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All Journal Journal of Applied Science, Engineering, Technology, and Education Variansi : Journal of Statistics and Its Application on Teaching and Research Quantitative Economics and Management Studies Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology PERADABAN JOURNAL of ECONOMIC and BUSINESS VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Ilmiah Sains ARRUS Jurnal Pengabdian Kepada Masyarakat
Alfairus, Muh. Qodri
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Journal : ARRUS Journal of Mathematics and Applied Science

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Application of LASSO Regression for the Identification of Underdeveloped Regions in Central Sulawesi Alfairus, Muh. Qodri; Amira, Husnul; Utomo, Agung Tri; Abbas, Nur Abshari
ARRUS Journal of Mathematics and Applied Science Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4813

Abstract

This study aims to identify the main factors influencing regional underdevelopment in Central Sulawesi through Human Development Index (HDI) modeling and to develop a robust predictive model. To address the challenges of multicollinearity and the limited number of observations (13 districts/cities with 10 variables), this study employs LASSO (Least Absolute Shrinkage and Selection Operator) regression, which is capable of simultaneously shrinking coefficients and selecting variables. The data used are sourced from the 2019 publication of the Central Statistics Agency (BPS). The analysis was conducted using descriptive statistics, Ordinary Least Squares (OLS) modeling, VIF tests, and LASSO regression with cross-validation (leave-one-out cross-validation). The results indicate that very high multicollinearity (VIF > 10 for most variables) renders the OLS model unstable. Conversely, LASSO regression yielded better performance with superior RMSE (1.282), MAE (1.075), and R² (0.918) values compared to OLS (RMSE 21.67; MAE 9.85; R² 0.78). Thus, LASSO is more suitable for limited data with high multicollinearity. The selected significant variables include the percentage of the poor population, the open unemployment rate, shopping facilities, the presence of hospitals, the population density ratio, and the number of elementary and secondary schools.
Co-Authors Abbas, Nur Abshari Abdul Rahman Ahmar, Ansari Saleh Aidid, Muhammad Kasim Akbar Iskandar Alia Rezki Amalia Alsa, Yudhistira Ananda Amalia, Alia Rezki Amira, Husnul Karina Ayudhia Sasmito Mamadiyarov, Zokir Mubaraq, Muhammad Raihan Muhammad Arif Tiro Muhammad Kasim Aidid Pratama, Muh. Riyaldi Rahmat Hidayat Rais, Zulkifli Rajesh Kumar Ruliana Singagerda, Faurani Santi Siti Maisaroh Sitti Masyitah Meliyana R. Sudarmin Utomo, Agung Tri Utriweni Mukhaiyar Vira Ananda