack of 16 traceability, and inconvenience for large transactions remain (PayComplete, 2024). As the vending machine industry evolves, enhancing functionality to better meet consumer needs and preferences, particularly in the realm of personal care, becomes increasingly important. AI-powered skin analysis systems are significantly advancing the field of dermatology by delivering precise skin type categorizations through sophisticated algorithms and machine learning techniques. Research by Zhang et al. (2020) and Singh et al. (2021) highlights how these systems utilize user responses to classify skin types, thereby offering personalized skincare recommendations. The integration of payment systems into these platforms, as discussed by Lee and Choi (2022), enhances user engagement by streamlining access to tailored skincare solutions. However, Chen et al. (2023) emphasize the importance of robust data security and ethical considerations to maintain user trust and privacy, reflecting the need for secure handling of sensitive personal information in AI-driven applications. The significance of accurate skin typing is further underscored by Rud (2022), who notes that understanding one’s skin type is essential for selecting effective skincare products and routines. Board-certified dermatologists, including Aanand Geria, MD, affirm that skin type is determined by various factors, including environmental conditions and aging. Rud's methodology for skin type assessment—washing the face, 17 waiting to observe its natural state, and evaluating characteristics such as oiliness or dryness—aligns with current best practices in dermatology. This approach ensures that skincare routines are based on accurate and personalized skin type information, facilitating more effective and targeted skincare management. Choosing the right sunscreen becomes crucial for all skin types to ensure effective protection and maintain skin health. Numerous studies have documented the protective benefits of sunscreen against harmful ultraviolet (UV) rays, which are major contributors to skin cancer and premature aging. The American Academy of Dermatology emphasizes the importance of broad-spectrum sunscreen, which protects against both UVA and UVB rays. UVA rays penetrate the skin more deeply and are primarily responsible for skin aging and wrinkle formation, while UVB rays cause sunburn and significantly contribute to the development of skin cancer (American Academy of Dermatology, 2021). Skin type is a significant factor in determining suitable skincare products, including sunscreen. The American Academy of Dermatology (AAD) categorizes skin into five main types: dry, oily, combination, sensitive, and normal, each with unique characteristics and requirements for optimal care (Fletcher, 2020).For instance, oily skin may benefit from non-comedogenic and oil-free formulations to prevent clogged pores, whereas dry skin types require sunscreens with added hydrating 18 ingredients to prevent further moisture loss. Sensitive skin types, often prone to irritation, benefit from mineral-based sunscreens containing zinc oxide or titanium dioxide due to their mild, physical barrier properties. Furthermore, combination skin requires a balanced approach to address varying oil production across different areas of the face, and normal skin types typically have more flexibility in sunscreen selection. Recognizing these unique needs is essential for creating a personalized sunscreen recommendation, which has been shown to improve user compliance and overall skin health outcomes (AAD, 2020). The blog post by Hashmi and Hashmi (2021) titled "Face Recognition using Deep Learning CNN in Python" offers an insightful exploration into the transformative impact of Convolutional Neural Networks (CNNs) on image processing, particularly in the realm of face recognition. It highlights how CNNs emulate the human visual perception system by focusing on specific regions of images, allowing for efficient pattern recognition and feature extraction. The authors delve into the fundamental mechanics of CNNs, illustrating how they convert images into numerical vectors for further processing by fully connected layers of artificial neural networks (ANNs). The blog also emphasizes the importance of data augmentation techniques in enhancing model accuracy by generating diverse training image variations. Moreover, it outlines the typical workflow for implementing a CNN model in face recognition, which includes pre- 19 processing images, dividing datasets into training and testing sets, and the critical role of hyperparameter tuning in optimizing model performance. Beyond face recognition, the authors discuss the broader applications of CNNs in fields like medical diagnostics, showcasing their potential for early disease detection through image analysis. This comprehensive overview underscores the versatility and efficacy of CNNs, positioning them as essential tools for advancing image classification systems across various domains. Local Literature The vending machine business has seen significant growth in the Philippines, emerging as a unique and attractive entrepreneurial opportunity. Defined as automated machines that dispense products such as food and beverages upon receipt of payment, vending machines provide convenience and accessibility to consumers (Philippine Vending Corporation, 2020). One of the primary motivations for entrepreneurs to enter this market is the potential for generating passive income with relatively low operational demands. Initial investment costs can start at around PHP 20,000, making it accessible for many aspiring business owners (Your Guide to Vending Machine Business in the Philippines, 2021). Furthermore, the automation of vending machines minimizes the need for constant supervision, allowing owners the flexibility to manage their 20 businesses with less hands-on involvement (Philippine Vending Corporation, 2020). The vending machine industry in the Philippines has experienced significant transformation due to technological advancements, evolving from traditional dispensers to sophisticated systems that provide enhanced consumer experiences. Modern vending machines now incorporate features such as touchscreen interfaces, convenient payment options, and real-time inventory management, appealing to tech-savvy consumers who prioritize convenience (Jusoh & Bakar, 2022). These innovations allow for a more interactive and user-friendly experience, ultimately enhancing customer satisfaction. Based on payment modes, there are two main types used in the Philippine vending machine market: cash and cashless. Cash is the predominant mode of payment, accounting for more than two-thirds of total payments made in the vending machines market. This prevalence is attributed to high levels of consumer consensus and the convenience factors associated with using cash (Wresearch, 2020). A notable development in this sector is the integration of artificial intelligence (AI), which facilitates personalized customer interactions. AI- powered vending machines can analyze consumer preferences and behaviors, allowing them to recommend products tailored to individual needs. This personalization is particularly crucial in the context of skincare, as it enables effective recommendations for sunscreen products 21 based on the customer’s unique skin type and condition (FDA Advisory No. 2023-0519, 2023). Understanding one’s skin type—whether normal, combination, oily, dry, or sensitive—is vital for creating effective skincare regimens. Normal skin is well-balanced, exhibiting neither excessive oiliness nor dryness, while combination skin presents characteristics of both, often being oily in the T-zone and normal elsewhere. Sensitive skin, on the other hand, can react easily to environmental factors, resulting in irritation or redness (Philippine One, 2023). Accurate determination of these skin types through AI technologies is essential for providing targeted skincare solutions, thereby enhancing customer satisfaction and promoting healthier skincare practices. In the presentation titled "5 Different Skin Types and Proper Skin Care Routine Explained by Dermatologist," Dr. Sarah Barba-Cabodil highlighted the importance of identifying one’s skin type for effective skincare. She categorized skin into five main types: normal, dry, oily, combination, and sensitive. Normal skin is balanced, not too oily or dry, and typically does not experience significant acne. In contrast, dry skin can appear flaky and may cause itching, necessitating the use of moisturizers. Sensitive skin is prone to irritation and discomfort, often requiring careful selection of dermatological products to prevent complications. Oily skin, characterized by excess oil production, is more susceptible to acne, while combination skin features both oily and dry areas, particularly in the T-zone. Dr. Barba- 22 Cabodil emphasized the need for tailored skincare routines to cater to these different skin types, reinforcing that proper skincare reflects overall health and well-being (UNTV News and Rescue, 2022). As awareness of the harmful effects of UV radiation increases, the demand for effective sun protection products arises. Research indicates that consumers are seeking convenient access to sunscreen and other skincare items, particularly in high-traffic areas such as beaches and parks (Micron Vending, 2024). This demand has prompted the development of specialized vending machines designed for outdoor environments, addressing challenges like product degradation due to heat exposure. For instance, the Weimi Outdoor Sunscreen Vending Machine features an integrated cooling system that maintains optimal temperatures to prevent the degradation of sunscreen products. This innovation not only enhances product longevity but also ensures that customers receive high-quality items upon purchase (Micron Vending, 2024). Additionally, the machine’s large capacity and adjustable slots accommodate a diverse range of products, further enhancing its functionality in outdoor retail settings. The challenge of recognizing skin diseases prevalent in tropical climates, particularly in the Philippines, is significant, as conditions like poor hygiene and pollution can exacerbate various skin issues. Efforts are underway to develop systems that effectively detect and classify a range of skin diseases, including acne vulgaris, atopic dermatitis, keratosis pilaris, 23 psoriasis, leprosy, and warts. Recent advancements in image processing and machine learning techniques have shown promise in this field. By utilizing various pre-processing and segmentation algorithms, researchers can extract crucial features such as texture, edges, and color from skin images. These features are essential for training machine learning classifiers, which can enhance diagnostic accuracy and efficiency. According to Goma and Devaraj (2020), the integration of advanced technology in dermatological practices holds the potential to transform how skin diseases are identified and treated. This is particularly vital in regions where skin conditions can lead to social stigma and impact individuals’ quality of life. The emergence of vending machines equipped with AI functionalities and outdoor adaptability signifies a paradigm shift in how consumers access skincare products. By combining technology that detects skin conditions with convenient retail solutions, vending machines can promote proactive skincare practices among consumers and enhance their overall well-being. For instance, vending machines equipped with diagnostic capabilities could analyze consumers' skin conditions in real time and recommend suitable sunscreen products based on individual skin types and conditions. By categorizing users into skin types based on analyses that reflect their unique needs, these machines effectively address public concerns about skin health while providing convenient access to essential 24 skincare products. In conclusion, the integration of advanced technology into the vending machine industry presents an exciting opportunity to enhance skincare solutions in the Philippines. By leveraging AI and machine learning, these machines can provide personalized recommendations, improving accessibility to vital sun protection products and addressing the public's increasing concern for skin health. Related Study The review of related studies offers a critical examination of previous research, providing valuable insights into the current knowledge base and identifying gaps that the present study aims to address. By analyzing various methodologies, findings, and theoretical frameworks from existing literature, this section contextualizes the study within its academic domain, reinforcing the relevance and necessity of further investigation. Foreign Study The evolution of vending machines has shifted dramatically from simple me chanical systems to sophisticated, digitally enhanced platforms. Historically, these machines provided a convenient, automated way for consumers to access various products. Over time, increasing demand for automation, convenience, and personalized service has driven significant innovations in their design and functionality (Higuchi, 2020). 25  Today, vending machines offer a diverse range of products, including snacks, beverages, and tickets in public spaces. Murena et al. (2020) highlighted those advancements in control systems have facilitated the integration of modern payment methods, such as mobile payments and point-of-sale terminals, enhancing user experience and operational efficiency. Intelligent vending systems now incorporate real-time monitoring and inventory management technologies, making them more adaptable to contemporary consumer needs. Technological advancements have extended to automated payment systems, enabling users to manually input amounts for greater flexibility in various commercial environments (Doe, 2022). Modern vending machines support multiple payment methods, from traditional coins to digital transactions, providing consumers with a wide array of secure options for completing purchases.The introduction of advanced digital interfaces, including touchscreens and high-speed transaction processing, has improved user interaction, creating a more intuitive experience. These developments underscore the role of intelligent systems in simplifying processes and offering tailored services within vending environments. Specifically, the integration of control systems to manage multiple functions points to the potential for these technologies to expand beyond traditional product dispensing (Murena et al., 2020). 26  Consumers today demand smarter and more efficient service from automated systems. The convenience of vending machines, combined with personalized product recommendations, increasingly attracts customers seeking faster, customized solutions (Smith, 2023). Emerging applications in various domains, such as product personalization and algorithm-based recommendations, suggest new possibilities for leveraging intelligent systems. These technologies can optimize consumer engagement by offering solutions based on individual preferences, reflecting broader trends in adaptive technology and consumer-centric services. A study by Karan et al. (2021) developed a cash payment system for vending machines using an infrared (IR) sensor to detect cash. This system efficiently pulls cash inside the machine, while a Raspberry Pi paired with a camera performs image processing to validate the amount inserted. The system demonstrated a high confidence level of approximately 95% to 98% for cash detection, although the Raspberry Pi operated at a frame rate of about 0.8 frames per second. If the cash inserted is insufficient, the system prompts the user to add more funds. The study also explored card payments using RFID cards, verifying the card's tag ID and balance before deducting the transaction amount. Upon successful transaction completion, items are dispensed via controlled motorized action. The overarching goal was to create a user-friendly vending machine that 27 enhances the customer purchasing experience while remaining easily manageable for the owner. There are five primary skin types, each defined by distinct characteristics. Normal skin appears uniform, luminous, and smooth without excessive shine or visible pores, feeling fresh and hydrated. In contrast, dry skin looks clear but dull, often flaky, with no visible pores and may exhibit signs of redness and roughness, making it feel cold and less flexible. Oily skin has a shiny appearance with large pores and uneven texture, often prone to comedones and acne. Sensitive skin shows signs of irritation, including redness, scaling, and discomfort, reacting adversely to various stimuli. Lastly, combination skin displays characteristics of both dry and oily skin, typically featuring dry patches in some areas and an oily appearance in others, particularly in the T-zone. These classifications are supported by studies on skin typology, considering factors like hydration, sebum production, sensitivity, and signs of aging (Oliveira et al., 2023). The consumer demand for personalization has prompted the ongoing development of more sophisticated algorithms and automated systems that respond to unique user characteristics. AI-powered face skin analysis systems have demonstrated promising results in accurately categorizing skin types and recommending tailored products, enhancing customer satisfaction and loyalty (Lin et al., 2022). These systems leverage machine learning to provide personalized skincare solutions based on individual 28 concerns, such as UV exposure and skin type. Algorithms designed to recommend sunscreen products tailored to users' unique skin conditions have been linked to improved consumer outcomes, emphasizing the importance of personalized recommendations in skincare (Lin et al., 2022). Furthermore, users are more likely to engage with systems offering personalized services, particularly in industries like skincare. The growing interest in applying artificial intelligence (AI) to skincare highlights the potential of machine learning (ML) and deep learning (DL) methods for personalizing solutions. Research by Lin et al. (2022) explored the use of analytic methods to identify skin types and conditions such as acne, aiming to recommend tailored products that address specific issues. These advanced algorithms improve the accuracy of skin condition identification, leading to more effective recommendations. Research indicates that user-friendly payment systems significantly influence purchasing behavior, with convenience playing a critical role in consumer engagement (Jangpong & Saknarong, 2023). These systems, combined with the integration of AI-powered face skin analysis and product recommendation algorithms, have transformed the retail experience, particularly in the skincare industry. Collectively, these advancements highlight the necessity of integrating innovative technologies to meet consumer needs and preferences in a competitive marketplace. 29  The integration of AI in healthcare, particularly for skin disease identification, has gained significant traction in recent years, as highlighted by Bizel et al. (2024). Their study examines the limitations of existing algorithms in accurately classifying skin diseases across diverse skin types, emphasizing the challenges faced by image classification systems for non- Caucasian skin tones. By employing Convolutional Neural Networks (CNNs), the authors aim to enhance the classification of skin disease images and address the inadequate performance of applications designed primarily for lighter skin colors. CNNs are pivotal in this context, automatically learning hierarchical features from images and proving highly effective for tasks like skin lesion detection and classification. The methodology implemented by Bizel et al. considers various patient demographics, including age, sex, disease sites, and a broad spectrum of skin tones (white, yellow, brown, black) and lesion stages (early, middle, late). Despite advancements in deep learning techniques, the study reveals persistent challenges in detecting skin cancer within darker skin populations, highlighting the urgent need for more inclusive and representative training datasets. While multiple private applications can detect skin diseases, their performance often declines for darker skin tones, leading to difficulties in identifying conditions like skin cancer. Through a thorough analysis of visual search patterns in skin-related health inquiries, the authors identify 30 vital opportunities to enhance digital health solutions for a broader demographic, advocating for increased accuracy and accessibility in skin disease diagnostics. The work of Bizel et al. significantly contributes to the ongoing discourse on equity in healthcare technology, emphasizing the necessity for AI systems, particularly those utilizing CNNs, to effectively represent the full spectrum of human diversity. Similarly, Saiwae et al. (2023) explore the potential of image processing and deep learning approaches, including CNNs, for human skin type classification in their research published in Heliyon. Their investigation into advanced methodologies for differentiating skin types underscores the growing intersection of technology and dermatological studies. This work complements the findings of Bizel et al. and reinforces the importance of developing robust AI-driven systems that cater to the diverse needs of various skin types, ensuring that healthcare technologies evolve to be inclusive and effective across all populations. Local Study The Philippine market for vending machines has gained considerable traction due to the increasing demand for convenient and accessible consumer products. Recent findings indicate a marked increase in the prevalence of vending machines, particularly in urban areas where convenience and accessibility are paramount (Philippine Statistics Authority, 2023). The rise of smart vending machines, equipped with 31 touchscreen interfaces and mobile payment capabilities, highlights the growing trend of technological integration within retail environments. A study by Punzalan et al. (2023) examined consumer perceptions of vending machine offerings, highlighting a growing preference for personalized product recommendations based on individual needs. The findings reveal that consumers are more likely to engage with machines that provide tailored suggestions, emphasizing the need for AI-driven technologies to enhance the user experience. Furthermore, the study identified a correlation between customer satisfaction and the accessibility of specialized products, particularly in high-demand categories such as skincare and sun protection. The importance of cash as a prevalent payment method in the Philippines is also highlighted, particularly among older consumers and those in less urbanized areas, where cash transactions remain the norm. While the adoption of cashless payment systems and smart technology enhances user convenience and fosters growth within the sector, reliance on cash still plays a crucial role in ensuring accessibility for all consumers. Despite challenges such as high operational costs, the research concludes that the vending machine market in the Philippines is well-positioned for growth, driven by evolving consumer preferences and the balancing act between cash and digital payment systems. 32  According to Smi A (2022), accurately identifying skin type is a foundational element in women's skincare routines. By understanding their unique skin characteristics, women are better equipped to choose products that effectively address their specific needs. The study identifies the most influential aspects impacting product purchases as the product's reviews and its formulation. As awareness of skincare needs continues to rise, consumers are seeking convenient access to effective sun protection products. The emergence of vending machines that cater specifically to this demand represents a promising avenue for the skincare industry. Research indicates that vending machines capable of offering a range of sunscreen products tailored to different skin types are more likely to attract consumer interest and drive sales (Martinez & Reyes, 2024). The integration of AI functionalities in these machines can provide customers with personalized recommendations based on their skin types, helping them make informed choices. The local market's response to vending machines offering skincare products highlights a significant shift in consumer behavior, driven by convenience and the desire for tailored solutions. The focus on enhancing customer experiences through AI technology aligns with global trends, emphasizing the need for continued innovation in the vending machine industry. As consumer preferences evolve, the importance of integrating 33 advanced technologies into vending solutions becomes increasingly clear, positioning these machines as viable platforms for skincare accessibility. In the study of Estrellado (2023), fundamental concepts of artificial intelligence (AI) are explored, highlighting their significant role in transforming various sectors. Algorithms are identified as the essential building blocks of AI systems, comprising a series of computations or rules that allow machines, particularly neural networks, to learn independently. These neural networks, inspired by the structure of biological neurons, enable the processing and recognition of complex patterns within data. The concept of machine learning emerges as a crucial subset of AI, focusing on the development of algorithms that empower machines to learn and adapt without explicit programming. Another vital area discussed is natural language processing (NLP), which facilitates the interaction between computers and human language, allowing machines to comprehend and generate text and speech in a human-like manner. Additionally, educational data mining is examined as an application of AI techniques that analyze educational data, providing insights to enhance learning experiences and outcomes. The use of AI algorithms is also evident in the study by Mendoza et al. (2020), which proposes an FPGA-based skin disease identification system utilizing the Scale-Invariant Feature Transform (SIFT) algorithm in conjunction with the K-Nearest Neighbors (K-NN) method. This innovative 34 approach aims to enhance the accuracy and efficiency of skin disease diagnosis by leveraging advanced image processing techniques. The authors demonstrate that the combination of SIFT, which extracts robust features from images, and K-NN, which classifies these features, provides a reliable framework for identifying various skin conditions. The system was presented at the Twelfth International Conference on Digital Image Processing, emphasizing its relevance in the field of digital health and medical imaging. Synthesis of the Reviewed Studies The growing public awareness of skin health and personalized care has paved the way for advancements in technology-driven solutions, particularly in the realm of vending machines and AI-powered skin analysis. By examining existing research, it is evident that the vending machine industry has transformed significantly from simple automated systems to multifunctional, digitally enhanced platforms. Innovations in control systems, as highlighted by Higuchi (2020) and Murena et al. (2020), have led to the integration of cashless payments, real-time inventory management, and touchscreen interfaces. These developments have elevated user convenience and operational efficiency, making modern vending machines adaptable to consumer needs and contributing to the appeal of automated, personalized retail experiences. 35  Foreign studies reveal that consumer demand for smarter, tailored services is reshaping industries such as skincare. Studies by Smith (2023) and Karan et al. (2021) showcase the potential of vending machines that offer personalized product recommendations through AI-based algorithms, enhancing customer satisfaction and loyalty. AI-powered face skin analysis systems are capable of identifying skin types and recommending suitable skincare products, as shown in studies by Lin et al. (2022). These advancements demonstrate how machine learning can drive consumer engagement by providing customized solutions, an approach gaining traction in both international and local markets. In the Philippine context, vending machines have become increasingly popular, particularly in urban areas, as they offer convenience and accessibility for everyday consumers. Recent studies underscore the Philippine market’s inclination toward machines with cash-based and digital payment options, particularly to cater to the diverse payment preferences across age groups and urbanization levels (Philippine Statistics Authority, 2023; Punzalan et al., 2023). Vending machines featuring tailored skincare products, especially sunscreen, align well with the rising demand for sun protection in the region. Research by Martinez and Reyes (2024) supports the notion that vending machines designed to cater to specific consumer needs, such as skin health, are poised to succeed in this growing market. 36  Complementary advancements in AI are shaping skincare diagnostics. Bizel et al. (2024) emphasize the importance of inclusivity in healthcare technology, illustrating how convolutional neural networks (CNNs) can improve skin disease classification for diverse skin tones, while Saiwae et al. (2023) explore similar image-processing approaches for skin type categorization. Together, these studies highlight the potential of AI to deliver accurate skin analyses across a wide demographic. Local studies, such as Estrellado’s (2023) on foundational AI concepts, illustrate how AI is transforming sectors through machine learning and neural networks, allowing machines to independently recognize complex patterns, which has significant implications for personalized skin care. The shift toward AI-powered, user-centric vending solutions signifies an important development in retail and healthcare, emphasizing the potential to meet evolving consumer needs with smart, accessible, and inclusive technologies. The synthesis of these studies suggests that integrating AI into retail settings, particularly in skincare-focused vending machines, could offer consumers accessible, effective, and personalized solutions that align with broader trends in technology and health equity. 37  Chapter 3 METHODOLOGY This chapter examined the study process as well as the sources of data. The researchers' information from the study's population, instrumentation, and validation, together with the data collection technique will be statistically treated for the development of the. Research Design The system design uses the Developmental Research Method and Evolutionary Prototyping Model for continuous improvement. This approach allows the system to be tested and updated based on user feedback. The design focuses on meeting the project goals and improving key features like skin analysis and sunscreen dispensing. Changes are made as needed to ensure the system is accurate and easy to use. The process ensures the system is effective and user-friendly. A. Developmental Research Method The development of the Smart Sunscreen Vending Machine with AI- Powered Skin Analysis involves a comprehensive process that integrates advanced technology to deliver personalized skincare solutions. The research systematically examines the design, development, and evaluation of the machine, focusing on the accuracy of the AI-powered skin analysis 38 and the user experience. It will assess how well the machine recommends sunscreen based on skin type and how users interact with the system. Data will be collected through user testing, surveys, and behavior analysis to refine the product, ensuring it meets both functional and user needs. This research aims to create a practical, innovative tool for personalized sun protection, enhancing consumer skincare routines. As stated by Adam S. (2024) The hardware development process is driven by AI, machine learning, and consumer demands. Key stages like concept analysis, design optimization, prototyping, testing, and production are enhanced by AI tools, 3D printing, and platforms like Raspberry Pi. Advanced testing methods, including digital twins and security assessments, ensure reliability. AI-powered logistics and materials like graphene improve performance, while trends in miniaturization, edge computing, and 5G integration are shaping the future of hardware, fostering innovation and meeting evolving market needs. 39 B. System Development Method Figure 2. Evolutionary Prototyping Model (Jayendra M. (2023) In the development of this system, the evolutionary prototyping technique will be employed to ensure the system evolves through multiple development stages. According to Jayendra M. (2023), the initial prototype will focus on delivering core functionalities, such as AI-powered skin analysis. As the prototype undergoes several iterations, more features will be integrated based on users skin type, enhanced user interfaces and payment options. Each iteration refines the system’s design and adds more sophistication. As pointed out by Jayendra M. (2023) highlights that feedback is essential at every stage, which helps improve the system’s 40 functionality and alignment with user needs. By the end of the development cycle, the prototype will be a fully functional, tested, and user-approved system ready for deployment. Requirement Gathering. The first phase in developing a prototyping model is requirement analysis. During this phase, the system's needs and expectations are clearly defined. System users are interviewed to understand their expectations and gather insights into the desired features. Quick Design. In the next phase, a preliminary or quick design is created. This is not a complete design but a basic version that offers users an overview of the system. The purpose of this rapid design is to assist in the creation of the prototype. Building Prototype. At this stage, a functional prototype is developed based on the information from the quick design. This prototype is a low- fidelity, working version of the system that allows for early testing and feedback. User Evaluation. The prototype is presented to the client for initial evaluation. This stage helps identify the strengths and weaknesses of the model. Feedback from users is collected to guide further improvements in the system. 41 Refining Prototype. If the user is not satisfied with the initial prototype, adjustments are made based on their feedback and suggestions. Once the user is content with the refined model, a final version of the system is developed, reflecting the approved prototype. Engineer Product. After the final prototype is developed, it undergoes thorough testing before being released for production. To ensure reliability and minimize downtime, the system is maintained and regularly updated to avoid potential failures. Population and Sampling The target population for this research includes individuals who are potential users of the AI-powered Smart Sunscreen Vending Machine, particularly residents and visitors in Biñan City, Philippines, where the machines are deployed particularly beauty stores, dermatology clinics, and aesthetic clinics. A sample of 50 individuals will be selected using a convenience sampling technique, chosen for its practicality in reaching participants who are readily available and willing to provide feedback after using the vending machine. This approach aligns with similar exploratory research, such as the Georgia Tech Face Database study conducted at the Georgia Institute of Technology, which used 50 participants to gather early 42 insights into face recognition technology (Li et al., 2020). Similarly, this sample size provides sufficient demographic diversity such as age, skin type, and gender for meaningful observations and preliminary feedback on usability and product effectiveness, while remaining manageable for an initial study. Sources of Data The primary source of data for this research will be a structured survey using a questionnaire designed to gather insights from potential users of the AI-powered Smart Sunscreen Vending Machine. The survey will include a set of core questions applicable to all respondents, focusing on their experiences using the vending machine, the accuracy of the AI- powered skin analysis, satisfaction with the recommended sunscreen, and their likelihood of using the machine again. To complement the survey data, relevant literature from peer- reviewed journals, theses, and research articles will be reviewed to provide context and support for the study. Sources may include recent studies on the efficacy of AI in skincare analysis, the importance of sun protection across different skin types, and consumer behavior related to sunscreen use. This approach ensures consistent data collection while capturing detailed feedback that reflects the experiences and preferences of the target user groups. 43 Respondents of the Study The respondents for this study will consist of a diverse group of individuals who are potential users of the AI-powered Smart Sunscreen Vending Machine. 50 participants will be selected to ensure representation across various demographics and skin types, allowing for more comprehensive analysis. Using a structured quantitative survey, participants will rate their experiences with the vending machine, including the perceived accuracy of the AI-powered skin analysis, satisfaction with the sunscreen recommendation, and likelihood of using the machine in the future. This quantitative approach will enable the collection of measurable data that reflects the preferences and needs of diverse user groups, facilitating an objective assessment of the vending machine’s effectiveness in addressing various skincare requirements. 44 Research Instrument Figure 3. ISO/IEC 25010 Quality Model, Genot S. (2023) (International Organization for Standardization) Researchers use ISO 25010. Genot S. (2023) quality model to develop the Smart Sunscreen Vending Machine with AI-Powered Skin Analysis. This model focuses on eight key aspects of software quality, functional suitability, reliability, performance efficiency, usability, security, compatibility, maintainability, and portability. It also highlights two main dimensions of quality: how well a product meets specific user needs in real-world situations and the inherent technical properties of the software. The researchers used these standards to establish clear benchmarks for development and testing, ensuring a balance between functionality and user experience. The focus on ISO 25010 allowed them to address potential technical challenges early in the design process. By prioritizing quality characteristics, they created a system that is both reliable and adaptable to various user contexts. The 45 AI-powered analysis ensures the machine delivers accurate and efficient results tailored to individual skin conditions. This approach not only improved software performance but also enhanced user trust and satisfaction with the innovative solution. Figure 4.Quality in use Model ISO/IEC 25010, Dalila A., Latifa B. (2022) Quality-in-use (QinU) and Product Quality specify characteristics from the user perspective. QinU, as defined in the ISO/IEC 25010 standard, refers to the capability of a software product to enhance the effectiveness, productivity, safety, and satisfaction of users in meeting their actual needs while achieving their goals within a specified context of use. The QinU model was applied to assess the Smart Sunscreen Vending Machine with AI-Powered Skin Analysis, focusing on its functionality, usability, reliability, performance efficiency, and supportability. This model 46 facilitated the evaluation of the system’s reliability and user satisfaction, with expert evaluators providing recommendations and completing assessment forms in alignment with ISO/IEC 25010 standards according to Dalila A. & Latifa B., (2022). The feedback provided essential insights into the system's performance gaps, supporting its continuous improvement and validation for high-quality service delivery. Evaluation and Scoring Various rating scales have been developed to measure attitudes and perceptions in research, with the Likert scale being one of the most widely utilized tools. Originally introduced by Rensis Likert in 1932, this scale allows respondents to express the extent of their agreement or disagreement with a given statement, typically across a five- or seven- point continuum. The Likert scale is commonly used in surveys to capture nuanced opinions on a range of topics, offering a structured yet flexible approach to data collection and analysis (McLeod, 2023). Table 1 Likert Scale of the Level of Accuracy of the Smart Vending Machine with Advanced Face Skin Analysis Assigned Range Verbal Interpretation 47  Point 5 4.21 - 5.00 Strongly Agree 4 3.41 – 4.20 Agree 3 2.61 – 3.40 Neither/Nor Agree 2 1.81 – 2.60 Disagree 1 1.0 - 1.80 Strongly Disagree Table 1 outlines the 5-point Likert scale used to measure the accuracy of the Smart Vending Machine with Advance Face Skin Analysis. Assigned point 1 has a range value of 1.00 – 1.80 and corresponds to a Strongly Disagree verbal interpretation. Point 2, with a range of 1.81 – 2.60, is interpreted as Disagree. Point 3, ranging from 2.61 – 3.40, represents a Neither/Nor Agree interpretation. Point 4, with values between 3.41 – 4.20, corresponds to Agree. Finally, point 5, ranging from 4.21 – 5.00, is interpreted as Strongly Agree. This table serves as the basis for classifying 48 the system's accuracy in terms of its ability to analyze skin and recommend suitable products effectively. Table 2 Likert Scale of the level of Functionality and Effectiveness Assigned Numerical Categorical Point Range Response 4 3.51 – 4.00 Strongly Agree 3 2.51 – 3.51 Agree 2 1.51 – 2.50 Disagree 1 1.0 – 1.50 Strongly Disagree Note. ISO/IEC 25010:2012 Product Quality Evaluation by codacy.com. Copyright 2019 by Codacy Automated Code Review. 49  Table 2 presents the Likert scale used to evaluate the level of effectiveness and functionality of the Advanced Face Skin Analysis feature within the Smart Vending Machine. Respondents can choose from options such as Strongly Disagree, Disagree, Agree, or Strongly Agree, which correspond to varying levels of perceived system effectiveness and functionality. This scale serves as a basis for assessing the system’s capability in delivering accurate and reliable skin type analysis and tailored product recommendations. Data Gathering Procedure Data will be collected through direct observation of participants, specifically those utilizing the Smart Sunscreen Vending Machine with Advance Face Skin Analysis. The researchers will seek permission from the participants to conduct a study on the system’s effectiveness, efficiency, and functionality. The primary data will be gathered through survey questionnaires, a widely used quantitative data collection method. These surveys will be administered both in person and remotely to ensure comprehensive participation. Following data collection, the researchers will tabulate, compute, analyze, and interpret the data to assess the performance of the Smart Sunscreen Vending Machine and its AI-powered analysis. The system will be evaluated 50 based on its accuracy in categorizing skin types and recommending sunscreen products. To select participants, a convenience sampling technique will be used, where individuals who are easily accessible and willing to participate in the study will be chosen. This method ensures efficient participant recruitment and data collection in line with the study’s objectives. Statistical Treatment of Data The following statistical data treatments were utilized by the researchers to assess the data and information collected for the development of this study on the Advanced Face Skin Analysis System integrated into the Smart Sunscreen Vending Machine: 1. Weighted Mean – The weighted mean was calculated to evaluate the effectiveness of the AI-powered face skin analysis system. This method was used to determine how accurately the system could classify users' skin types (normal, combination, or sensitive) based on facial features and responses. Additionally, the weighted mean was employed to assess the system’s overall functionality, accuracy in identifying skin conditions, and the performance of the sunscreen recommendations. This assessment was in line with the ISO/IEC 25010 quality model, which evaluates the software’s functionality, usability, and reliability. 51  2. Composite Mean or Average – The composite mean was derived from the weighted mean values to evaluate the overall performance of the advanced face skin analysis system. This included measuring its efficiency in categorizing skin types, detecting specific skin conditions (e.g., dryness, oiliness), and recommending sunscreen based on users' needs. The composite mean provided an aggregate view of the system's overall reliability and consistency, contributing to a holistic evaluation of the technology’s effectiveness. 3. Percentage – The percentage method was used to quantify the accuracy of the face skin analysis system, based on feedback from participants. This calculation assessed how accurately the AI model identified various skin types and conditions, as well as the precision of sunscreen recommendations based on users' individual needs. The percentage also reflected user satisfaction with the system’s recommendations and skin analysis, providing valuable insights into the machine’s effectiveness in real-world use. These statistical methods ensured that the development of the Advanced Face Skin Analysis System was thoroughly evaluated in terms of its functionality, accuracy, and efficiency, contributing to the success of the Smart Sunscreen Vending Machine as a whole. 52 System Planning and Requirement Analysis System Hardware and Software Requirements Table 3. System Hardware and Software Requirements System Characteristic System Requirement Properties Description The researcher Operating Raspberry Pi will use System: 4 Model B Raspbian OS (or a similar Raspberry Pi OS) to manage all device operations, including peripheral connections and software execution. Python and C++ Programming Python 3.13, will be used to Language: C++ 53  program device functions, control sensor input, manage data processing, and communicate with output devices on the Raspberry Pi. Visual Studio IDEs: Visual Studio Code will be Code 1.76, used for Python Arduino IDE and C++ 2.0 development on the Raspberry Pi, while Arduino IDE will be used for programming any 54  microcontrollers involved. These sensors Input Raspberry Pi and modules Data/Device(s): Camera will capture Module v2, environmental Multi-Coin data (e.g., UV Acceptor, Bill light, distance, Acceptor temperature) Module, and user inputs VEML6075 (e.g., coin and UV Light bill acceptors, Sensor, buttons). Tactile Push Button, HX711 Load Cell with Load Cell Sensor, HC-SR04 Ultrasonic Distance 55  Sensor, DHT22 Temperature and Humidity Sensor. The Output Raspberry Pi touchscreen Data/Device(s): 7" display will Touchscreen present data Display, Mini and system Amplified feedback to Speaker users, while the Module speaker will provide audio notifications or alerts. Table 3, Continued System Characteristic System Requirement Properties Description 56  The researcher Sensor(s) / Raspberry Pi will use these Camera(s): Camera Module sensors to v2, VEML6075 gather UV Light Sensor, environmental Tactile Push data (UV Button, HX711 levels, Load Cell with temperature, Load Cell humidity) and Sensor, user inputs HC-SR04 (button Ultrasonic presses, weight Distance Sensor, measurements, DHT22 distance) for Temperature and analysis. Humidity Sensor. The researcher No. of 1 will use one Cameras Raspberry Pi Used: Camera Module v2 for 57  image capture or video recording to support visual monitoring or data collection. The Raspberry Single-Board Raspberry Pi 4 Pi will handle Computer: Model B, Arduino main system Uno R3 processing, while the Arduino Uno will interface with sensors that require real-time or precise control, aiding data collection and processing. The 1 researcher will 58 No. of RPi use one Used: Raspberry Pi 4 Model B as the primary computing unit for data handling, sensor management, and running programmed functions. One No. of 1 Arduino Uno Arduino R3 will support Used: the Raspberry Pi by handling specific sensors or modules, providing more accurate 59  control where needed. The researcher Power 24V AC-DC will use a 24V Source: power supply AC-DC power with Buck supply, Converters regulated with Buck converters, to ensure stable power for all system components, enabling reliable operation during data collection. System Hardware Specifications Table 4, Continued 60  System Characteristic System Requirement Properties Description GPIO: Raspberry Pi Accessory that 4 GPIO provides Expansion additional Board General Purpose Input/Output (GPIO) pins, enabling expanded connectivity and control for various electronic projects. Video & HDMI Cable, Provides Sound USB-Powered seamless audio- Mini Speaker visual connectivity, 61  delivering high- quality sound through a compact Multimedia Mini Compact audio Amplified device that Speaker enhances sound Module output, typically used in DIY electronics projects, offering amplification for small speakers while providing easy integration with microcontrollers SD Card Samsung Provides fast, Support EVO Select reliable storage for cameras. 62  32GB MicroSD Card Power DC Power The developed Source: Supply Input: system is 100~240Vac - powered using a 50/60Hz DC power Output: 5Vdc supply that is - 0.6A-3A sufficient to power up or starts the system Table 5 Raspberry Pi Camera Module v2 Technical Specification Characteristic System Properties Description Resolution: 8 MP The researcher