Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

Автор: Muhammad Resa Arif Yudianto, Tinuk Agustin, Ronaldus Morgan James, Firstyani Imannisa Rahma, Arham Rahim, Ema Utami

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 3 vol.13, 2021 года.

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Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.

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Rainfall forecasting, Naive Bayes, Moving Average, Crops, Prediction.

Короткий адрес: https://sciup.org/15017702

IDR: 15017702   |   DOI: 10.5815/ijmecs.2021.03.03

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