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

Dedy Ariyadi(1), Taghfirul Azhima Yoga Siswa(2*), R Rudiman(3),

(1) Universitas Muhammadiyah Kalimantan Timur, Indonesia
(2) Universitas Muhammadiyah Kalimantan Timur, Indonesia
(3) Universitas Muhammadiyah Kalimantan Timur, Indonesia
(*) Corresponding Author

Abstract


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%.


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References


K. Desderius, M. S. B. Arrinjani, Z. F. Sa’adia, and F. R. Lie, “Analisis tingkat risiko bencana tanah longsor di wilayah Kabupaten Blitar, Jawa Timur,” Reg. J. Pembang. Wil. dan Perenc. Partisipatif, vol. 19, no. 1, p. 200, 2024, doi: 10.20961/region.v19i1.58889.

D. A. Zakarias Demon Daton, “Bencana Longsor Terjang 9 Titik di Samarinda, 4 Rumah Warga Rusak,” Kompas.com, 2021. https://regional.kompas.com/read/2021/07/02/174254478/bencana-longsor-terjang-9-titik-di-samarinda-4-rumah-warga-rusak

M. F. Yassar et al., “Penerapan Weighted Overlay Pada Pemetaan Tingkat Probabilitas Zona Rawan Longsor di Kabupaten Sumedang, Jawa Barat,” J. Geosains dan Remote Sens., vol. 1, no. 1, pp. 1–10, 2020, doi: 10.23960/jgrs.2020.v1i1.13.

K. W. Gusti, “Klasifikasi Bencana Alam Pada Twitter Menggunakan Naïve Bayes, Support Vector Machine Dan Logistic Regression,” Technol. J. Ilm., vol. 14, no. 4, p. 349, 2023, doi: 10.31602/tji.v14i4.11614.

M. Y. R. Rangkuti, M. V. Alfansyuri, and W. Gunawan, “Penerapan Algoritma K-Nearest Neighbor (Knn) Dalam Memprediksi Dan Menghitung Tingkat Akurasi Data Cuaca Di Indonesia,” Hexag. J. Tek. dan Sains, vol. 2, no. 2, pp. 11–16, 2021, doi: 10.36761/hexagon.v2i2.1082.

A. Widiastari, S. Solikhun, and I. Irawan, “Analisa Datamining dengan Metode Klasifikasi C4.5 Sebagai Faktor Penyebab Tanah Longsor,” J. Comput. Syst. Informatics, vol. 2, no. 3, pp. 247–255, 2021, [Online]. Available: http://ejurnal.seminar-id.com/index.php/josyc/article/view/741

J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0192-5.

N. Razali, S. Ismail, and A. Mustapha, “Machine learning approach for flood risks prediction,” IAES Int. J. Artif. Intell., vol. 9, no. 1, pp. 73–80, 2020, doi: 10.11591/ijai.v9.i1.pp73-80.

N. N. Sholihah and A. Hermawan, “Implementation of Random Forest and Smote Methods for Economic Status Classification in Cirebon City,” J. Tek. Inform., vol. 4, no. 6, pp. 1387–1397, 2023, doi: 10.52436/1.jutif.2023.4.6.1135.

M. A. Latief, L. R. Nabila, W. Miftakhurrahman, S. Ma’rufatullah, and H. Tantyoko, “Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification,” Int. J. Eng. Comput. Sci. Appl., vol. 3, no. 1, pp. 11–18, 2024, doi: 10.30812/ijecsa.v3i1.3758.

Kantinit, “Particle Swarm Optimization Adalah: Konsep dan Cara Kerja,” Kantinit, 2023. https://kantinit.com/algoritma/particle-swarm-optimization-adalah-konsep-dan-cara-kerja/

R. Syaputra, T. A. Y. Siswa, and W. J. Pranoto, “Model Optimasi SVM Dengan PSO-GA dan SMOTE Dalam Menangani High Dimensional dan Imbalance Data Banjir,” Teknika, vol. 13, no. 2, pp. 273–282, 2024, doi: 10.34148/teknika.v13i2.876.

S. Dwiasnati and Y. Devianto, “Optimasi Prediksi Bencana Banjir menggunakan Algoritma SVM untuk penentuan Daerah Rawan Bencana Banjir,” Pros. SISFOTEK, pp. 202–207, 2021, [Online]. Available: http://seminar.iaii.or.id/index.php/SISFOTEK/article/view/283

I. R. Pratama, M. Maimunah, and E. R. Arumi, “Sistem Klasifikasi Penjualan Produk Alat Listrik Terlaris Untuk Optimasi Pengadaan Stok Menggunakan Naïve Bayes,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2135, 2022, doi: 10.30865/mib.v6i4.4418.

T. Ridwansyah, “Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 2, no. 5, pp. 178–185, 2022, doi: 10.30865/klik.v2i5.362.

B. P. Pratiwi, A. S. Handayani, and S. Sarjana, “Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi Wsn Menggunakan Confusion Matrix,” J. Inform. Upgris, vol. 6, no. 2, pp. 66–75, 2021, doi: 10.26877/jiu.v6i2.6552.




DOI: https://doi.org/10.30645/kesatria.v6i1.574

DOI (PDF): https://doi.org/10.30645/kesatria.v6i1.574.g569

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