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All Journal Bulletin of Electrical Engineering and Informatics Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Anggi Pramunendar, Ricardus
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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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.
Pengaruh Penambahan Arsitektur Model dalam Klasifikasi Citra Bencana Alam Menggunakan Ensemble Learning Amanda Cahyadewi, Felicia; Richo Kurniawan, Ibnu; Umar Fakhrizal, Irsyad; Denta Saputra, Fahrizal; Achmad, Achmad; Naufal, Muhammad; Anggi Pramunendar, Ricardus
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9103

Abstract

Penelitian ini berfokus pada peningkatan akurasi klasifikasi citra bencana alam dengan mengintegrasikan Convolutional Neural Network (CNN), InceptionV3, dan InceptionResNetV2 dalam pendekatan ensemble learning. Model ini dilatih pada dataset multikelas yang terdiri dari citra empat kategori bencana: gempa bumi, banjir, kebakaran hutan, dan siklon. Pendekatan ensemble menghasilkan akurasi klasifikasi sebesar 96,02%, lebih tinggi dibandingkan model tunggal seperti CNN dengan 88,3%, InceptionV3 dengan 94,1%, dan InceptionResNetV2 dengan 92,4%. Penggunaan ensemble learning, khususnya soft voting, memungkinkan model untuk menggabungkan keunggulan dari masing-masing arsitektur, yang secara signifikan meningkatkan performa pada semua kategori bencana. Model ensemble menunjukkan kemampuan generalisasi yang lebih baik, terutama untuk kategori yang lebih sulit seperti Flood dan Earthquake, yang mana model tunggal kesulitan. Hasil juga menunjukkan peningkatan precision, recall, dan F1-score, dengan pendekatan ensemble mengurangi kesalahan klasifikasi sebesar 7,5% dibandingkan model terbaik tunggal, InceptionV3. Penelitian ini menunjukkan potensi ensemble learning untuk sistem deteksi bencana waktu nyata, khususnya dalam situasi kritis yang memerlukan akurasi tinggi dan kecepatan klasifikasi. Penelitian ini juga menekankan pentingnya kombinasi model deep learning yang beragam untuk meningkatkan kemampuan sistem dalam menangani berbagai skenario bencana sambil memastikan ketahanan dalam aplikasi dunia nyata.
Co-Authors Achmad Achmad Amanda Cahyadewi, Felicia Anisa Sri Winarsih, Nurul Budi Widjajanto Denta Saputra, Fahrizal Fajar Shidik, Guruh Muhammad Naufal Oka Ratmana, Danny Richo Kurniawan, Ibnu Syaifur Rohman, Muhammad Umar Fakhrizal, Irsyad