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All Journal ARRUS Journal of Social Sciences and Humanities ARRUS Journal of Mathematics and Applied Science
Agung Tri Utomo
Universitas Negeri Makassar
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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Application of LASSO Regression for the Identification of Underdeveloped Regions in Central Sulawesi Muh. Qodri Alfairus; Husnul Amira; Agung Tri Utomo; Nur Abshari Abbas
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.
Earth System Variables as Drivers of Environmental Commodity Price Dynamics: A Systematic Review of Physics-Informed and Data-Driven Modelling Approaches (2010–2026) Muhammad Nusrang; Ansari Saleh Ahmar; Abdul Rahman; Agung Tri Utomo; Muh. Qodri Alfairus
ARRUS Journal of Social Sciences and Humanities Vol. 6 No. 3 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/soshum4973

Abstract

Environmental commodity markets (carbon allowances, electricity, natural gas, and renewable energy instruments) are, at their root, earth system markets: their price-generating processes are as much a product of atmospheric circulation, hydrological regimes, and climate teleconnections as of supply-demand fundamentals or regulatory signals. Despite this physical reality, the machine learning forecasting literature has treated these markets primarily as benchmarking arenas, decoupling predictive architectures from the physical processes that drive price-relevant forcing. This systematic review follows PRISMA 2020 guidelines on a corpus of 413 peer-reviewed studies drawn from 652 Scopus records (2010–2026) and examines how earth system variables, including temperature anomalies, precipitation regimes, wind resource indices, atmospheric pollution metrics, and climate teleconnections such as ENSO and NAO, have been integrated into ML-based environmental commodity price models, and evaluates the evidence for whether physics-informed feature engineering confers measurable accuracy advantages over purely data-driven approaches. Bibliometric analysis reveals rapid field expansion (71.6% of publications in 2021–2026), geographic concentration in Chinese ETS research (?68% of high-impact output), and methodological dominance of hybrid decomposition-deep learning architectures. Models incorporating earth system variables consistently outperform endogenous-only ML architectures by 12–35% on MAPE, yet fewer than 30% of corpus studies include any physical predictor and climate teleconnection indices appear in under 4% of studies despite their relevance at energy market planning horizons. Six research priorities are identified, centred on numerical weather prediction ensemble integration, cross-climate-regime validation, and probabilistic forecasting grounded in physical uncertainty quantification.
Co-Authors Abdul Rahman Ahmar, Ansari Saleh Alfairus, Muh. Qodri Husnul Amira Muhammad Nusrang Nur Abshari Abbas