Analysis of Slow Moving Goods Classification Technique: Random Forest and Naïve Bayes

Deny Jollyta, Gusrianty Gusrianty, Darmanta Sukrianto

DOI: https://doi.org/10.23917/khif.v5i2.8263

Abstract

Classifications techniques in data mining are useful for grouping data based on the related criteria and history. Categorization of goods into slow moving group or the other is important because it affects the policy of the selling. Various classification algorithms are available to predict labels or class labels of data. Two of them are Random Forest and Naïve Bayes. Both algorithms have the ability to describe predictions in detail through indicators of accuracy, precision, and recall. This study aims to compare the performance of the two algorithms, which uses testing data of snacks with labels for package type, size, flavor and categories. The study attempts to analyze data patterns and decides whether or not the goods fall into the slow moving category. Our research shows that Random Forest algorithm predicts well with accuracy of 87.33%, precision of 85.82% and recall of 100%. The aforementioned algorithm performs better than Naïve Bayes algorithm which attains accuracy of 84.67%, precision of 88.33% and recall of 92.17%. Furthermore, Random Forest algorithm attains AUC value of 0.975 which is slightly higher than that attained by Naïve Bayes at 0.936. Random Forest algorithm is considered better based on the value of the metrics, which is reasonable because the algorithm does not produce bias and is very stable.

Keywords

slow moving; random forest; naïve bayes

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