Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Indonesian Journal of Energy East Java Economic Journal
Annisa Dira Hariyanto
PT. Sagamartha Ultima Indonesia
Economics, Econometrics & Finance Energy Social Sciences

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Spatial Decision Support System Model for Economic Resilience in East Java Province Firman Afrianto; Annisa Dira Hariyanto
East Java Economic Journal Vol. 8 No. 1 (2024)
Publisher : Kantor Perwakilan Bank Indonesia Provinsi Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53572/ejavec.v8i1.118

Abstract

Economic resilience has many dimensions and aspects to consider. The complexity of studying resilience ultimately requires simplification in the form of a model that can be applied in spatial decision-making. This study aims to find a model and simulation of economic resilience policy priorities for East Java Province in the form of a Spatial Decision Support System. The calculation is done by applying the TOPSIS algorithm on the vectorMCDA plugin in a geographic information system and qualitative descriptive analysis. The results of the calculation indicate that the focus of economic resilience policy is on the aspects of recovery and transformational innovation, while the ideal resilience policy alternative is the most appropriate priority policy alternative.
Big Data and Satellite Imagery for Energy Efficiency Mapping in Indonesia: : A Future Shaped by Advanced Analytics Firman Afrianto; Andini Putri Salsabillah; Annisa Dira Hariyanto
Indonesian Journal of Energy Vol. 8 No. 1 (2025): Indonesian Journal of Energy
Publisher : Purnomo Yusgiantoro Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33116/ije.v8i1.229

Abstract

In the sophisticated realm of big data, analyzing energy efficiency in Indonesia has become crucial for identifying savings opportunities. This study utilizes large-scale raster data, including CO2 emissions from the OCO-2 GEOS satellite, nocturnal satellite images from VIIRS, and demographic and infrastructural data from WorldPOP and EsriWorld Cover. Through advanced regression techniques in machine learning—Support Vector Regression, Artificial Neural Network, and particularly Random Forest—the research analyzes and forecasts energy efficiency across various Indonesian provinces. The analysis highlights a notable increase in CO2 emissions from 2019 to 2023, with a significant reduction in night-time light emissions in 2020 due to the pandemic, which temporarily decreased human activities. Despite these fluctuations, the continuous increase in population density and built-up areas underscores the persistent influence of urbanization on emissions. The Random Forest model, which provided the most accurate predictions, indicates an expected rise in total CO2 emissions until 2030, driven by urbanization and economic growth, followed by a decline by 2045 due to targeted governmental policies. These insights contribute significantly to understanding the distribution of energy efficiency and support the development of sustainable energy policies in Indonesia. The study not only enriches scientific literature but also guides policy-making, offering a framework for tailored energy efficiency improvements. This research marks a pivotal advancement in utilizing big data and satellite technology to optimize energy use in a context that was previously underexplored.
Co-Authors Andini Putri Salsabillah Firman Afrianto Firman Afrianto