Optimizing Weather Forecast Using Ensemble Method on Naïve Bayes and C4.5
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Published:
Dec 21, 2022
Keywords:
Bagging, Boosting, C4.5, Ensemble, Naïve Bayes Bagging, Boosting, C4.5, Ensemble, Naïve Bayes
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Abstract
Weather forecasting is important for the survival of the wider community. Therefore, the accuracy of the weather forecast must be high. Based on this, a study was conducted to improve the accuracy of weather forecasting with the naïve Bayes and C4.5 models and then performed an optimization using the ensemble method. The dataset used is weather data observed from BMKG Bandung for 10 years. Accuracy in the pretest process shows that the naïve Bayes algorithm has an accuracy of 49.45% and the C4.5 algorithm produces 41.24% accuracy, while in the posttest process the accuracy obtained is 49.76% for bagging naïve Bayes, 46.47% for boosting naïve Bayes, 45.76 for bagging C4.5 and 38.82% for C4.5.
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How to Cite
[1]
V. I. Yani, A. Aradea, and H. . Mubarok, “Optimizing Weather Forecast Using Ensemble Method on Naïve Bayes and C4.5”, JuTISI, vol. 8, no. 3, pp. 607 –, Dec. 2022.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.