Shallot Production Prediction System Using the C.45 Decision Tree Algorithm

This research applies the C4.5 algorithm, which is a machine learning algorithm for classification using decision trees, in a case study for predicting the performance of shallot production. The data used includes attributes such as production yield, land area, and productivity. The C4.5 Decision Tree algorithm is utilized to build an accurate prediction model after going through data cleaning and training processes. This study results in an application that can perform the entire process of initial data processing to data analysis using the aforementioned technique, making it efficient and effective in analyzing large amounts of data to obtain optimal prediction results.