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All Journal Journal on Education Journal of Innovation Information Technology and Application (JINITA)
Md. Wira Putra Dananjaya
Universitas Pendidikan Nasional
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Analisis Penggunaan Regresi Linier Sederhana dalam Memprediksi Nilai Matematika Berdasarkan Faktor Demografi dan Akademik Md. Wira Putra Dananjaya
Journal on Education Vol 7 No 1 (2024): Journal on Education: Volume 7 Nomor 1 Tahun 2024
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v7i1.7610

Abstract

Penelitian ini bertujuan untuk mengevaluasi kemampuan regresi linier sederhana dalam memprediksi nilai matematika siswa (Math_Score) berdasarkan faktor demografi dan akademik, yaitu Gender, Attendance_Rate, dan Extracurriculars. Dataset yang digunakan diproses melalui teknik encoding untuk variabel kategorikal dan penanganan nilai yang hilang. Model diuji dengan data latih dan data uji, serta dievaluasi menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R-squared (R²). Hasil menunjukkan nilai MAE sebesar 25.72, RMSE sebesar 29.06, dan R² sebesar 0.0118, mengindikasikan bahwa model memiliki kinerja prediktif yang rendah. Visualisasi residual mengungkap pola distribusi yang tidak normal dan sistematis, melanggar asumsi dasar regresi linier. Koefisien regresi menunjukkan bahwa variabel independen memiliki pengaruh yang lemah terhadap prediksi nilai matematika. Kesimpulannya, regresi linier sederhana kurang cocok untuk data ini karena hubungan antarvariabel cenderung kompleks. Diperlukan penambahan variabel relevan, seperti waktu belajar atau tingkat kesulitan materi, serta model prediktif yang lebih kompleks untuk meningkatkan akurasi. Studi ini memberikan wawasan awal dalam analisis faktor-faktor yang memengaruhi performa akademik siswa.
Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study Md. Wira Putra Dananjaya; Putu Gita Pujayanti
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2540

Abstract

Academic performance prediction is a crucial area in education; however, the complexity of influencing factors often cannot be adequately captured by simple linear models. This research conducts a methodological comparative analysis of five machine learning models Simple Linear Regression, Multiple Linear Regression (MLR), Decision Tree, Random Forest, and Artificial Neural Network (ANN) to determine the most accurate predictive approach using a comprehensive dataset encompassing academic, behavioral, and psychosocial factors. The models were evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. Evaluation results on the test data revealed that the Multiple Linear Regression (MLR) model unexpectedly delivered the most superior performance, achieving an R2 value of 0.7324 and the lowest RMSE of 2.0391. Further analysis from non-linear models identified Attendance and Hours_Studied as the two factors with the highest predictive influence. This study concludes that interpretable models like MLR can be highly effective when supported by relevant features, offering practical implications for institutions to develop effective early warning systems by focusing on key, actionable factors.
Co-Authors Putu Gita Pujayanti