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All Journal Urania Jurnal Ilmiah Daur Bahan Bakar Nuklir Majalah Ilmiah Bijak JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Sains dan Teknologi Journal Sensi: Strategic of Education in Information System Jurnal Sistem Komputer & Kecerdasan Buatan Infotech: Journal of Technology Information Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Abdi Masyarakat Humanis PASCIDEV: Pasundan Social Science Development Jabdimas: Jurnal Pengabdian Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat
Taswanda Taryo
Center for Development of Research Reactor Technology-BATAN, Kawasan Puspiptek, Gedung 31, Serpong 15310, Tangerang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : Journal Sensi: Strategic of Education in Information System

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Data Analysis of Thesis Guidance Students Using Random Forest, Gradient Boosting, and Naïve Bayes Algorithms (Case Study: University of Raharja) Iman, Fajar Nur; Tukiyat, Tukiyat; Taryo, Taswanda
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3771

Abstract

Thesis guidance is a crucial stage in higher education, as the thesis is one of the primary requirements for earning a bachelor's degree. One of the main challenges in thesis guidance is managing consultation data between students and their supervisors. The application of technology and machine learning approaches offers significant potential in addressing this issue. Machine learning algorithms such as Random Forest, Gradient Boosting, and Naïve Bayes can be utilized to automatically analyze thesis guidance data, thereby assisting supervisors in efficiently monitoring student progress. This research aims not only to provide a solution for supervisors in monitoring the progress of their students but also to offer a valuable tool for university management to evaluate the performance of supervisors in providing guidance. Based on the results and comparisons conducted, it can be concluded that the Gradient Boosting method achieves the highest accuracy, reaching 100%, compared to Random Forest with an accuracy of 98.8% and Naïve Bayes with an accuracy of 97.4%. From the testing data results using the Naïve Bayes, Gradient Boosting, and Random Forest algorithms, different accuracy levels were observed. However, the prediction outcomes were consistent: out of 235 testing data, 25 data points were classified as "Not Eligible," and 210 data points were classified as "Eligible" based on the established criteria.
Co-Authors Achmad Hindasyah, Achmad Adhyaksa Waskita, Arya Agung Budi Susanto Agustian, Rizki B., Hamsinah Baharuddin, Hamsinah Budi Susanto, Agung Geni Rina Sunaryo Halimatussadiah, Siti Hamsinah Hamsinah, Hamsinah Hilman, Amirul iman, Fajar nur Madinah, Dzahra Al Makhsun, Makhsun Mardiyanto Mardiyanto Meniek Rachmawati Nafiah, Nafiah Ridwan, Ridwan Rizaldi, Devan rusilowati, umi Saepudin, Asep Salsabilah, Zafira Sarwani Sarwani, Sarwani Sekar Putri, Utari Sholihin, Dwi Rahmad Sunardi, Nardi Sunarsi, Denok Syarifudin Syarifudin Teguh Prasetyo Tukiyat Tukiyat, Tukiyat Ubaidillah Ubaidillah