Klasifikasi Produktivitas Buah Nanas Menggunakan Algoritma Classification and Regression Tree (CART) - Aprihartha, Moch. Anjas; Putrawan, Zulhandi; Zulhan , Dicky; Ahardika Nurfaizal, Fatma (Diophantine Journal of Mathematics and Its Applications, 2024)
Indonesia is one of the countries that has a variety of fruits cultivated. One of them is the pineapple fruit. Various pineapple-based products such as pineapple juice, canned foods, pineapple jam, etc. The high demand for pineapples presents an opportunity for companies to increase pineapple product processing. The increase in pineapple productivity is influenced by several factors, one of which is the extent of land and the type of pineapple produced. To improve pineapple productivity, it can be done by classifying the types of pineapples based on productive and non-productive categories. The purpose of this classification is to enable farmers or plantation managers to allocate resources more efficiently by providing more intensive care for productive category pineapples. The classification method that can be used to classify productive and non-productive pineapples is the Classification and Regression Tree (CART) algorithm. The CART method is a method that produces decision tree models that are used to solve classification and regression problems. This research uses the CART method to classify pineapple productivity. The research results obtained accuracies, sensitivities, specificities, and precisions of 97.06%; 92.31%; 100%; 100% respectively. Meanwhile, the AUC obtained is 0.962 which indicates that the model is very good at predicting pineapple productivity correctly.