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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Angkasa: Jurnal Ilmiah Bidang Teknologi Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) G-Tech : Jurnal Teknologi Terapan Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII
Joko Prasojo
Institut Teknologi Nasional Yogyakarta
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Application of Genetic Algorithm Neural Network in Identifying Buildings in Landslide-Prone Areas Pratama, Bagus Gilang; Sari, Sely Novita; Prasojo, Joko
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7168

Abstract

Indonesia is a disaster-prone country, one of which is landslides, which often occur in hilly areas with high rainfall. The impact damages the environment and infrastructure, especially buildings. For effective mitigation, a risk identification system based on artificial intelligence technology is needed. This study applies Genetic Algorithm Neural Network (GANN) in identifying buildings in landslide-prone areas. GANN was chosen for its ability to optimize network weights globally through selection, crossover, and mutation mechanisms, thus avoiding suboptimal local solutions. The dataset consists of 169 data with 12 structural features of the building. The model was configured with genetic parameters such as the number of generations 500, population size of 50, mutation rate of 10%, and the Stochastic Universal Sampling selection method. To Evaluate the performance of model created from dataset, we employed accuracy, precision, recall, and F1-score. The results showed an accuracy of 81% and an average F1-score of 0.82, with the best performance in the "Unsafe" class (recall 0.84). Although it still needs improvement, GANN has proven to have the potential as a decision support tool in data-driven landslide risk mitigation.
Co-Authors Arif Basuki Asniar Aliyu Bagus Gilang Pratama, Bagus Gilang Dwika Nove Rahmanto Dwika Nove Rahmanto Joko Firdaus Muklis F. Azari Prasojo NUGROHO, TRIYAN WAHYU Oni Yuliani Sely Novita Sari Sudiana Tito Tiyastono Taufik Trie Handayani, Trie Tugino Wahyu Eko Prasetyo