Prediction of Rice Farming Yields in Padangsidimpuan City through Support Vector Machine (SVM) Algorithms

Abstract

The purpose of this study is to determine the prediction of rice farming yields in Padangsidimpuan City through SVM (Support Vector Machine) Algorithms. This type of research used quantitative methods of SVM (Support Vector Machine) with a Data-Driven development (DDD) method. This approach utilized patterns and trends in data to build accurate prediction models where the DDD method can be used when researchers have access to relevant and meaningful data to guide the development of software or prediction models.The SVM algorithm has proven to be effective in predicting rice yield trends, both in determining the direction of change (up or down) and in estimating the value of the next harvest. The implemented SVM model is able to identify patterns of change in historical data and provide relevant predictions for agricultural yields. Historical data covering a fairly long period of time provides sufficient information for models to identify trends and patterns. This model can provide better predictions with more complete and high-quality data.