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All Journal JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Muhammad Hafriyandi Saputra
STMIK Widya Cipta Dharma
Computer Science & IT Electrical & Electronics Engineering Engineering

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Developing A Student Dropout Detection System By Using The KNN Model Muhammad Hafriyandi Saputra; Pitrasacha Adytia; Wahyuni Wahyuni
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 2 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i2.537

Abstract

The high number of students experiencing dropout is a serious challenge that must be addressed by higher education institutions, including STMIK Widya Cipta Dharma. This study aims to propose an early detection system that demonstrates the ability to identify students who are at risk of dropping out based on their academic data. The system is developed using the K-Nearest Neighbor (KNN) algorithm as a classification method to predict the risk of student dropout. The data used includes Grade Point Average (GPA), total credits earned, and the duration of study. The test results show that the KNN algorithm can provide a high level of accuracy in categorizing students into dropout risk levels. This system can serve as a support tool for the institution in determining preventive actions to reduce the number of students at risk of failing to complete their studies and to improve the quality of academic guidance.
Co-Authors Pitrasacha Adytia Wahyuni - Wahyuni