The application of particle swarm optimization (PSO) to improve the accuracy of the naive bayes algorithm in predicting floods in the city of Samarinda
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Aug 24, 2023
Keywords:
accuracy, flood, naive_bayes, Optimization, Particle Swarm Optimization (PSO)
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Abstract
This study focuses on the implementation of Particle Swarm Optimization (PSO) to enhance the accuracy of the Naive Bayes algorithm in predicting floods specifically in the city of Samarinda. The aim is to improve the efficiency and precision of flood prediction models in order to mitigate the impact of flooding in the area. The results of this research highlight the effectiveness of PSO in optimizing the Naive Bayes algorithm, showing promising potential for more accurate flood prediction and proactive measures in Samarinda. The accuracy value obtained from testing using the Naive Bayes method alone is 91.12%. However, there is an improvement in accuracy after conducting testing with the optimization technique based on Particle Swarm Optimization (PSO) and the Naive Bayes algorithm. The conducted testing achieved an accuracy value of 94.38%. This accuracy result is higher compared to testing without optimization.
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Faldi, F., NurHalisha, T., Joko Pranoto , W., Saputra, H., Johar Latipah, A., Harits Suryawan, S. ., & Azmi Verdikha, N. . (2023). The application of particle swarm optimization (PSO) to improve the accuracy of the naive bayes algorithm in predicting floods in the city of Samarinda. Journal of Intelligent Decision Support System (IDSS), 6(3), 138-146. https://doi.org/10.35335/idss.v6i3.148
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Yakup, S. (2020). Diabetes Mellitus Detection Expert System Using a WEB-Based Naïve Bayesian Approach. Journal of Intelligent Decision Support System (IDSS), 3(2), 51–61.
Ali, M., & Farida, B. N. I. (2021). Completion of FCVRP using Hybrid Particle Swarm Optimization Algorithm. Jurnal Teknik Industri, 22(1), 98–112. https://doi.org/10.22219/jtiumm.vol22.no1.98-112
Amrin, A., Pahlevi, O., & Satriadi, I. (2021). Optimasi Algoritma C4. 5 dan Naïve Bayes Berbasis Particle Swarm Optimization Untuk Diagnosa Penyakit Peradangan Hati. INSANTEK-Jurnal Inovasi Dan Sains Teknik Elektro, 2(1), 10–14.
Asri, A. M., Ahmad, S. R., & Yusop, N. M. M. (2023). Feature Selection using Particle Swarm Optimization for Sentiment Analysis of Drug Reviews. International Journal of Advanced Computer Science and Applications, 14(5), 286–295. https://doi.org/10.14569/IJACSA.2023.0140530
Cazacu, M., & Titan, E. (2021). Adapting CRISP-DM for social sciences. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 11(2Sup1), 99–106.
Dåderman, A., & Rosander, S. (2018). Evaluating frameworks for implementing machine learning in signal processing: A comparative study of CRISP-DM, SEMMA and KDD.
Haghighi, S., Jasemi, M., Hessabi, S., & Zolanvari, A. (2018). PyCM: Multiclass confusion matrix library in Python. Journal of Open Source Software, 3(25), 729.
Hasanah, M. A., Soim, S., & Handayani, A. S. (2021). Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir. Journal of Applied Informatics and Computing, 5(2), 103–108. https://doi.org/10.30871/jaic.v5i2.3200
Hasnain, M., Pasha, M. F., Ghani, I., Imran, M., Alzahrani, M. Y., & Budiarto, R. (2020). Evaluating trust prediction and confusion matrix measures for web services ranking. IEEE Access, 8, 90847–90861.
Hayatin, N., Marthasari, G. I., & Nuarini, L. (2020). Optimization of Sentiment Analysis for Indonesian Presidential Election using Naïve Bayes and Particle Swarm Optimization. Jurnal Online Informatika, 5(1).
Huber, S., Wiemer, H., Schneider, D., & Ihlenfeldt, S. (2019). DMME: Data mining methodology for engineering applications–a holistic extension to the CRISP-DM model. Procedia Cirp, 79, 403–408.
Kareem, T. A., Hussain, M. A., & Jabbar, M. K. (2020). Particle swarm optimization based beamforming in massive MIMO systems. International Journal of Interactive Mobile Technologies, 14(5), 176–192. https://doi.org/10.3991/IJIM.V14I05.13701
Markoulidakis, I., Kopsiaftis, G., Rallis, I., & Georgoulas, I. (2021). Multi-class confusion matrix reduction method and its application on net promoter score classification problem. The 14th Pervasive Technologies Related to Assistive Environments Conference, 412–419.
Mustajab, R. (2023). BNPB: Indonesia Alami 3.522 Bencana Alam pada 2022. Dataindonesia.Id.
Naderi, E., Pourakbari-Kasmaei, M., & Abdi, H. (2019). An efficient particle swarm optimization algorithm to solve optimal power flow problem integrated with FACTS devices. Applied Soft Computing, 80, 243–262.
Nofitri, R., & Irawati, N. (2019). Analisis Data Hasil Keuntungan Menggunakan Software Rapidminer. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 5(2), 199–204.
Putri, D. A., Kristiyanti, D. A., Indrayuni, E., Nurhadi, A., & Hadinata, D. R. (2020). Comparison of naive bayes algorithm and support vector machine using pso feature selection for sentiment analysis on e-wallet review. Journal of Physics: Conference Series, 1641(1), 12085.
Sa’diyah, N., Supianto, A. A., & Dewi, C. (2020). Implementasi Algoritme Fuzzy C-Means dengan Particle Swarm Optimization (FCMPSO) untuk Pengelompokan Proses Berpikir Siswa dalam Proses Belajar. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 4(6), 1625–1632.
Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526–534.
Utomo, D. P., & Mesran, M. (2020). Analisis komparasi metode klasifikasi data mining dan reduksi atribut pada data set penyakit jantung. Jurnal Media Informatika Budidarma, 4(2), 437–444.
Venkata, R. B., & Narsimha, G. (2021). A Multi-purpose Data Pre-processing Framework using Machine Learning for Enterprise Data Models. International Journal of Advanced Computer Science and Applications, 12(3), 646–656. https://doi.org/10.14569/IJACSA.2021.0120376
Wiratama, M. A., & Pradnya, W. M. (2022). Optimasi algoritma data mining menggunakan backward elimination untuk klasifikasi penyakit diabetes. Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 11(1), 1–12.
Yakup, S. (2020). Diabetes Mellitus Detection Expert System Using a WEB-Based Naïve Bayesian Approach. Journal of Intelligent Decision Support System (IDSS), 3(2), 51–61.
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