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

Abstract

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.

Application of artificial neural networks for time series rainfall forecasting in the Loktak lift irrigation command area of Manipur, India

“…If the series is found to be nonhomogeneous, data smoothing is applied. Although there are many methods for data smoothing, such as the Fourier transform, Gaussian filter, exponential smoothing and Savitzky–Golay filter (Large et al, 2019), in the present study, we used the moving average method as it has been applied in many previous studies (Yudianto et al, 2021). The moving average is a technique to reduce the fluctuations in data (Villarreal & Mendoza, 2017), thereby making the data homogeneous.…”

Comparison of machine learning classification algorithms based on weather variables and seed characteristics for the selection of paddy seed

“…According to Pallathadka et al, (2023), this method is recursive and needs to be performed for each sub-tree that begins its root at different nodes. Yudianto et al, (2021) state that this technique can quickly and accurately predict outcomes by assuming that the properties of the input data are conditionally independent with respect to the class. In statistics, naive Bayes classifiers are simple models that use the Bayes theorem for probability.…”

Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian

“…Namun, penelitian ini tidak memaparkan jenis-jenis tanaman yang cocok sebagai komoditas unggulan sesuai dengan kondisi cuaca di wilayah setempat. Penelitian selanjutnya dapat menentukan kalender pola tanam sehingga sektor pertanian bisa memanfaatkan curah hujan secara optimal [52], [53]. Selain itu, penelitian selanjutnya dapat mengembangkan metode hibrida menggunakan model deep learning yang berbeda dan metode PCA lebih ditingkatkan lagi agar lebih aplikatif dan efisien [48].…”