Sistem Prediksi Hasil Produksi Jagung Menggunakan Algoritma Decision Tree C.45 - Aghnie Kurnia; Wina Witanti; Agus Komarudin (CV. ITTC INDONESIA, 2024)
This research aims to develop a corn production prediction system using Decision Tree C4.5 algorithm. The system is designed to help farmers and stakeholders make better decisions regarding corn production. The data used was obtained from Kaggle, consisting of 4,121 data from various countries. The system development process involved several stages, including data preprocessing, normalization, and division of the dataset into training data (80%) and test data (20%). The C4.5 Decision Tree algorithm was chosen due to its ability to handle both continuous and categorical attributes, as well as produce an easy-to-understand decision tree. The results showed that the developed system was able to predict corn production with an accuracy rate of 92.82%. With this system, it is expected that farmers can conduct more effective and efficient production planning, and reduce the risk of losses due to inaccurate predictions.