Abstract

The use of IoT in drip irrigation has been widely applied in previous studies in agriculture to overcome water management problems well in agriculture by utiliz-ing certain sensors in IoT. The method used in this study to classify IoT-based drip irrigation data is the Support Vector Machine (SVM) method. The SVM method is used to find the best classification and distinguish be-tween the two classes in the training data. From the results of the data using the SVM method, the original data before diff has an accuracy of 0.8334 and after diff is done on the data it results in an increase in accuracy to 0.9127. It can be concluded that the data after diff has greater accuracy. Cloudy weather results in faster watering and longer drying. With the rbf graph, it can be concluded that the higher the temperature, the more watering is required and will result in high soil moisture, while if the temperature de-creases, the soil will dry out.

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