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All Journal JURNAL DINAMIKA EKONOMI PEMBANGUNAN Journal of Information Systems and Informatics
Eko Riyanto
Diponegoro University
Computer Science & IT Economics, Econometrics & Finance

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ANALISIS PROFITABILITAS PADA INDUSTRI TEKSTIL DAN PRODUK TEKSTIL DI INDONESIA Eko Riyanto
JURNAL DINAMIKA EKONOMI PEMBANGUNAN Vol 3, No 2: Agustus 2020
Publisher : Fakultas Ekonomika dan Bisnis, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jdep.3.2.77-89

Abstract

This study aims to analyze profitability in the relationship between structure and performance of the textile and textile product industry in Indonesia with an annual 2011-2014. The type of data used is secondary data obtained from the Badan Pusat Statistik (BPS) of  Indonesia. This study employs panel data regression with Fixed Effect Model (FEM). This research uses panel data to see effect of concentration ratio (CR4), internal efficiency (XEF), output value growth (Growth), and labor productivity (Prod) on profitability (PCM).The results of this research shows that the structure of textile and textile product industry in Indonesia is high-moderate oligopoly, with an average value of CR4 is 60.93 percent. The results of the regression shows internal efficiency (XEF) and labor productivity (Prod) have a positive significant effect on the profitability (PCM). While the concentration ratio (CR4) and output growth (Growth) does not have a significant effect on the profitability (PCM).
Comparative Machine Learning Bias Correction of Satellite-Derived Solar Irradiance at Tropical Stations in Central Java, Indonesia Eko Riyanto; Iwan Setiawan; Uky Yudatama
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1778

Abstract

Satellite-derived Global Horizontal Irradiance products carry systematic bias in tropical regions, so site-level correction is a prerequisite for their use where ground networks are sparse. This study compares bias-correction methods for NASA POWER daily irradiance in Central Java, Indonesia, and asks how much reported accuracy reflects algebraic dependence between predictors and the ground reference. Ten PUSDATARU stations were analysed over 2020–2024 (17,072 station-days; 8,459 from five exactly co-located stations). Random Forest, XGBoost, LightGBM, Elastic Net, and Quantile Mapping were trained chronologically (2020–2022/2023/2024) against persistence and climatology baselines, with preprocessing fitted on training data only, paired significance tests, leave-one-station-out validation, and SHAP attribution. Random Forest reduced RMSE from 3.398 to 2.674 MJ m⁻² day⁻¹ (21.3%), lowered mean bias from 0.988 to 0.221, and raised R² from 0.266 to 0.546; across ten stations XGBoost reduced error from 5.921 to 2.831. Differences among tree ensembles were not statistically robust, and the weighted ensemble gave no gain at the highest inference cost. Adding ground-observed sunshine as a predictor raised the coefficient of determination to 0.9994, identified as recovery of the Ångström–Prescott expression defining the reference (70.9% attribution). Near-perfect accuracies against sunshine-derived references therefore measure leakage, not skill; correction also failed to transfer spatially, motivating sub-degree geostationary products as the next step.
Co-Authors Iwan Setiawan Uky Yudatama