<![CDATA[The national rice harvest area was recorded at 10.05 million hectares, a decrease of 1.64% compared to the previous year. This fact indicates that the agricultural sector is still facing various challenges that need to be addressed immediately. One of these challenges comes from pest attacks that can disrupt farmers’ productivity. Therefore, this research aims to assist farmers in Indramayu Regency in monitoring and predicting the risk level of pest attacks on rice crops using the Tsukamoto Fuzzy Logic method. The main focus of this system is on three types of pests that most frequently attack, namely green leafhoppers, brown planthoppers, and stem borers. The data used is obtained from Internet of Things (IoT) devices that record environmental conditions such as temperature, humidity, rainfall, and rice plant age in real time. The predicted risk levels are presented visually in the form of a pie chart showing the percentage of risk, as well as a distribution map (heatmap) based on location coordinates. This system is integrated into a mobile application so that it can be directly accessed by farmers in the field. With this system, it is expected that farmers will be more alert and take appropriate preventive measures to avoid losses caused by pest attacks.]]>
Copyrights © 2026
