Prediksi Sebaran Hama Padi Dengan Metode LSTM Pada Pertanian Padi Di Buleleng - Negara, I Gede Sunia; Sugiartawan, Putu; Murpratiwi, Santi Ika (INFOTEKS (Information Technology, Computer and Sciences), 2022)
Prediction is a systematic process of estimating future values based on patterns contained in data that has been converted into numerical form. In this study, the aim was to predict the distribution of rice borer in Buleleng district which could endanger the productivity of the rice agricultural sector. One of the methods used in this research is Long Short Term Memory (LSTM), a form of development of Recurrent Neural Network (RNN) which is suitable for processing and predicting time series data. The data used in this study is rice borer attack data for the last ten years, from 2012 to 2021. The results show that the LSTM model has an MAE data testing of 16.8149 and MAPE data testing of 2.356%, and MAE data training of 16.8149 and MAPE data training of 2,356%. These values measure the prediction error with the MAE and MAPE techniques. With these results, the agricultural service can recognize the pattern of distribution of rice borer attacks in the region and take appropriate action to overcome them.