Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Salwa Tsabitah
Universitas Negeri Jakarta
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Prediksi Risiko Putus Sekolah Menggunakan Long Short-Term Memory Berdasarkan Data Prestasi Akademik Ria Arafiyah; Vera Maya Santi; Muhammad Eka Suryana; Salwa Tsabitah; Rahma Wati Malawat
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.06

Abstract

Student dropout is a critical issue affecting academic quality and institutional performance in higher education. Dropout behavior usually emerges gradually through declining academic performance across semesters. Therefore, time-series modeling is essential to capture such temporal patterns. This study proposes a Long Short-Term Memory (LSTM) model to predict student dropout risk based on semester-wise academic data. The dataset consists of 385 undergraduate students from the Computer Science program, FMIPA, represented by Grade Point Average (GPA) and credit load (SKS) over eight semesters. Student status is converted into a binary label: dropout and non-dropout. To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is applied. Experimental results show that the proposed LSTM model achieves a recall of 1.00 for the dropout class, indicating that all dropout cases are successfully detected. Although the precision remains relatively low due to false positives, the model demonstrates strong potential as a basis for academic monitoring and early intervention systems.
Co-Authors Arafiyah, Ria Muhammad Eka Suryana Rahma Wati Malawat Vera Maya Santi