Oyster mushroom house conditions classification using K-Nearest Neighbor

Abstract

Internet of Things devices that were implemented to support condition monitoring and control systems in mushroom houses get a lot of temperature and humidity data from mushroom houses. The large number of temperature and humidity data can be used as the variables or indicators that affect mushroom production. Temperature and humidity data can be classified based on the production of mushrooms produced in one house or mushroom houses. This study aims to determine the classification of temperature and humidity data in mushroom houses based on mushroom production. The method used is a data mining approach based on the K-Nearest Neighbor. This research begins with determining the variables from training data or training data, taking testing data, then the testing data is reprocessed based on the K-Nearest Neighbor method with training data. Finally, evaluation of the method used was carried out by calculating the accuracy value. As a result, the accuracy of the K- Nearest Neighbor method was about 89%. These results are expected to be used to forecast the yield of mushroom production for future research. The forecast can be seen from the pattern of temperature and humidity data that is formed based on a certain period of time.