[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122286-id":3,"doc-seo-122286-113":31,"detail-sidebar-cat-0-id-113":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},122286,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",54,"Penelitian & Laporan","Strategi Manajemen Pendidikan Berbasis Machine Learning untuk Prediksi Prestasi Siswa","Prediksi prestasi akademik siswa menjadi kebutuhan strategis dalam manajemen pendidikan modern. Penelitian ini menilai efektivitas model Machine Learning, yaitu Support Vector Machine (SVM) dan Random Forest, dalam memprediksi capaian peserta didik SMA Negeri menggunakan dataset sintetis yang menyerupai data sekolah riil. Dataset dibangun dari nilai semester, tingkat kehadiran, serta latar belakang sosial ekonomi. Pengujian dilakukan dengan validasi silang lima lipat dan evaluasi metrik akurasi, presisi, recall, serta F1-score. Hasil menunjukkan Random Forest lebih stabil dan unggul akurasinya, terutama pada data non-linier multidimensi, sehingga mendukung keputusan berbasis data dan intervensi dini.","21  \nBEduManageRs Journal  \nBorneo Educational Management and Research Journal, Vol.6, No.1, 2025 ISSN: 2747-0504  \nStrategi Manajemen Pendidikan Berbasis Machine Learning untuk  \nPrediksi Prestasi Siswa  \nHeny Pratiwi1, Muhammad Ibnu Sa’ad2, Salmon3  \n1,2,3 STMIK Widya Cipta Dharma, Samarinda, Indonesia  \n[1](1 henypratiwi@wicida.ac.id)[ henypratiwi@wicida.ac.id](1 henypratiwi@wicida.ac.id), [2](2 saad@wicida.ac.id)[ saad@wicida.ac.id](2 saad@wicida.ac.id), [3](3 salmon@wicida.ac.id)[ salmon@wicida.ac.id](3 salmon@wicida.ac.id)  \nAbstrak. Prediksi prestasi akademik siswa berbasis data menjadi keperluan strategis dalam manajemen pendidikan modern. Studi ini mengkaji efektivitas dua model Machine Learning—Support Vector Machine (SVM) dan Random Forest—dalam memprediksi capaian akademikpeserta didik SMA Negeri menggunakan data sintetis yang menyerupai data riil sekolah. Dataset dikembangkan dari tiga variabel utama: nilai semester, tingkat kehadiran, dan latar belakang sosial ekonomi. Model diuji menggunakan validasi silang lima lipat dan dievaluasi melalui metrikakurasi, presisi, recall, serta F1-score. Hasil menunjukkan bahwa Random Forest lebih stabil dan unggul secara akurasi dibandingkan SVM dalam konteks data multidimensi non-linier. Studi ini menunjukkan potensi integrasi sistem prediktif ke dalam praktik manajerial sekolah untuk mendukung pengambilan keputusan berbasis data yang lebih akurat dan preventif terhadapkegagalan akademik.  \nKata Kunci: manajemen pendidikan, prediksi akademik, machine learning, SVM, Random Forest  \nAbstract. Academic performance prediction based on student data is a strategic necessity in modern educational management. This study evaluates the effectiveness of two Machine Learning models—Support Vector Machine (SVM) and Random Forest—in predicting the academic achievement of public high school students using a synthetic dataset resembling real school records. The dataset was constructed from three primary variables: semester grades, attendance rate, and socioeconomic background. Both models were tested using five-fold cross-validation and assessed via accuracy, precision, recall, and F1-score metrics. The results indicate that Random Forest offers more stable and accurate performance than SVM, especially when handling nonlinear and multidimensional data. This study highlights the potential integration of predictive systems into school management practices to support data-driven decision-making and early academic intervention.  \nKeywords: educational management, academic prediction, machine learning, SVM, Random Forest  \nJurnal BeduManagers, Vol.6, No.1, 30 Juni 2025  \n22  \nBEduManageRs Journal  \nBorneo Educational Management and Research Journal, Vol.6, No.1, 2025 ISSN: 2747-0504  \nPENDAHULUAN  \nSekolah Menengah Atas Negeri saat ini  \nmenghadapi tantangan dalam  \nmemanfaatkan data internal secara optimal untuk mendeteksi risiko akademik pesertadidik. Meskipun institusi telah menerapkan sistem informasi manajemen pendidikan, proses identifikasi siswa dengan potensikegagalan belajar masih dilakukan secara manual. Hal ini berdampak padalambatnya intervensi dan kurang tepatnya strategi pembelajaran individual. Permasalahan ini menunjukkan adanya kesenjangan antara pengumpulan data dan pemanfaatannya dalam pengambilan keputusan manajerial.  \nSistem informasi yang ada seringkali hanya berfungsi sebagai media penyimpanan data tanpa diikuti oleh analisis yang mendalam dan pemanfaatanyang efektif. Akibatnya, keputusan strategis seperti pengalokasian sumber daya, penentuan intervensi pembelajaran, maupun penyusunan program bimbingan masih dilakukan berdasarkan intuisi dan pengalaman subjektif, bukan data yang valid dan terintegrasi. Hal ini menyebabkan intervensi yang terlambat, tidak terfokus, dan kurang berdampak dalam meningkatkan prestasi akademik siswa. Di sisi lain, data pendidikan yang dikumpulkan memiliki sifat multidimensi dan kompleks, melibatkan faktor nilai akademik, kehadiran, serta latar be","cbCairEGRw69d8Mz","https://ap.wps.com/l/cbCairEGRw69d8Mz","pdf",417306,5,1,10,"Indonesian","id",113,"# Pendahuluan\n## Tantangan pemanfaatan data internal di sekolah\n## Kesenjangan antara pengumpulan data dan pengambilan keputusan\n## Relevansi Machine Learning untuk prediksi risiko akademik\n## Perbandingan model SVM dan Random Forest\n## Keterbatasan penelitian dan kebutuhan kajian lanjutan","[{\"question\":\"Penelitian ini memprediksi prestasi akademik siswa menggunakan variabel apa saja?\",\"answer\":\"Dataset dibangun dari tiga variabel utama: nilai semester, tingkat kehadiran, dan latar belakang sosial ekonomi.\"},{\"question\":\"Bagaimana evaluasi model dilakukan pada penelitian ini?\",\"answer\":\"Model diuji menggunakan validasi silang lima lipat dan dievaluasi melalui metrik akurasi, presisi, recall, serta F1-score.\"},{\"question\":\"Model mana yang menunjukkan kinerja lebih stabil dan akurat serta mengapa?\",\"answer\":\"Random Forest menunjukkan kinerja lebih stabil dan unggul akurasi dibandingkan SVM, terutama ketika menangani data non-linier multidimensi.\"}]","Strategi Manajemen Pendidikan Berbasis Machine Learning untuk Prediksi Prestasi Siswa | 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ini memprediksi prestasi akademik siswa menggunakan variabel apa saja?","Question",{"text":77,"@type":78},"Dataset dibangun dari tiga variabel utama: nilai semester, tingkat kehadiran, dan latar belakang sosial ekonomi.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Bagaimana evaluasi model dilakukan pada penelitian ini?",{"text":82,"@type":78},"Model diuji menggunakan validasi silang lima lipat dan dievaluasi melalui metrik akurasi, presisi, recall, serta F1-score.",{"name":84,"@type":75,"acceptedAnswer":85},"Model mana yang menunjukkan kinerja lebih stabil dan akurat serta mengapa?",{"text":86,"@type":78},"Random Forest menunjukkan kinerja lebih stabil dan unggul akurasi dibandingkan SVM, terutama ketika menangani data non-linier 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