Penerapan Metode PSO-SMOTE Pada Algoritma Random Forest Untuk Mengatasi Class Imbalance Data Bencana Tanah Longsor

Ariyadi, Dedy
Siswa, Taghfirul Azhima Yoga
Rudiman, R

Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Vol 6, No 1 (2025): Edisi Januari

Landslides are natural disasters that frequently occur in Samarinda City, with 45-80 affected areas in 2022-2023. The use of machine learning to classify landslide data faces the challenge of data imbalance, which can lead to bias towards the majority class. This study aims to address this issue by implementing the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and optimization using Particle Swarm Optimization (PSO). The data used comes from BMKG and BPBD Samarinda City, consisting of 11 features and 730 records. The results show that SMOTE successfully balanced the data, improving accuracy from 89.91% to 94.76%, an increase of 4.85%.