Comparison of Naïve Bayes, CART, dan CART Adaboost Methods in Predicting Tire Product Sales

moch anjas  aprihartha; Fitri  Astutik; Nani  Sulistianingsih

doi:10.20956/j.v20i3.33187

Authors

moch anjas aprihartha Program Studi PJJ Informatika, Fakultas Ilmu Komputer, Dian Nuswantoro University
Fitri Astutik Program Studi Sistem dan Teknologi Informasi, Fakultas Teknik, Universitas Muhammadiyah Mataram
Nani Sulistianingsih Program Studi Sistem dan Teknologi Informasi, Fakultas Teknik, Universitas Muhammadiyah Mataram

DOI:

https://doi.org/10.20956/j.v20i3.33187

Keywords:

CART, discrete adaboost, naive bayes

Abstract

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

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