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All Journal ROUTERS: Jurnal Sistem dan Teknologi Informasi
Putri, Yunita Dwi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Prediksi Kelulusan Siswa dengan Algoritma Pembelajaran Mesin: Aplikasi Regresi Linear dan Logistik pada Faktor-Faktor Pendidikan Fenni Aprilia; Anggraini, Rasti Aulia; Putri, Yunita Dwi
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.3897

Abstract

The main challenge in education lies in accurately predicting student graduation and understanding the factors influencing it. This study aims to provide a data-driven solution using machine learning algorithms, specifically linear regression to predict student exam scores and logistic regression to classify student graduation. The study contributes by developing predictive models that serve as tools to support strategic decision-making in educational institutions. This study utilized the Student Performance Factors dataset, comprising 6,607 samples with independent variables such as study hours, attendance, and parental involvement. Data analysis involved cleaning, transformation, and normalization before applying regression models. The findings showed that linear regression achieved a Mean Squared Error (MSE) of 3.256, indicating high accuracy in predicting exam scores. Logistic regression demonstrated an accuracy of 99.85% in classifying student graduation. These models complement each other by offering strategic insights to enhance educational quality.
Co-Authors Anggraini, Rasti Aulia Fenni Aprilia