[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126742-en":3,"doc-seo-126742-105":29,"detail-sidebar-cat-0-en-105":89},{"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":11},126742,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","MACHINE LEARNING MODEL TO PREDICT THE RISK OF STUDENTS NOT GRADUATING ON TIME","This study focuses on developing a machine learning program to predict the risk of college students not graduating within the standard four-year period. The Random Forest algorithm is employed to build this predictive model. A dummy dataset comprising US student academic records from the first to fourth semesters was used for training and testing. The model's performance was further analyzed by varying the number of generated trees. The evaluation metrics employed were accuracy, precision, misclassification, and recall rates, derived from a Confusion Matrix. The study found that the Random Forest model with 91 generated trees achieved the highest accuracy of 67% in predicting the risk of students graduating late.","# PROJECT REPORT\n\nMACHINE LEARNING MODEL TO PREDICTTHE RISK OF STUDENTS NOT GRADUATING ONTIME  \nDIONYSIUS ABIRAMA PUTRA  \n16.K1.0052  \nFaculty of Computer ScienceSoegijapranata Catholic University2023  \n# PROJECT REPORT\n\n## MACHINE LEARNING MODEL TO PREDICTTHE RISK OF STUDENTS NOT GRADUATING ON\n\nTIME  \nDIONYSIUS ABIRAMA PUTRA  \n16.K1.0052  \nFaculty of Computer ScienceSoegijapranata Catholic University  \n## ABSTRACT\n\nThe purpose of this study is creating a program to predict the risk of college student notgraduating on time, which is graduating after more than four years of study. The approach tosolve this problem is by developing machine learning with Random Forest algorithm. The modelis trained and tested using dummy dataset of US student academic records within the firstsemester to fourth semester. The model is also trained with different number of generated tree.To analyze the result, Confusion Matrix is used to compare the accuracy, precision,misclassification, and recall rates of each trained model. The final result shown that RandomForest model with 91 generated tree has the highest accuracy of 67%, to predict the risk ofstudent not graduating on time.  \nKeyword: classification, machine learning, Confusion Matrix, Random Forest","cbCaiiq2R8yTXyLK","https://ap.wps.com/l/cbCaiiq2R8yTXyLK","pdf",341795,1,3,"English","en",105,"# PROJECT REPORT\n## MACHINE LEARNING MODEL TO PREDICT THE RISK OF STUDENTS NOT GRADUATING ON TIME","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"The main objective is to create a machine learning program that can predict the risk of college students not graduating on time, meaning graduating after more than four years.\"},{\"question\":\"Which machine learning algorithm was used in this study?\",\"answer\":\"The study used the Random Forest algorithm to develop the predictive model.\"},{\"question\":\"How was the performance of the machine learning model evaluated?\",\"answer\":\"The performance was evaluated using a Confusion Matrix to compare accuracy, precision, misclassification, and recall rates for each trained model.\"}]","MACHINE LEARNING MODEL TO PREDICT THE RISK OF STUDENTS NOT GRADUATING ON TIME | 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