IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI HARGA CABAI MERAH KOTA PAGAR ALAM MENGGUNAKAN METODE K-NEAREST NEIGHBOR - sasmita -; Siti muntari (BETRIK, 2024)
This research aims to implement data mining in predicting the price of red chili plants in Pagar Alam City using the K-Nearest Neighbor algorithm. The current problem is that farmers and the food security and fisheries service still predict chili prices by conducting direct survey methods to the market and the data is not further processed on the data, especially if the price of chili is uncertain and changes, there are no conclusions for the category for classifying historical prices, of course this is a consideration on how to anticipate this problem. The method of collecting data to obtain the information needed is by Observation, Interview, Literature Study, and Documentation. This study uses the K-Nearest Neighbor Algorithm with the CRISP-DM method. Where the stages include Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data is processed using the Rapid Miner application, the testing method uses Root Mean Square Error (RMSE). It was found that the results of this study were for the calculation of Root Means Square Error and obtained an average value of 0.09% and for the calculation using RapidMiller with the K-Nearest Neighbor algorithm, the Accuracy was 94.03% with Recall 83.56% and Precession 88.73%