Predicting Jasmine Rice Yield in Northeastern Region in Missing Data Imputation from Adjusted K Nearest Neighbor Method Based on Decile Mean
Keywords:
missing data, Jasmine rice yield, adjusted K Nearest Neighbor methodAbstract
This research aimed to develop a new method for calculating missing data by using Decile Mean K Nearest Neighbor Bhattacharyya Imputation method--DKNN-BH and compare the efficiency of the new missing data imputation method, DKNN-BH, with Mean Imputation, K Nearest Neighbor--KNN and Decile Mean K Nearest Neighbor Imputation--DKNN methods. This data was collected from the production of Jasmine rice and related data. In order to forecast the Jasmine rice yield prediction in Northeastern Thailand, the missing data was implemented to the DKNN-BH method and each situation was replicated 500 times. The results concluded that the newly developed missing data estimation method, DKNN-BH method, was derived from the fine tuning of KNN method, using decile mean and Bhattacharyya distance. After imputing the Jasmine rice yield in Northeastern Thailand data to these methods and comparing them with the 3 missing data imputation methods, the results showed that the estimated yield of Jasmine rice in Northeastern from DKNN-BH method had the lowest Mean Square Error in all cases at the percentage of missing data at 5% and k equal 11. This method was utilized to predict the Jasmine rice yield in Northeastern using multiple regression method. After comparing the results with real data, the Mean Absolute Percentage Error values were 3.155
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