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All Journal Jurnal Varian Prosiding Seminar Nasional Official Statistics
Irianto, Hikmal Mardian
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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Analisis Pengaruh Pendidikan, Upah Pekerja dan Akses Sanitasi Layak Terhadap Pendapatan Per Kapita di Indonesia Tahun 2023 Rifdianti, Shinta; Irianto, Hikmal Mardian; Hariaddin, Muhammad; Kartiasih, Fitri
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2455

Abstract

Economic welfare disparity across regions in Indonesia remains a challenge in achieving the Sustainable Development Goals (SDGs), particularly Goal 8: decent work and economic growth. One of the key indicators reflecting economic well-being is per capita income. This study aims to analyze the influence of socio-economic variables namely the percentage of high school graduates, average worker wages, and access to proper sanitation on per capita income across 34 Indonesian provinces in 2023. Data were obtained from official publications of Statistics Indonesia (BPS) and analyzed using multiple linear regression to examine the significance of the relationships between variables. The results indicate that all three independent variables have a positive and significant effect on per capita income. These findings highlight the importance of improving secondary education quality, wage protection, and equitable access to sanitation services in promoting inclusive and welfare-based economic growth.
Prediction of CO2 Emissions Using ANN, ARIMAX, and Hybrid ARIMAX-ANN Models Syaharani, Afifah Dayan; Shafira, Hervira Nur; Irianto, Hikmal Mardian; Kartiasih, Fitri
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5045

Abstract

The escalation of carbon dioxide (CO2) emissions has emerged as a critical environmental concern, particularly in the context of Indonesia’s pursuit of sustainable development. This study aims to forecast CO2 emissions in Indonesia using annual time-series data spanning 1967–2023. Three methodological approaches are employed: an artificial neural network (ANN), an autoregressive model with exogenous variables (ARIMAX), and a hybrid ARIMAX-ANN model. The dataset comprises Gross Domestic Product obtained from the World Bank, along with per capita CO2 emissions, per capita natural gas consumption, and per capita hydropower consumption sourced from Our World in Data. The findings of this research demonstrate that the hybrid ARIMAX-ANN model provides the best forecasting performance, as evidenced by the lowest RMSE, MAPE, and MAE values among the other two models. These results suggest that the hybrid model is currently the most reliable for predicting CO2 emissions in the Indonesian context. The study enriches the expanding literature on emission forecasting by providing empirical evidence to support data-driven policymaking for climate change mitigation and sustainable energy development in Indonesia. 
Co-Authors Fitri Kartiasih Hariaddin, Muhammad Rifdianti, Shinta Shafira, Hervira Nur Syaharani, Afifah Dayan