[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120555-en":3,"doc-seo-120555-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120555,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","University Prescribed Student Uniform Classification - Machine Learning Modeling","This study evaluates the performance of three machine learning models—Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF)—for classifying student uniform compliance using image data at ISAT U Miagao. Uniform status is categorized into three classes: not in school uniform, female complete uniform, and male complete uniform. Model quality is measured with standard classification metrics including F1 score, accuracy, precision, and recall, with NN showing the strongest overall results.","University Prescribed Student Uniform Classification Machine Learning Modeling  \nJoemarie N. Gelano 1 and Ramil G. Lumauag2  \n1 Computer Studies Council, Iloilo Science and Technology University -Miagao Campus, Miagao, Iloilo, Philippines  \n2 Computer Studies Council, Iloilo Science and Technology University -Dumangas Campus, Dumangas, Iloilo, Philippines  \n[joemarie.gelano@isatu.edu.ph](joemarie.gelano@isatu.edu.ph) (Corresponding Email)  \nAbstract. This study evaluates the performance of three machine learning models, Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF), in classifying student uniform compliance at ISAT U Miagao using image data.  \nThe classification focused on three categories: Not in School Uniform, Female Complete Uniform, and Male Complete Uniform. Model performance was assessed using standard classification metrics. Among the models tested, the Neural Network consistently outperformed the others across all categories, demonstrating its effectiveness in accurately identifying uniform compliance through image data. The SVM also produced strong and reliable results, indicating its viability as an alternative model for this task. In contrast, the Random Forest model showed relatively weaker performance, particularly in recognizing students not in uniform, which may limit its effectiveness in highaccuracy monitoring environments. Overall, the findings highlight the superior capability of deep learning, particularly neural networks, in handling image-based classification tasks. The strong performance of SVM also supports the use of kernel-based approaches for institutional compliance systems. Meanwhile, the lower performance of Random Forest suggests potential limitations in more nuanced visual classification scenarios. These insights support the integration of advanced machine learning models in real-world applications that require reliable and automated compliance monitoring.  \nKeywords: Prescribed University Uniform, Supervised Machine Learning, Neural Network, Support Vector Machine, Random Forest, Orange Data Mining Tool.  \n1. Introduction  \nSchool uniforms are implemented in educational institutions not only to promote equality and discipline among students but also to establish a clear sense of identity and security within the school environment. However, enforcing uniform compliance, especially in institutions with large student populations, remains a persistent challenge. Manual monitoring at school entrances can be inefficient, prone to error, and resourceintensive. To address this issue, the application of machine learning techniques provides a modern, automated approach to assist in identifying and classifying whether students are wearing the prescribed school uniforms or not.  \nThis study aims to explore the effectiveness of machine learning models in detecting and classifying uniform violations among students entering the ISAT U Miagao Campus. By integrating various classification algorithms, the system can support school personnel in enhancing security protocolsand maintaining uniform compliance with greater accuracy and efficiency.  \n1.1 Objectives of the Study  \nThe primary objectives ofthis research are to:  \n⚫ Evaluate the performance of the machine learning model in terms ofF1 score, accuracy, precision, and recall;  \n⚫ Assess the model’s effectiveness in distinguishing between prescribed (male and female) and non-prescribed school uniforms; and  \n⚫ Compare the classification performance of different machine learning algorithms, including Neural Networks (NN), Support Vector Machine (SVM), and Random Forest (RF).  \n1.2 Conceptual Framework  \nFigure 1. Tourist Spot Recommender Systems  \nThis study develops a machine learning model to classify student uniform compliance at ISAT U Miagao Campus, following an Input-Process-Output framework , as shown in Figure 1. It uses labeled images of students, preprocessed for consistency, captured in various uncontrolled environments","cbCaijYpQWddGiwV","https://ap.wps.com/l/cbCaijYpQWddGiwV","pdf",819854,1,10,"English","en",105,"# Introduction\n## Objectives of the Study\n## Conceptual Framework\n# Literature Reviews\n# Methodology\n## Model Workflow\n### Image Import","[{\"question\":\"What machine learning models are compared for uniform compliance classification?\",\"answer\":\"The study compares Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) using image data.\"},{\"question\":\"How are student uniform classes defined in the classification task?\",\"answer\":\"The system classifies students into three categories: not in school uniform, female complete uniform, and male complete uniform.\"},{\"question\":\"Which model performs best overall and how is it assessed?\",\"answer\":\"Neural Network (NN) consistently outperforms the other models across categories. Performance is evaluated using standard classification metrics such as accuracy, precision, recall, and F1 score.\"}]","University Prescribed Student Uniform Classification - Machine Learning Modeling | PDF",1785730629,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"university-prescribed-student-uniform-classification-machine-learning-modeling","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/university-prescribed-student-uniform-classification-machine-learning-modeling/120555/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What machine learning models are compared for uniform compliance classification?","Question",{"text":75,"@type":76},"The study compares Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) using image data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are student uniform classes defined in the classification task?",{"text":80,"@type":76},"The system classifies students into three categories: not in school uniform, female complete uniform, and male complete uniform.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best overall and how is it assessed?",{"text":84,"@type":76},"Neural Network (NN) consistently outperforms the other models across categories. 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