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All Journal KOMPUTIKA - Jurnal Sistem Komputer Indonesian Journal of Energy
Natasya Afira
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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Analisis Cluster dengan Metode Partitioning dan Hierarki pada Data Informasi Kemiskinan Provinsi di Indonesia Tahun 2019 Natasya Afira; Arie Wahyu Wijayanto
Komputika : Jurnal Sistem Komputer Vol 10 No 2 (2021): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v10i2.4317

Abstract

Poverty is an important indicator in seeing the success of a country's economic development. The poverty rate in Indonesia is 9.22 percent with a total poor population of 24.79 million people. Poverty data in each region will be different, influenced by various indicators. Therefore, it is important to categorize regions in Indonesia based on poverty characteristics so that the government can make the right policies related to poverty reduction. This study uses two clustering methods, namely partitioning and hierarchy to group provinces in Indonesia based on poverty characteristics. The partitioning method chosen is K-Means. The data used are 8 poverty variables in 34 provinces in Indonesia in 2019 The determination of the number of clusters using internal validation and stability validation shows that the hierarchical method with the optimum number of clusters 2 produces the most optimal clusters. The comparison of the hierarchical method is assessed based on the agglomerative coefficient, where the Ward method is able to provide the best grouping results.
AI-Driven Carbon Pricing Optimization: A Geospatial Analysis Framework for Indonesia’s Energy Transition Wijayanto, Arie W.; Putri, Salwa R.; Putra, Yoga C.; Natasya Afira; Anggita, Fauzan F.; Aziz, Jafar H.
Indonesian Journal of Energy Vol. 9 No. 1 (2026): Indonesian Journal of Energy
Publisher : Purnomo Yusgiantoro Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33116/ije.v9i1.289

Abstract

Indonesia faces a critical climate challenge as the world’s sixth-largest carbon emitter, with coal accounting for more than 60% of its electricity generation. Achieving its ambitious net-zero target by 2060 requires urgent action. While Indonesia has introduced various carbon pricing mechanisms to advance carbon neutrality, these initiatives demand sophisticated optimization across the archipelago’s diverse regions to balance emissions reduction with sustainable development goals. This research presents an innovative artificial intelligence framework that leverages geospatial big data to estimate carbon stock and inform pricing strategies while supporting Indonesia’s transition away from coal dependency. The framework integrates three key components: (1) a remote sensing-based Measurement, Reporting, and Verification (MRV) model that accurately quantifies carbon stocks across varied ecosystems; (2) an automated reporting system powered by generative Artificial Intelligence that enhances transparency and reduces bias in carbon accounting; and (3) a comprehensive analytics dashboard that visualizes dynamic carbon stock data to inform policy decisions. By addressing Indonesia’s geographical complexities through tailored carbon stock estimation policies and optimizing resource allocation across diverse ecological contexts, this framework provides a data-driven foundation for Indonesia to navigate its energy transition and meet its climate commitments through enhanced MRV systems and targeted green financing initiatives.
Co-Authors Anggita, Fauzan F. Arie Wahyu Wijayanto Aziz, Jafar H. Putra, Yoga C. Putri, Salwa R. Wijayanto, Arie W.