Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Al-Aqlu : Jurnal Matematika, Teknik dan Sains
Handoyo, Sapto Mukti
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Mathematics

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

Found 1 Documents
Search

 1 
PREDIKSI INDEKS PERFORMA SISWA BERDASARKAN WAKTU BELAJAR, NILAI SEBELUMNYA, KEGIATAN EKSTRAKURIKULER, WAKTU TIDUR, DAN BANYAKNYA SOAL YANG DIKERJAKAN DENGAN REGRESI LINEAR KUADRAT-TERKECIL: Prediction of Student Performance Index Based on Hours Studied, Previous Scores, Extracurricular Activities, Sleep Hours, and Sample Question Papers Practiced with Least-Squares Linear Regression Handoyo, Sapto Mukti; Najib, Mohamad Khoirun
Al-Aqlu: Jurnal Matematika, Teknik dan Sains Vol. 3 No. 1 (2025): Januari 2025
Publisher : Yayasan Al-Amin Qalbu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59896/aqlu.v3i1.121

Abstract

The students’ performance index is a measure used to represent the overall performance of students. One model that can be used to predict the students’ performance index is a machine learning-based regression model. Therefore, this study aims to apply a machine learning-based least-squares linear regression model to predict the performance index using these factors and interpret the model. The regression model utilized is available in the Julia programming package called MLJ. This model is evaluated based on several criteria, including R-squared, RMSE, and MAE. The results show that the previous scores have the most significant influence on the students’ performance index. Furthermore, the R-squared value for the test data is 0.988, the RMSE for the training data is 0.106, the RMSE for the test data is 0.108, the MAE for the training data is 0.84, and the MAE for the test data is 0.86. Based on the evaluation results, the model has good predictive performance with low average error, does not experience overfitting, and has good generalization ability.
Co-Authors Najib, Mohamad Khoirun