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All Journal Bulletin of Electrical Engineering and Informatics Prosiding SNATIF Jurnal Transformatika Jurnal Informatika Universitas Pamulang Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Journal of Future Artificial Intelligence and Technologies
Budi Widjajanto
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : Bulletin of Electrical Engineering and Informatics

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Optimizing earthquake damage prediction using particle swarm optimization-based feature selection Anisa Sri Winarsih, Nurul; Anggi Pramunendar, Ricardus; Fajar Shidik, Guruh; Widjajanto, Budi; Syaifur Rohman, Muhammad; Oka Ratmana, Danny
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8421

Abstract

Earthquakes have destroyed the economy and killed many people in many countries. Emergency response actions immediately after an earthquake significantly reduce economic losses and save lives, so accurate earthquake damage predictions are needed. This research looks at how machine learning (ML) techniques are used to predict damage from earthquakes. The ML algorithms used are k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and Naïve Bayes (NB). Feature selection is necessary, it needs to select the most relevant features from big data. One of the most commonly used algorithms to optimize ML is particle swarm optimization (PSO). PSO is also suitable for feature selection. This research compares various of PSO. Based on research, the RF algorithm with Phasor PSO has the highest fitness score. This process succeeded in reducing features from 38 features to 14 features. Based on the process after feature selection, it was found that the KNN, DT, and RF algorithms had improved. RF obtained the best accuracy, namely 72.989%. The processing time in DT, RF, and NB is faster than before. In conclusion, the ML algorithm can be combined with PSO feature selection to create a classification model that provides better performance than without feature selection.
Co-Authors Achmad Wahid Kurniawan Anggi Pramunendar, Ricardus Anisa Sri Winarsih, Nurul Budi, Setyo De Rosal Ignatius Moses Setiadi Dewi Agustini Santoso Ery Mintorini Fajar Shidik, Guruh Hariyadi, Guruh Taufan Ichwan Setiarso, Ichwan Ida Farida Ifan Rizqa Imam Nuryanto Jaka Prasetya Juli Ratnawati Karis Widyatmoko karmila karmila Kurniawan, Setyo Budi Kusumaningrum, Lely Kusumaningrum Lewa, Andi Hallang Mertha Agung Durya, Ngurah Pandji Moch. Sjamsul Hidajat Muhamada, Keny Neni Kurniawati Nova Rijati Ojugo, Arnold Adimabua Oka Ratmana, Danny Pandji Mertha Agung Durya, Ngurah Prawitasari, Dian Prihandono, Adi Puji Purwatiningsih, Aris Puspita Sari, Raden Ayu Aminah Rizkia Retno Astuti Setijaningsih Sasono Wibowo Setiawan, Aries Sudibyo, Usman Syaifur Rohman, Muhammad Yenny Ernitawati Yulita Setiawanta Zaenal Sugiyanto