APPLICATION OF K-NEAREST NEIGHBOR REGRESSION METHOD FOR RICE YIELD PREDICTION

Lestari Handayani, Alif Alfarabi.B, Tasya Aprilia, Indah Wulandari, Jasril Jasril, Siti Ramadhani, Elvia Budianita

Abstract


Rice plants with the Latin name Oryza Sativa are food plants that are widely used as the main food crop in various countries, one of which is Indonesia. Indonesia is ranked 4th as the largest rice consuming country in the world. This requires the availability of rice to be maintained. Unstable rice production can be a problem. One of the districts that has experienced a decline in rice production in recent years is the district of Lima puluh kota located in West Sumatra province. This requires prediction of rice production so that it can be used as a benchmark for the future. This study uses data on rice production in fifty cities from 2013 to 2023. The method used to predict is k-nearest neighbor regression (KNN Regression). The data division uses rasio 90 : 10. In testing the data used is divided into 2, namely normal data and data that has been normalized. The test results produce the smallest mean absolute percentage error (MAPE) value of 6.98% on normal data, the value of k is 6 with data division using k-fold 5. Based on the resulting MAPE value, it can be said that KNN Regression can predict rice production results very accurately.


Keywords


K-Nearest Neighbor Regression; Mean Absolute Percentage Error; Rice; Prediction

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References


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DOI: http://dx.doi.org/10.24014/coreit.v11i1.30907

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