[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-494932-113":53,"doc-detail-494932-id":126},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},113,"id","application-of-c45-and-k-nearest-neighbor-methods-for-student-graduation-classification-based-on-academic-data","Penerapan Metode C4.5 dan K-Nearest Neighbor untuk Klasifikasi Kelulusan Mahasiswa Berdasarkan Data Akademik","","Permasalahan ketepatan waktu kelulusan mahasiswa menjadi indikator penting untuk menilai kualitas dan efektivitas proses pendidikan tinggi. Penelitian ini menerapkan serta membandingkan algoritma klasifikasi C4.5 dan K-Nearest Neighbor (KNN) dalam memprediksi ketepatan waktu kelulusan berdasarkan atribut akademik seperti IPK, jumlah SKS, dan IPS. Metode Knowledge Discovery in Database (KDD) dilakukan melalui seleksi data, preprocessing, transformasi, data mining, dan evaluasi menggunakan RapidMiner pada 279 data mahasiswa Informatika angkatan 2015–2019. Hasil menunjukkan KNN lebih unggul: akurasi 76,08%, precision 73,11%, recall 41,92%, sedangkan C4.5 akurasi 73,49%, precision 64,62%, recall 41,89%.",{"@graph":63,"@context":118},[64,81,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/application-of-c45-and-k-nearest-neighbor-methods-for-student-graduation-classification-based-on-academic-data/494932/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":95,"encodingFormat":94,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/application-of-c45-and-k-nearest-neighbor-methods-for-student-graduation-classification-based-on-academic-data/494932.png","ImageObject",300,407,{"name":89,"@type":90},"Mia  ","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-10-02",true,{"@type":98,"interactionType":99,"userInteractionCount":71},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Penelitian ini memprediksi apa dan menggunakan data akademik apa?","Question",{"text":108,"@type":109},"Penelitian memprediksi ketepatan waktu kelulusan mahasiswa dengan input atribut akademik berupa IPK, jumlah SKS, dan IPS.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"Metode apa yang digunakan untuk proses analisis data?",{"text":113,"@type":109},"Metode yang digunakan adalah Knowledge Discovery in Database (KDD) yang mencakup seleksi data, preprocessing, transformasi, data mining, dan evaluasi hasil.",{"name":115,"@type":106,"acceptedAnswer":116},"Algoritma mana yang memberikan performa lebih baik dan bagaimana hasilnya?",{"text":117,"@type":109},"KNN memberikan performa lebih baik dibandingkan C4.5, dengan akurasi 76,08% (precision 73,11%, recall 41,92%) sedangkan C4.5 akurasi 73,49% (precision 64,62%, recall 41,89%).","https://schema.org",{"og:url":79,"og:type":120,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":122,"canonical":79},"index,follow",{"doc_id":124,"site_id":56},494932,1790949063,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":89,"user_avatar":129,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":71,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":141},687207024478,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Penerapan Metode C4.5 dan K-Nearest Neighbor untuk Klasifikasi Kelulusan Mahasiswa Berdasarkan Data Akademik  \nDina Amalia Putri1*, Naza Sefti Prianita2, Elkin Rilvani3  \n1-3Teknik Informatika, Universitas Pelita Bangsa  \n[Email :](Email : dinaamaliap33@gmail.com1)[ ](Email : dinaamaliap33@gmail.com1)[dinaamaliap33@gmail.com](Email : dinaamaliap33@gmail.com1)[1](Email : dinaamaliap33@gmail.com1), [nazasefti5@gmail.com](nazasefti5@gmail.com2)[2](nazasefti5@gmail.com2), [elkin.rilvani@gmail.com](elkin.rilvani@gmail.com3)[3](elkin.rilvani@gmail.com3)  \n[Korespondensi penulis :](Korespondensi penulis : dinaamaliap33@gmail.com)[ ](Korespondensi penulis : dinaamaliap33@gmail.com)[dinaamaliap33@gmail.com](Korespondensi penulis : dinaamaliap33@gmail.com)*  \nAbstract. The issue of determining the number of students' graduation times is one of the important indicators in transmitting the quality and effectiveness of the higher education process in universities. The rate of on-time graduation not only impacts accredited institutions, but also becomes a concern for campus management in designing learning strategies and academic guidance. This study aims to apply and compare two classification algorithms in data mining, namely C4.5 and K-Nearest Neighbor (KNN), in predicting the accuracy of students'graduation times. Predictions are made based on academic attributes such as Grade Point Average (GPA), number of credits that have been achieved, and Semester Grade Point Average (IPS) as input variables. The method used in this study is Knowledge Discovery in Database (KDD) which includes data selection, preprocessing, transformation, data mining, and evaluation of results. The study was conducted using the RapidMiner tool, with a dataset of 279 Informatics Study Program students from the 2015 to 2019 intake. The data was classified into two categories: \"graduated on time\" and \"not graduated on time\". The test results showed that the KNN algorithm provided better performance compared to C4.5. KNN produced an accuracy of 76. 08%, with a precision of 73. 11% and a recall of 41.92%. Meanwhile, the C4.5 algorithm produced an accuracy of 73.49%, with a precision of 64. 62% and a recall of 41.89%. This difference in accuracy indicates that KNN is more effective in capturing patterns in the data and providing more accurate predictions in this context. Thus, the KNN algorithm can be considered a more optimal method to assist universities in predicting potential student admissions in a timely manner, thus enabling early intervention for students at risk of late graduation. This research also contributes to the development of data mining-based academic decision support systems in higher education.  \nKeywords: C4.5, Data Mining, Graduation Prediction, K-Nearest Neighbor, Student Graduation.  \nAbstrak. Permasalahan ketepatan waktu kelulusan mahasiswa merupakan salah satu indikator penting dalam mengevaluasi kualitas dan efektivitas proses pendidikan tinggi di perguruan tinggi. Tingkat kelulusan tepat waktutidak hanya berdampak pada akreditasi institusi, tetapi juga menjadi perhatian bagi manajemen kampus dalam merancang strategi pembelajaran dan bimbingan akademik. Penelitian ini bertujuan untuk menerapkan dan membandingkan dua algoritma klasifikasi dalam data mining, yaitu C4.5 dan K-Nearest Neighbor (KNN), dalam memprediksi ketepatan waktu kelulusan mahasiswa. Prediksi dilakukan berdasarkan atribut akademik seperti Indeks Prestasi Kumulatif (IPK), jumlah SKS yang telah ditempuh, dan nilai Indeks Prestasi Semester (IPS) sebagai variabel input. Metode yang digunakan dalam penelitian ini adalah Knowledge Discovery in Database (KDD) yang mencakup tahap seleksi data, preprocessing, transformasi, data mining, dan evaluasi hasil. Penelitian dilakukan menggunakan tools RapidMiner, dengan dataset sebanyak 279 data mahasiswa Program Studi Informatika dari tahun angkatan 2015 hingga 2019. Data diklasifikasikan ke dalam dua kategori yaitu \"lulus tepat waktu\" dan \"t","cbCaid7cANtpXtb3","https://ap.wps.com/l/cbCaid7cANtpXtb3","pdf",571631,12,"Indonesian","# Latar Belakang\n# Metode Penelitian\n## Knowledge Discovery in Database (KDD)\n## Algoritma C4.5 dan KNN\n## Pengujian dan Evaluasi\n# Hasil dan Pembahasan\n# Kesimpulan","[{\"question\":\"Penelitian ini memprediksi apa dan menggunakan data akademik apa?\",\"answer\":\"Penelitian memprediksi ketepatan waktu kelulusan mahasiswa dengan input atribut akademik berupa IPK, jumlah SKS, dan IPS.\"},{\"question\":\"Metode apa yang digunakan untuk proses analisis data?\",\"answer\":\"Metode yang digunakan adalah Knowledge Discovery in Database (KDD) yang mencakup seleksi data, preprocessing, transformasi, data mining, dan evaluasi hasil.\"},{\"question\":\"Algoritma mana yang memberikan performa lebih baik dan bagaimana hasilnya?\",\"answer\":\"KNN memberikan performa lebih baik dibandingkan C4.5, dengan akurasi 76,08% (precision 73,11%, recall 41,92%) sedangkan C4.5 akurasi 73,49% (precision 64,62%, recall 41,89%).\"}]","Penerapan Metode C4.5 dan K-Nearest Neighbor untuk Klasifikasi Kelulusan Mahasiswa Berdasarkan Data Akademik | PDF",1790899817,18]