Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes

Firman Tempola


This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.


Naive Bayes; Support Vector Machine; Particle Swarm Optimization; Volcanoes

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Abraham, A., Grosan, C., & Ramos, V. (2006). Swarm Intelegence in Data Mining. London: Verlag Berlin Heidelberg, Springer.

Agustina C, 2017 “Optimasi Naive Bayes Menggunakan Particle Swarm Optimization Untuk Meningkatkan Akurasi Deteksi Autisme Spectrum Disorder,” J. Speed – Sentra Penelit. Eng. dan Edukasi, vol. 10, no. 2, pp. 1–5.

Gorunescu F, (2011) Data Mining: Concepts, Models and Techniques. London: Springer, 2011.

Harrington, P., (2012). Machine Learning in Action. USA: Manning Publication.

Idrus A, Brawijaya H, and Maruloh, 2018 “Sentiment Analysis of State Officials News on Online Media Based on Public Opinion Using Naive Bayes Classifier Algorithm and Particle Swarm Optimization,” 2018 6th Int. Conf. Cyber IT Serv. Manag. CITSM 2018, no. Citsm, pp. 1–7.

Kumar S N, Dinesh D, Siddharth T, Ramkumar S, Nikhill S, and Lavanya R, 2017 “Selection of features using particle swarm optimization for microaneurysm detection in fundus images,” in Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET, vol. 2018-Janua, pp. 140–144.

Muhamad H et al., 2017 “Optimization Naïve Bayes Classifier using Particle swarm optimization,” J. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 3, pp. 180–184

Pratomo, I. (2006). Klasifikasi Gunung Api di Indonesia, Studi Kasus Dari Beberapa Letusan Gunung Api Dalam Sejarah. Jurnal Geologi Indonesia, vol. 1, No. 4, Desember 2006, 2009-227.

Reath, K. A., & et al. (2016). Predicting eruptions from precursory activity using remote sensing data hybridization. Journal of Volcanology and Geothermal Research. Vol. 321, 18-30. diakses dari [] tanggal 3 Februari 2018

Santosa B and Willy P, (2011) Method metaheuristic, concept and Implementation. Yogyakarta: Graha Ilmu.

Tempola F, Muhammad M, and Khairan A, 2018 “Naive Bayes Classifier for Prediction of Volcanic Status in Indonesia,” Proc. - 2018 5th Int. Conf. Inf. Technol. Comput. Electr. Eng. ICITACEE 2018, pp. 365–369

Tempola F, Muhammad M, and Khairan A, 2018 “Perbandingan Klasifikasi Antara Knn Dan Naive Bayes Pada Penentuan Status Gunung Berapi Dengan K-Fold Cross Validation” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 577–584.


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