Perbandingan Metode Klasifikasi dalam Memprediksi Penjualan Produk Ban Terlaris - moch anjas aprihartha; Fitri Astutik; Nani Sulistianingsih (Jurnal Matematika, Statistika dan Komputasi, 2024)
Data mining is a term to describe the process of moving through large databases in search of certain previously unknown patterns. In finding certain patterns, you need a supporting technique, called machine learning. Machine learning involves learning hidden patterns in data and further using patterns to classify or predict an event related to a problem. One of the problems can be solved with machine learning such as predicting the sales rate of tire products. This can help companies predict tire products that are selling well in the market. In producing an accurate prediction model, it will be compared with decision tree classification methods of CART, CART + Discrete Adaboost, and Naive Bayes applied to tire sales data by PT. Mitra Mekar Mandiri. The results of the study based on successive model performance evaluations are model Naive Bayes < model CART < model CART+Discrete Adaboost. The Discrete Adaboost model with a data proportion of 90:10 is the best model for predicting tire sales. The accuracy, sensitivity and specificity values for the model were 79.17%; 89.47%; and 68.84%. The AUC value is 0.8 which indicates the model is good