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All Journal Jurnal Rekayasa Mesin Speed - Indonesian Journal on Computer Science Speed - Sentra Penelitian Engineering dan Edukasi REKAYASA Et-Tijarie: Jurnal Hukum dan Bisnis Syariah Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) INTEGER: Journal of Information Technology Syntax Literate: Jurnal Ilmiah Indonesia DIKLUS: Jurnal Pendidikan Luar Sekolah Jurnal Nusa Sylva RISTEK : Jurnal Riset, Inovasi, dan Teknologi Kabupaten Batang Generation Journal Al-Manhaj: Jurnal Hukum dan Pranata Sosial Islam Innovation in Research of Informatics (INNOVATICS) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Bulletin of Science Education RISTEK: Jurnal Riset, Inovasi dan Teknologi Kabupaten Batang Journal of Computer Science and Research Jurnal Pelita Sriwijaya Power Elektronik : Jurnal Orang Elektro Journal of Technology and Computer (JOTECHCOM) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal Pemberdayaan Masyarakat dan Komunitas
Rudi Hermawan
Program Studi Sistem Informasi Universitas Surakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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Journal : Journal of Computer Science and Research

 1 
Classification of Student Activity Status Using Machine Learning Algorithms at Royal University Hermawan, Rudi; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Inactivity is a significant challenge that impacts academic performance, retention rates, and the operational effectiveness of higher education institutions. Royal University faces an urgent need to identify students at risk of becoming inactive early, so that academic interventions can be carried out appropriately and effectively. This study aims to develop a classification model for student inactivity status (Active or Passive) using a machine learning approach, by testing three main algorithms: Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The dataset used consists of 642 student entries, including academic information such as Grade Point Average (GPA), total credits taken, attendance percentage, number of courses per semester, and semester level. The methodology steps include data cleaning and transformation, splitting the dataset into 80% training data and 20% testing data using a random sampling method ( train_test_split with random_state = 42), model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results show that DT and SVM achieve the highest accuracy of 98.44%, with maximum precision in predicting active students, while RF excels in recall (0.96), making it more effective in detecting active students at risk of being missed. Feature importance analysis reveals that GPA and attendance are the most determining factors in predicting student active status, while the number of courses, credits taken, and semester level have a lower additional influence. The primary contribution of this research is the provision of an accurate and practically applicable classification model, enabling universities to conduct automated student monitoring, proactive academic interventions, and data-driven decision-making. Implementing this model in academic information systems can improve the effectiveness of advising programs, reduce the risk of student inactivity , and support efforts to improve retention and graduate quality. This research also emphasizes the importance of contextual features in improving prediction accuracy and provides insights that can be leveraged for the development of data-driven academic strategies
Co-Authors Abd. Rasyid Syamsuri Adhy, Dewanto Rosian Agus Hikmat Al Mursyid, Mohammad Irsyad Alfian Alfian Anggara, Mohamad Bayu Apriliani, Erlina Arista, Ivan Anindito Aryanto, Renaldi Ashlihah, Ashlihah Astuti - Bambang Eka Purnama BUDI TJAHJONO Costa, Anthony Dendy Adanta Dewi Puspita Sari Didik Nur Huda Dwi Yulistyanti, Dwi Edi Setiyo Erlanda Augupta Pane Faddillah, Rani Desy Fatimah Zahro Fauzi, Ahmad Miftah Fitriyah Fitriyah Galuh Widitya Qomaro Ghaffar, Nurshofi Gita Kencanawaty, Gita Handayani, Shinta Dwi HARRIS, SOEPARDI Heni Pujiastuti Hermudananto, Hermudananto Irvan Irvan Jauhari, Mohammad Agitha Juharsa, Jujun Jumadi Jumadi Kartini, Dewi Khoiriyah, Fitrotul Khoiriyah, Umi Khozinatul Kumala, Siti Ayu Landa, Silvia Anastasia Lapau, Fitri LILIK BUDIPRASETYO Luthfie, Muhammad Marwani Marwani, Marwani Mauhib, Akpil Ma’sum, Mohamad Meldianto, Efri Mochamad Amri Santosa Mualifah, Leni Muhammad Iqbal Muhammad Yusup Murianty, Riana Nizirwan Anwar Noor, Yudin Wahyudin Nur Widiyasono, Nur Nusroh, Alfi Chumidafin Pane, Erlanda Patriana, Ari Prakoso, Akbar Teguh Pratama, Fitrianto Adi Puspita, Fitriyah Qardhawi, Youssuf Rachmadini, Syafira Rahayu, Kadek Ridoi Rajahasta, Patih Rosada, Nyataku Ibnu Rosyiidah, Afiifah Al Safitri, Mazda Leva Okta Saputra, M. A. Ade Saputra, Suranto Sari, Hilda Asyifa Siska Iriani Siti Maesaroh Sudrajat Sudrajat Suhendar, Asep Sukadi . Sukoyo, Kuntari Sri Sutrisno, Sutrisno syofii, imam Tarigan, Masmur Titiek Setyawati Wadirin, Wadirin Yadi, Farhan Yeasy Agustina Sari, Yeasy Agustina Zulkifli, Miranti Triana