Machine-Learning Prediction of Informatics Students Interest to the MBKM Program: A Study Case in Universitas Pembangunan Jaya

Uddin, Nur and Jaya, Safitri and Purwanto, Edi and Putra, Ananda Arta Dwi and Fadhilah, Muhammad Wildan and Ramadhan, Abimanyu Luthfi Rizq (2021) Machine-Learning Prediction of Informatics Students Interest to the MBKM Program: A Study Case in Universitas Pembangunan Jaya. International Seminar on Machine Learning, Optimization, and Data Science. pp. 146-151. ISSN 978-1-6654-0544-7

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Abstract

This paper presents a prediction model of student interest to join in the MBKM (Merdeka Belajar Kampus Merdeka) program. The MBKM is a new learning program launched by the Indonesian ministry of Education and Culture to improve the quality and competency of the students. This program offers a freedom to the students in accomplishing their study. Since this is a new program, knowing the students interest is very important in preparation, implementation, and improvement of the program. The students interest can be known through a survey, but this is time consuming and expensive. While a survey is difficult to be done, a prediction would be an alternative solution to know the student interest. Machine learning is applied to predict the students interest by implementing support vector machine (SVM) as the learning algorithm. The machine learning is built using a dataset that was obtained through a survey to the students at the Department of Informatics, Universitas Pembangunan Jaya (UPJ). The result shows that the machine learning was able to predict the student interest with accuracy up to 89.29%. Index Terms—Machine learning, support vector machine (SVM), prediction, MBKM program.

Item Type: Artikel Jurnal/Prosiding
Uncontrolled Keywords: Machine learning, support vector machine (SVM), prediction, MBKM program.
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Teknologi dan Desain > Informatika
Depositing User: Admin Repository
Date Deposited: 22 Jun 2022 02:04
Last Modified: 16 Jan 2023 03:32
URI: http://eprints.upj.ac.id/id/eprint/2700

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