[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120001-en":3,"doc-seo-120001-105":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":20,"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},120001,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Prediction of Student Performance at Polytechnic Using Machine Learning Approach - Regression Model Comparison","Educational data mining (EDM) is used to forecast student-related outcomes by extracting patterns from educational records. This work builds regression models to predict first-semester student performance and the waiting period for graduate employment using informatics management (MI) and noninformatics management (non-MI) student data. Four regression approaches are compared: SVR, RFR, AdaBoost, and XGBoost. Results report the best first-semester R2 from SVR for MI and RFR for non-MI, while graduate waiting-period prediction shows strongest performance from AdaBoost for MI and SVR for non-MI.","Prediction of student performance at polytechnic using machine  \nlearning approach  \nKristina Hutajulu, Lili Ayu Wulandhari  \nComputer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia  \nArticle Info ABSTRACT  \nArticle history:  \nReceived Dec 28, 2023 Revised Apr 9, 2024 Accepted Jun 9, 2024  \nKeywords:  \nEducational data mining Machine learning Regression model Student performance Waiting period for graduate employment  \nCorresponding Author:  \nEducational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. Oneof the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and noninformatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students'performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI.  \nThis is an open access article under the CC BY-SA license.  \nKristina Hutajulu  \nComputer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University  \nJakarta, 11480, Indonesia  \n[Email: kristina.hutajulu@binus.ac.id](Email: kristina.hutajulu@binus.ac.id)  \n1. INTRODUCTION  \nThe quality of an educational institution is measured in part by the performance of its students. Polytechnics are obliged to prepare and produce competent graduates so they can compete in the world of work. One indicator of the quality of graduates who can compete in the world of work can be represented by the waiting period for graduates to get their first job. To produce skilled graduates, it is the responsibility of polytechnics to ensure that students maintain the expected level of performance from the first semester until the completion of their studies. The first semester is a significant adaptation period for students. The grade point in this semester can impact students' self-perception of their learning abilities and provide an early indication of potential academic success in the future. The grade point of students in their first semester can act as an indicator and early warning for polytechnics to provide additional support to graduates who exhibit poor performance. Essentially, student performance, as evidenced by their academic achievements, serves asthe initial foundation for students to compete in the professional world. Success in the first semester can be influenced by the academic and social support received by students. The Polytechnic can do a selective admission process as an effort to attract potential students, considering factors such as their social background, educational history, and admission result.  \nAchieving positive results in the early semesters has a beneficial impact on students, motivating students to take a proactive approach to their studies. In addition to academic performance, students'involvement in non-academic activities in college, such as proficiency in foreign languages, character education, organizational experience, internship experience, and other activities related to soft skills, can significantly influence their ability to face the world of work. Polytechnics can enhance their understanding of their gra","cbCaiaXAaWxHLK7p","https://ap.wps.com/l/cbCaiaXAaWxHLK7p","pdf",635653,1,10,"English","en",105,"# Introduction\n## Background and need for prediction\n## Educational data mining and machine learning\n# Methodology\n## Data sources (MI and non-MI)\n## Compared regression models\n# Results\n## First-semester performance prediction\n## Graduate employment waiting-period prediction\n# Conclusion","[{\"question\":\"What outcomes does the regression framework predict in this study?\",\"answer\":\"It predicts first-semester student performance and the waiting period for graduate employment using machine learning regression models.\"},{\"question\":\"Which regression models are compared for prediction?\",\"answer\":\"The study compares support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression.\"},{\"question\":\"How do the best-performing models differ between MI and non-MI data?\",\"answer\":\"For first-semester performance, SVR is highest for MI (R2=0.58) while RFR is highest for non-MI (R2=0.34). For waiting period prediction, AdaBoost is highest for MI (R2=0.44) and SVR is highest for non-MI (R2=0.39).\"}]","Prediction of Student Performance at Polytechnic Using Machine Learning Approach - Regression Model Comparison | PDF",1785727623,25,{"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},"prediction-of-student-performance-at-polytechnic-using-machine-learning-approach-regression-model-comparison","",{"@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/prediction-of-student-performance-at-polytechnic-using-machine-learning-approach-regression-model-comparison/120001/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What outcomes does the regression framework predict in this study?","Question",{"text":75,"@type":76},"It predicts first-semester student performance and the waiting period for graduate employment using machine learning regression models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which regression models are compared for prediction?",{"text":80,"@type":76},"The study compares support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the best-performing models differ between MI and non-MI data?",{"text":84,"@type":76},"For first-semester performance, SVR is highest for MI (R2=0.58) while RFR is highest for non-MI (R2=0.34). 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