[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119125-id":3,"doc-seo-119125-113":31,"detail-sidebar-cat-0-id-113":92},{"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},119125,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",54,"Penelitian & Laporan","Prediksi Performa Akademik Mahasiswa untuk Kelulusan Predikat Cum Laude dengan Pendekatan Machine Learning","Penelitian ini bertujuan mengembangkan model prediktif berbasis machine learning untuk memperkirakan jumlah mahasiswa yang lulus dengan predikat cum laude di perguruan tinggi. Pendekatan klasifikasi diterapkan agar prediksi dapat mendukung pengambilan keputusan pemangku kebijakan. Hasil perbandingan Naïve Bayes, Random Forest, dan C4.5 menunjukkan performa terbaik pada Naïve Bayes dengan akurasi 87,60%, precision 86,70%, recall 92,10%, dan F1-score 89,30%, sekaligus waktu komputasi terendah. Temuan ini membantu institusi mengantisipasi penurunan standar kelulusan dan meningkatkan kualitas lulusan secara keseluruhan.","Volume 11 Nomor 1 halaman 39 – 49 e ISSN: 2654-9735, p ISSN: 2089-6026  \nTersedia secara online di:  \n[http://journal.ipb.ac.id/index.php/jika](http://journal.ipb.ac.id/index.php/jika)  \nPrediksi Performa Akademik Mahasiswa untuk Kelulusan Predikat Cum Laude dengan Pendekatan Machine Learning  \nPredicting Academic Performance of Students for Graduating with Cum Laude Honors using Machine Learning Approach  \nFIRGIAWAN INDRA KUSUMA BUDIYANTO 1, IRMAN HERMADI 1, MEDRIA KUSUMA DEWI HARDHIENATA 1*  \nAbstrak  \nPenelitian ini bertujuan untuk mengembangkan model yang dapat memprediksi jumlah mahasiswa yang lulus dengan predikat cum laude pada perguruan tinggi. Penelitian ini menggunakan algoritma machine learning untuk klasifikasi sehingga dapat dilakukan prediksi. Hasil dari penelitian ini menunjukkan efektivitas model dalam memprediksi kelulusan cum laude agar dapat memberikan kesempatan bagi universitas untuk meningkatkan kualitas lulusan secara keseluruhan dan mengatasi penurunan standar kelulusan yang mungkin terjadi. Prediksi jumlah mahasiswa cum laude dilakukan pada penelitian ini, untuk membantu proses pengambilan keputusan oleh pemangku kebijakan pada perguruan tinggi. Dengan memanfaatkan teknik machine learning, institusi dapat mengantisipasi dan mendukung mahasiswa dalam mencapai predikat cum laude, sehingga diharapkan dapat meningkatkan kualitas lulusan secara keseluruhan. Dalam penelitian ini, dibandingkan tiga algoritma machine learning yakni algoritma Naïve Bayes, Random Forest, dan C4.5 untuk melakukan prediksi kelulusan mahasiswadengan predikat cum laude. Hasil penelitian menunjukkan bahwa dalam kasus ini kinerja terbaik dicapai olehalgoritma Naïve Bayes dengan nilai akurasi 87.60%, precision 86.70%, recall 92. 10% dan F1-score 89.30% . Selain itu, algoritma Naïve Bayes juga menghasilkan nilai waktu komputasi terendah pada kasus yang diujikandibandingkan dengan algoritma lainnya.  \nKata Kunci: cum laude, kelulusan, machine learning, model prediktif.  \nAbstract  \nThis research aims to develop a predictive model using machine learning techniques to forecast cum laude graduations within a university. Machine learning algorithms are utilized for classification to enable such predictions. The research results demonstrate the effectiveness of the model in predicting cum laude graduation, thereby providing opportunities for the university to enhance the overall quality ofgraduates and address potential declines in graduation standards. Predictions regarding the number of cum laude students are made in this study to assist decision-making processes among university stakeholders. By leveraging machine learning techniques, institutions can anticipate and support students in achieving cum laude honours, ultimately leading to an improvement in the overall quality of graduates. In this study, three machine learning algorithms—Naïve Bayes, random forest, and C4.5—are compared for predicting student graduation with cum laude honours. The results of the study show that, for the considered case, the best performance was achieved by theNaïve Bayes algorithm with 87.60% accuracy, 86. 70% precision, 92.10% recall, and 89.30% F1-score. In addition, theNaïve Bayes algorithm also obtained the lowest computational time compared to other algorithms.  \nKeywords: cum laude, graduation, machine learning, predictive model.  \nPENDAHULUAN  \nMelalui pendidikan pada perguruan tinggi, mahasiswa dapat memperoleh kualifikasi akademik yang dibutuhkan, yang dapat membantu menghadapipersaingan kerja di masa depan. Predikat cum laude adalah pencapaian akademik yang menunjukan bahwa mahasiswa tersebut  \n1 Departemen Ilmu Komputer, Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor, Bogor 16680 * Penulis Korespondensi: Tel/Faks: +62 858-9280-8513; [Surel: medria.hardhienata@apps.ipb.ac.id](Surel: medria.hardhienata@apps.ipb.ac.id)  \n40 Budiyanto , Hermadi, dan Hardhienata JIKA  \ntelah mencapai prestasi akademik yang tinggi. Meski demikian","cbCaipWYNhQKaOgf","https://ap.wps.com/l/cbCaipWYNhQKaOgf","pdf",521774,3,1,11,"Indonesian","id",113,"# Pendahuluan\n## Predikat cum laude dan latar akademik\n## Tujuan penelitian dan manfaat prediksi\n# Metode dan perbandingan algoritma\n## Naïve Bayes, Random Forest, dan C4.5\n# Hasil dan evaluasi kinerja\n## Akurasi, precision, recall, F1-score, dan waktu komputasi","[{\"question\":\"Penelitian ini bertujuan memprediksi apa?\",\"answer\":\"Penelitian ini bertujuan mengembangkan model untuk memprediksi jumlah mahasiswa yang lulus dengan predikat cum laude pada perguruan tinggi.\"},{\"question\":\"Algoritma machine learning apa saja yang dibandingkan dalam penelitian ini?\",\"answer\":\"Penelitian ini membandingkan tiga algoritma, yaitu Naïve Bayes, Random Forest, dan C4.5 untuk prediksi kelulusan dengan predikat cum laude.\"},{\"question\":\"Mengapa algoritma Naïve Bayes dipilih sebagai kinerja terbaik?\",\"answer\":\"Naïve Bayes menghasilkan kinerja terbaik pada kasus yang diuji, dengan akurasi 87,60%, precision 86,70%, recall 92,10%, F1-score 89,30%, serta waktu komputasi terendah dibanding algoritma lainnya.\"}]","Prediksi Performa Akademik Mahasiswa untuk Kelulusan Predikat Cum Laude dengan Pendekatan Machine Learning | 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ini bertujuan memprediksi apa?","Question",{"text":76,"@type":77},"Penelitian ini bertujuan mengembangkan model untuk memprediksi jumlah mahasiswa yang lulus dengan predikat cum laude pada perguruan tinggi.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Algoritma machine learning apa saja yang dibandingkan dalam penelitian ini?",{"text":81,"@type":77},"Penelitian ini membandingkan tiga algoritma, yaitu Naïve Bayes, Random Forest, dan C4.5 untuk prediksi kelulusan dengan predikat cum laude.",{"name":83,"@type":74,"acceptedAnswer":84},"Mengapa algoritma Naïve Bayes dipilih sebagai kinerja terbaik?",{"text":85,"@type":77},"Naïve Bayes menghasilkan kinerja terbaik pada kasus yang diuji, dengan akurasi 87,60%, precision 86,70%, recall 92,10%, F1-score 89,30%, serta waktu komputasi terendah dibanding algoritma 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