Comparison of Decision Tree and Random Forest Methods in Predicting Oil Palm Productivity After Replanting - Sukardi; Yuhandri; Sarjon Defit (Jurnal KomtekInfo, 2026)
Oil palm is a strategic commodity in Indonesia that can be affected by various factors such as plant age, soil conditions, rainfall, and maintenance variations between farmers. Over time, oil palm productivity decreases, so it is necessary to predict the productivity of oil palm rejuvenation. Based on this, the purpose of this study is to apply and compare the Decision Tree and Random Forest algorithms to predict the level of oil palm productivity after rejuvenation. The prediction process was carried out at the Koperasi Unit Desa (KUD) Tirta Kencana, Kuantan Singingi Regency. The Decision Tree algorithm is a supervised prediction model, meaning it requires a training dataset whose role replaces past human experience in making decisions. The Random Forest algorithm is also able to present several decision trees used in the prediction process. The dataset in this study amounted to 241 farmer data sourced from the KUD Tirta Kencana in Kuantan Singingi Regency. The comparative results of these two methods show that both the Decision Tree and Random Forest algorithms are capable of predicting precisely and accurately. The comparative results show that the random forest method outperforms the decision tree method with an accuracy of 99%. The contribution of this research provides knowledge with the application of data mining science by comparing the performance of the decision tree and random forest algorithms in the process of plant productivity management at KUD Tirta Kencana. Keywords: Oil Palm Productivity, Data Mining, Decision Tree, Random Forest, Productivity Prediction