Perbandingan Algoritme Klasifikasi Untuk Prediksi Cuaca

Amril Mutoi Siregar, Sutan Faisal, Yana Cahyana, Bayu Priyatna


Weather conditions is an air condition in a place with a relatively short time, which is expressed by the value of parameters such as temperature, wind speed, pressure, rainfall, which is another atmospheric phenomenon as the main component. Human activities can be influenced by weather conditions, such as transportation, agriculture, plantation, construction or even sports. Therefore, for determining the weather, getting weather information needs to be made so that it can be utilized by the community. Problems that arise how to make weather predictions automatically so that it can be done by everyone. In this study proposed several algorithms Navie Bayes, Decision Tree, Random Forest to calculate the opportunities of one class from each of the existing group attributes and determine which class is the most optimal, meaning that grouping can be done based on the categories that users enter in the application. The prediction system has been made to obtain an accuracy rate of Navie Bayes of 77.22% with a standard deviation of 29%, a Decision Tree accuracy rate of 79.46% with a standard deviation of 15%, a random forest accuracy rate of 82.38% with a standard deviation of 43%.


Cuaca; Decision Tree; Klasifikasi; Naive bayes; Random forest; Prediksi

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