The Prediction Of Product Sales Level Using K-Nearest Neighbor and Naive Bayes Algorithms (Case Study : PT Kotamas Bali)

Aris Setiawan Miha Djami; Nengah Widya Utami; A. A. Istri Ita Paramitha

doi:10.33480/pilar.v19i2.4420

Aris Setiawan Miha Djami ^(1*) Primakara University
Nengah Widya Utami ⁽²⁾ Primakara University
A. A. Istri Ita Paramitha ⁽³⁾ Primakara University

(*) Corresponding Author

DOI: https://doi.org/10.33480/pilar.v19i2.4420

Keywords: Data Mining, Product Sales Level, Knowledge Discovery in Databases, K-Nearest Neighbor, Naïve Bayes.

Abstract

PT Kotamas Bali is a company that operates in tableware and kitchenware, where every sale is sold at various counters. Product sales are permanently printed and entered in sales reports, and there are problems such as the fact that the product is sold very much and it is difficult to see the rate of sale of most products, lots, and not lots. Then, to see the level of product sales, it is necessary to use data mining techniques with the method of Knowledge Discovery in Databases to predict the purchase rate of products using the two algorithms K-Nearest Neighbor and Naïve Bayes. The purpose of this research is so that PT Kotamas Bali can see the sales rate of each product sold so that there is no accumulation of goods and more focus on the most marketed products. These two algorithms result in different accuracy on the 90:10 data split, where the K-Nearest Neighbor algorithm successfully predicted the sales rate of the product with a 99% accuracy rate and was categorized as an excellent classification. The Naïve Bayes algorithm failed to make predictions with an accuracy of only 54% and was classified as a failure classification. ROC performance results on the K-Nearest Neighbor algorithm with an AUC value of 99% and the Naïve Bayes algorithm with an AUC of 74%. K-Nearest Neighbor managed to obtain the highest accuracy, while the Naïve Bayes algorithm failed to conduct classification.

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