Article Per Year (5 Year)
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This Author published in this journals
All Journal Indonesian Journal of Fundamental and Applied Geography Journal of Loomingulisus ja Innovatsioon
Heinrich Rakuasa
National Research Tomsk State University, Russian Federation
Religion Computer Science & IT Earth & Planetary Sciences Education Environmental Science Law, Crime, Criminology & Criminal Justice Other

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Utilization of Artificial Intelligence for Spatial Decision Support System Glendy Somae; Heinrich Rakuasa
Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v1i2.1260

Abstract

The integration of Artificial Intelligence (AI) into Spatial Decision Support Systems (SDM) is a transformative advancement in improving decision-making processes in various fields, including urban planning, environmental management, and disaster response. This research uses a literature review methodology to systematically collect, analyze, and synthesize existing scientific articles, conference papers, and relevant reports related to AI applications in SDSS. The findings of this study reveal that AI technologies, such as machine learning and natural language processing, significantly enhance data processing capabilities, enabling the analysis of complex spatial data and the identification of hidden patterns that may be missed by traditional methods. Despite the great benefits, challenges related to data quality, ethical considerations, and the need for capacity building among stakeholders are critical to the successful implementation of AI in SDSS. It can be concluded that while AI has the potential to revolutionize spatial decision-making, ongoing research is essential to develop best practices, address ethical implications, and foster collaboration among various stakeholders to create a more sustainable and resilient society.
Utilization of GIS Technology for Mapping Flood-prone Areas in Ambon Island, Indonesia Heinrich Rakuasa; Viktor Vladimirovich Budnikov
Indonesian Journal of Fundamental and Applied Geography Vol. 2 No. 1 (2024)
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/ijfag.v2i1.20249

Abstract

Flooding is one of the most common natural disasters in Indonesia, including on Ambon Island, which can cause significant economic and social losses. This research aims to map flood-prone areas on Ambon Island using Geographic Information System (GIS) technology to map flood hazards and affected residential areas. This research uses variables of elevation, slope, rainfall, land cover, distance from rivers, and soil type. The weighted overlay method was used to produce maps of flood hazards and affected areas. The results showed that the low class flood had an area of 58,114.44 ha, the medium class had an area of 14,066.44 ha, and the high class had an area of 4,733.31 ha, while the built-up land area affected by flooding in the low class had an area of 907.92 ha, the medium class had an area of 3,445.92 ha, and the high class had an area of 1,681.40. The results of this study are expected to make a meaningful contribution to disaster risk management policies on Ambon Island and other areas with similar characteristics.
Co-Authors Glendy Somae Viktor Vladimirovich Budnikov