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Halif, Jenny
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Computer Science & IT

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Model Regresi Linear Berganda untuk Prediksi Tingkat Pengangguran di Provinsi Jawa Barat Halif, Jenny; Wahiddin, Deden; Sanjaya, Iman; Faisal, Sutan
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2312

Abstract

The Open Unemployment Rate (TPT) in West Java has been the highest nationally in recent years. This study aims to predict the TPT in 2025 using the Multiple Linear Regression (RLB) algorithm with variables such as inflation, GRDP, HDI, and population. Secondary data from 2013-2024 was analyzed through preprocessing, PCA, and training-test data division methods. The model was evaluated using RMSE and R-squared, with the results of RMSE 0.0148 and R² 0.5716. Multiple Linear Regression was chosen because it is able to handle many variables at once and provide a quantitative estimate of the contribution of each factor, in contrast to the individual approach which only looks at the influence of one variable separately. These results can serve as the basis for unemployment reduction policies at the regional level.
Co-Authors Faisal, Sutan Iman Sanjaya Wahiddin, Deden