[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120549-en":3,"doc-seo-120549-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120549,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Prediction of Student Major Selection at High School Using a Machine Learning Approach","A machine learning prediction system is developed and evaluated to match Senior High School (SMA) Nusa Putra Kota Tangerang students with suitable potential majors based on academic interests and performance levels. Random Forest, SVM, logistic regression, K-NN, and Naive Bayes are trained using academic records, interest tests, and questionnaires. Random Forest produces the best results with 85% accuracy, 82% precision, 88% recall, and AUC 0.92. Influential factors include Grade XII Mathematics scores and Science Interest Test results, with implications for accuracy and fairer educational choices.","International Journal of Engineering and Computer Science Applications (IJECSA)  \nVol. 4, No. 1, March 2025, pp. 51∼58 ISSN: 2828-5611  \nDOI: [doi.org/10.30812/ijecsa.v4i1.4983](doi.org/10.30812/ijecsa.v4i1.4983) 51  \n\n| Prediction of Student Major Selection at High School Using a Machine\u003Cbr>Learning Approach\u003Cbr>Nasril Sany 1 , Dody 1 , Esa Firmansyah Muchlis 1 , Muhaimin Hasanudin2 , Budi Berlinton3\u003Cbr>1Institut Teknologi PLN, Jakarta, Indonesia\u003Cbr>2Universitas Mercu Buana, Jakarta, Indonesia\u003Cbr>3Universitas Multimedia Nusantara, Tangerang, Indonesia |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received March 13, 2025 Revised March 16, 2025 Accepted March 25, 2025\u003Cbr>Keywords:\u003Cbr>Machine Learning Major High School Prediction of Student | ABSTRACT\u003Cbr>The primary objective of this research was to develop and evaluate a machine learning prediction system that matches Senior High School (SMA) Nusa Putra Kota Tangerang students with their potential school majors based on their academic interests and performance levels. This research method employs machine learning algorithms, including Random Forest, Support Vector Machine (SVM), logistic regression, K-Nearest Neighbor (K-NN), and Nave Bayes, using academic records, interest tests, and questionnaires for data collection. The data was processed and analyzed to train and test the algorithm. The findings of this study indicate that the Random Forest algorithm achieved the best performance among the models, with an accuracy of 85%, a precision of 82%, a recall of 88%, and an AUC score of 0.92 . The factors that affected the prediction of major selection were Grade XII Mathematics scores and Science Interest Test results. The research implications suggest that Random Forest technology within Machine Learning (ML) enhances major selection accuracy while promoting fairness, providing superior educational choices, and increasing student satisfaction. Future studies should investigate additional factors that influence this phenomenon.\u003Cbr>Copyright ©2025 The Authors.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| Corresponding Author:\u003Cbr>Muhaimin Hasanudin,\u003Cbr>Informatics Engineering, Universitas Mercu Buana, Indonesia,\u003Cbr>[Email: muhaimin.hasanudin@mercubuana.ac.id](Email: muhaimin.hasanudin@mercubuana.ac.id) |  |\n| How to Cite: N. Sany, D. Dody, E. F. Muchlis, M. Hasanudin, and B. Berlinton,“Prediction of Student Major Selection at High School Using a Machine Learning Approach,” International Journal of Engineering and Computer Science Applications (IJECSA) , vol. 4, no. 1, pp. 51-58, Mar. 2025. doi: 10.30812/ijecsa.v4i1.4983 . |  |\n\nJournal homepage: [https://journal.universitasbumigora.ac.id/index.php/ijecsa](https://journal.universitasbumigora.ac.id/index.php/ijecsa)  \n1. INTRODUCTION  \nSenior High School (SMA) Nusa Putra in Tangerang City conducts a rigorous selection process for its high school students, which shapes their future academic and professional paths. Teachers, along with school counselors, base their major selection decisions traditionally on multiple objective measures consisting of academic grades, student interests, and field study potential [1] . The current approach to major selection produces improper placements, so students face academic problems while losing their motivation and might even choose to leave school [2] . The absence of standard and reliable major selection criteria necessitates the immediate implementation of scientific methods to enhance educational quality and improve student satisfaction. Scientific research teams have developed different approaches to tackle major selection obstacles [1] . Developed career counseling through trait and factor theory for high school major selection, yet their model depends significantly on personal opinions as a rating method [3–5] . The research conducted by [5] examined students’ perception of objective structured practical examinations (OSPE) in anatomy while establishing the s","cbCaihZdd1PoTvcK","https://ap.wps.com/l/cbCaihZdd1PoTvcK","pdf",338689,1,"English","en",105,"# Introduction\n## Research motivation and problem of current major selection\n## Need for scientific and data-driven methods\n## Related work in machine learning for educational outcomes","[{\"question\":\"What is the main goal of the research?\",\"answer\":\"To develop and evaluate a machine learning system that predicts which high school major best fits a student’s interests and academic performance.\"},{\"question\":\"Which machine learning algorithms were compared in the study?\",\"answer\":\"Random Forest, Support Vector Machine (SVM), logistic regression, K-Nearest Neighbor (K-NN), and Naive Bayes.\"},{\"question\":\"What model performed best and what metrics were achieved?\",\"answer\":\"Random Forest performed best, reaching 85% accuracy, 82% precision, 88% recall, and an AUC score of 0.92.\"}]","Prediction of Student Major Selection at High School Using a Machine Learning Approach | PDF",1785730604,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"prediction-of-student-major-selection-at-high-school-using-a-machine-learning-approach","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/prediction-of-student-major-selection-at-high-school-using-a-machine-learning-approach/120549/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the research?","Question",{"text":74,"@type":75},"To develop and evaluate a machine learning system that predicts which high school major best fits a student’s interests and academic performance.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms were compared in the study?",{"text":79,"@type":75},"Random Forest, Support Vector Machine (SVM), logistic regression, K-Nearest Neighbor (K-NN), and Naive Bayes.",{"name":81,"@type":72,"acceptedAnswer":82},"What model performed best and what metrics were achieved?",{"text":83,"@type":75},"Random Forest performed best, reaching 85% accuracy, 82% precision, 88% recall, and an AUC score of 0.92.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]