COMPARISON OF K-NEAREST NEIGHBORS AND NAIVE BAYES ALGORITHM FOR WEATHER FORECAST

Teguh Iman Santoso, Hironimus Leong

Abstract


Weather is an atmospheric condition that occurs at a certain time and place. Weather is

very influential in daily life. For example for agriculture, farmers usually plant corn during the

rainy season. That’s why accurate weather predictions are needed. Weather prediction is a way

to know the future weather using science and technology.

To be able to know the weather in the future, certain datasets and algorithms are needed. The algorithm used is Naïve Bayes and K-Nearest Neighbors Both are included in the classification of data mining which aims to group data into a certain category by reading the previous data.

The result of this research is to find out which algorithm is better for weather forecast, between Naïve Bayes and K-Nearest Neighbors. Confusion Matrix is a method to determine the performance results of an algorithm. So that it can be known which algorithm is better in terms of accuracy, precision, recall, and F1 score


Keywords


Data mining; Classification; Weather Forecasting; Naïve Bayes; K-NN

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DOI: https://doi.org/10.24167/proxies.v6i2.12460

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