[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125285-id":3,"doc-seo-125285-113":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},125285,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",54,"Penelitian & Laporan","Data Mining Pendidikan - Prediksi Gaya Belajar Mahasiswa Teknik Menggunakan Machine Learning","Penelitian menganalisis perilaku belajar mahasiswa pada platform online untuk mendeteksi dan memprediksi gaya belajar agar rekomendasi sumber daya lebih tepat serta hasil belajar meningkat. Prediksi dilakukan dengan membandingkan kinerja beberapa algoritma machine learning, termasuk regresi logistik, pohon keputusan, K-Nearest Neighbors, Support Vector Machine, Neural Network, dan Naive Bayes. Dataset melibatkan 100 mahasiswa teknik yang belajar Arsitektur Komputer selama satu semester. Model terbaik mencapai akurasi klasifikasi 65–78% dengan empat parameter: nilai akhir, predikat, program studi, dan jenis kelamin, sementara K-Nearest Neighbors mencapai akurasi tertinggi sebesar 78%.","DATA MINING PENDIDIKAN: PREDIKSI GAYA BELAJAR MAHASISWATEKNIK MENGGUNAKAN MACHINE LEARNING  \nSumarlin*1, Dewi Anggraini2  \n1,2 Sekolah Tinggi Informatika dan Komputer Uyelindo Kupang, Kupang  \n[Email:](Email:1shumarlin@gmail.com)[1](Email:1shumarlin@gmail.com)[shumarlin@gmail.com](Email:1shumarlin@gmail.com), [2](2thewifoeh@gmail.com)[thewifoeh@gmail.com](2thewifoeh@gmail.com)  \n*Penulis Korespondensi  \n(Naskah masuk: 06 September 2024, diterima untuk diterbitkan: 19 Juni 2025)  \nAbstrak  \nDalam platform online, pembelajar yang berbeda memiliki gaya belajar yang berbeda berdasarkan perilaku belajar. Oleh karena itu, menganalisis perilaku dan mendeteksi gaya belajar mahasiswa adalah penting untuk memberikan rekomendasi sumber daya yang tepat, sehingga meningkatkan hasil belajar mahasiswa. Untuk memprediksi gaya belajar mahasiswa, dihitung dan dibandingkan kinerja algoritma pembelajaran mesin sepertiregresi logistik, pohon penentuan, K-Nearest neighbour, support vector machine, neural network, dan Naive Bayes. Dataset terdiri dari seratus mahasiswa teknik yang belajar Arsitektur Komputer selama satu semester. Studi berbasis data seperti ini sangat penting untuk membangun sistem analisis pembelajaran di institusipendidikan tinggi dan membantu proses pengambilan keputusan. Hasilnya menunjukkan bahwa model yang disarankan mencapai akurasi klasifikasi sebesar 65–78% dengan hanya empat parameter digunakan: nilai akhir, predikat, program studi, dan jenis kelamin. Hasil menunjukkan bahwa algoritma K-Nearest Neighbour memilikitingkat akurasi 78% tertinggi dibandingkan dengan algoritma machine learning lainnya. Ini menunjukkan bahwa ada korelasi yang signifikan antara data aktual dan data prediksi. Hasilnya menunjukkan bahwa 78% sampel diklasifikasikan dengan benar. Hasil empiris dari penelitian ini memungkinkan pemahaman yang lebih baik tentang proses penggalian data pendidikan perguruan tinggi saat ini. Pemahaman ini dapat digunakan untuk mempertimbangkan faktor-faktor yang perlu dipertimbangkan oleh para mahasiswa teknik saat membuat keputusan tentang proses pembelajaran.  \nKata kunci: Gaya Belajar, Pembelajaran Mesin, Penggalian Data.  \nEDUCATIONAL DATA MINING: PREDICTING OF ENGINEERING STUDENTS’LEARNING STYLES USING MACHINE LEARNING  \nAbstract  \nIn online platforms, different learners have different learning styles based on learning behavior. Therefore, analyzing behavior and detecting student learning styles is important to provide appropriate resource recommendations, thereby improving student learning outcomes. To predict student learning styles, the performance of machine learning algorithms such as logistic regression, determination trees, K-Nearest neighbors, support vector machines, neural networks, and Naive Bayes are calculated and compared. The dataset consists of one hundred engineering students studying Computer Architecture for one semester. Databased studies like this are essential for building learning analytics systems in higher education institutions and aiding decision-making processes. The results show that the proposed model achieves a classification accuracy of 65–78% with only four parameters used: final grade, predicate, study program, and gender. The results show that the K-Nearest Neighbor algorithm has the highest accuracy rate of 78% compared to other machine learning algorithms. This shows that there is a significant correlation between the actual data and the predicted data. The results show that 78% of the samples were classified correctly. The empirical results of this research enable a better understanding of the current process of mining higher education education data. This understanding can be used to consider factors that engineering students need to consider when making decisions about the learning process.  \nKeywords: Learning Style, Machine Learning, Data Mining,  \n1. PENDAHULUAN  \nData Mining (DM) mudah digunakan dengan pengetahuan pemrograman apapun, topik analitik pembelajaran semakin popule","cbCaihcNE7gaQ6hZ","https://ap.wps.com/l/cbCaihcNE7gaQ6hZ","pdf",1040964,3,1,10,"Indonesian","id",113,"# Pendahuluan\n## Konsep Data Mining dalam Pendidikan\n## Educational Data Mining (EDM)\n## Analisis Pembelajaran dan Perkembangannya","[{\"question\":\"Mengapa analisis perilaku dan deteksi gaya belajar mahasiswa penting?\",\"answer\":\"Karena pembelajar memiliki gaya belajar berbeda berdasarkan perilaku belajar, sehingga pendeteksian dibutuhkan untuk memberikan rekomendasi sumber daya yang tepat dan meningkatkan hasil belajar.\"},{\"question\":\"Algoritma machine learning apa saja yang dibandingkan untuk memprediksi gaya belajar?\",\"answer\":\"Regresi logistik, pohon keputusan, K-Nearest Neighbors, Support Vector Machine, Neural Network, dan Naive Bayes dibandingkan berdasarkan kinerja klasifikasinya.\"},{\"question\":\"Apa hasil terbaik dan metrik akurasi yang dicapai model?\",\"answer\":\"Model yang disarankan mencapai akurasi klasifikasi 65–78% menggunakan empat parameter. Algoritma K-Nearest Neighbors memberikan akurasi tertinggi sebesar 78% dan 78% sampel terklasifikasi dengan benar.\"}]","Data Mining Pendidikan - Prediksi Gaya Belajar Mahasiswa Teknik Menggunakan Machine Learning | PDF",1785897972,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"educational-data-mining-predicting-engineering-students-learning-styles-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/educational-data-mining-predicting-engineering-students-learning-styles-using-machine-learning/125285/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa analisis perilaku dan deteksi gaya belajar mahasiswa penting?","Question",{"text":76,"@type":77},"Karena pembelajar memiliki gaya belajar berbeda berdasarkan perilaku belajar, sehingga pendeteksian dibutuhkan untuk memberikan rekomendasi sumber daya yang tepat dan meningkatkan hasil belajar.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Algoritma machine learning apa saja yang dibandingkan untuk memprediksi gaya belajar?",{"text":81,"@type":77},"Regresi logistik, pohon keputusan, K-Nearest Neighbors, Support Vector Machine, Neural Network, dan Naive Bayes dibandingkan berdasarkan kinerja klasifikasinya.",{"name":83,"@type":74,"acceptedAnswer":84},"Apa hasil terbaik dan metrik akurasi yang dicapai model?",{"text":85,"@type":77},"Model yang disarankan mencapai akurasi klasifikasi 65–78% menggunakan empat parameter. Algoritma K-Nearest Neighbors memberikan akurasi tertinggi sebesar 78% dan 78% sampel terklasifikasi dengan benar.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]