[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123166-en":3,"doc-seo-123166-105":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":4,"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},123166,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","ANALYSIS STUDENT EMOTIONS AND MENTAL HEALTH ON CUMULATIVE GPA USING MACHINE LEARNING AND SMOTE","This research investigates the impact of emotions and mental health on students' cumulative grade point average (CGPA) using machine learning classification while addressing data imbalance with the Synthetic Minority Oversampling Technique (SMOTE). Emotional well-being and mental health are treated as key determinants of academic achievement, and imbalance in mental health signals such as anxiety and depression can degrade predictive performance. Using 226 students’ academic records and self-reported evaluations, random forest reaches 87.63% accuracy versus logistic regression at 86.56%. Results support SMOTE’s effectiveness and highlight the value of psychological features for improved educational data mining and policy-focused mental health support.","ANALYSIS STUDENT EMOTIONS AND MENTAL HEALTH ON CUMULATIVE GPA USING MACHINE LEARNING AND SMOTE  \nFadhil Muhammad Basysyar1*; Gifthera Dwilestari2; Ade Irma Purnamasari3  \nInformation System1,2,3  \nSTMIK IKMI Cirebon, Cirebon, Indonesia 1,2,3  \n[ikmi.ac.id](ikmi.ac.id1)[1](ikmi.ac.id1),2,3  \n[fadhil.m.basysyar@gmail.com](fadhil.m.basysyar@gmail.com1)[1](fadhil.m.basysyar@gmail.com1)* , [ggdwilestari@gmail.com](ggdwilestari@gmail.com2)[2](ggdwilestari@gmail.com2), [irma2974@yahoo.com](irma2974@yahoo.com3)[3](irma2974@yahoo.com3)  \n(*) Corresponding Author  \n(Responsible for the Quality of Paper Content)  \nThe creation is distributed under the Creative Commons Attribution-NonCommercial 4.0 International License.  \nAbstract—This research investigates the impact of emotions and mental health on students' cumulative grade point average (CGPA) using machine learning classification algorithms while addressing data imbalances with the Synthetic Minority Oversampling Technique (SMOTE). Emotional well-being and mental health are acknowledged as vital determinants of academic achievement. Data imbalance, particularly in mental health metrics such as anxiety and depression, frequently compromises forecast accuracy. This study improves the accuracy of CGPA prediction based on emotional and mental health factors by utilizing SMOTE in machine learning models such as logistic regression and random forest. A dataset including 226 university students, including academic records and self-reported mental health evaluations, was evaluated. The random forest model attained an accuracy of 87.63%, exceeding the logistic regression model's accuracy of 86.56%. These findings emphasize the significant role of emotions and mental health in academic outcomes and validate SMOTE’s efficacy in addressing class imbalance. This work offers afresh technique in educational data mining by revealing the possibility for improved academic achievement forecasts based on psychological characteristics, helping to the development of targeted therapies for students experiencing emotional issues. Implications for educational policy emphasize the significance of mental health support systems in promoting academic achievement. Subsequent research should investigate supplementary psychological variables and comprehensible models to improve predictive accuracy and facilitate evidence-based policymaking.  \nKeywords: cumulative grade point average, emotions, machine learning, mental health, synthetic minority oversampling technique.  \nIntisari—Studi ini mempelajari pengaruh emosi dan kesehatan mental mahasiswa terhadap Indeks Prestasi Kumulatif (IPK) mereka menggunakan algoritma klasifikasi pembelajaran mesin, denganfokus khusus padapenyelesaian ketidakseimbangan data melalui Teknik Synthetic Minority Oversampling (SMOTE). Kesejahteraan emosional dan kesehatan mental semakin diakui sebagai prediktor utama kinerja akademis. Namun, kesulitan kumpulan data yang tidakseimbang, khususnya dalam mengumpulkan penanda kesehatan mental seperti kesedihan dan kecemasan, seringkali menghambatakurasiprediksi. Dengan mengintegrasikan SMOTE dengan beberapa model pembelajaran mesin, termasuk regresi logistik dan random forest, penelitian ini bertujuan untuk meningkatkan akurasi prediksi IPK berdasarkan data kesehatan emosional dan mental. Kumpulan data dari 226 mahasiswa, yang menggabungkan catatan akademis dan penilaian kesehatan mental yang dilaporkan sendiri, dievaluasi. Hasil penelitian mengungkapkan bahwa model random forest memperoleh akurasi 87,63%, mengalahkan regresi logistik pada 86,56%. Temuan ini menyiratkan bahwa emosi dan kesehatan mental memiliki dampak yang signifikan terhadap keberhasilan akademis, dan bahwa SMOTE merupakan teknik yang berguna untuk meminimalkan ketidakseimbangan kelas. Studi ini berkontribusi pada semakin banyaknya penelitian tentang penggalian data pendidikan dengan memberikanteknik baru untuk memprediksi keberhasilan akademis berdasarkan karakteristik psikolo","cbCaivGI5XOdwP3J","https://ap.wps.com/l/cbCaivGI5XOdwP3J","pdf",1301139,1,"English","en",105,"# Introduction\n## Academic success and psychological factors\n## Machine learning prediction and SMOTE for imbalanced data\n## Data imbalance as a core challenge for modeling","[{\"question\":\"What is the main research goal of the study?\",\"answer\":\"To predict students’ cumulative grade point average (CGPA) by using emotions and mental health variables, while improving prediction quality under data imbalance with SMOTE.\"},{\"question\":\"How does the study handle imbalanced data?\",\"answer\":\"It applies the Synthetic Minority Oversampling Technique (SMOTE) to reduce the negative effects of class imbalance, particularly in mental health metrics.\"},{\"question\":\"Which model performed better and what was the accuracy?\",\"answer\":\"The random forest model performed better, achieving 87.63% accuracy compared with 86.56% for logistic regression.\"}]","ANALYSIS STUDENT EMOTIONS AND MENTAL HEALTH ON CUMULATIVE GPA USING MACHINE LEARNING AND SMOTE | 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