Optimization of the Naïve Bayes Classifier (NBC) Algorithm Using the Sparrow Search (SSA) Algorithm to Predict the Distribution of Goods Receipts

Rachma Oktari; Tjong Wan Sen

doi:10.24014/ijaidm.v4i2.15339

Optimization of the Naïve Bayes Classifier (NBC) Algorithm Using the Sparrow Search (SSA) Algorithm to Predict the Distribution of Goods Receipts

Rachma Oktari, Tjong Wan Sen

Abstract

Distribution must be able to meet all needs based on sales orders from consumers, be responsible for the delivery order process running optimally, and ensure the good receipt process is in accordance with consumer sales order requests. PT. Diamond Cold Storage currently uses Enterprise Resource Planning (ERP) to record all reports from production to sales. But in reality there are still some obstacles in the distribution section. In the good receipt process, several items were found that did not match the sales order, such as: the item did not match the order request or the item did not match the order request. The process of mismatching the good receipt with the sales order will be met with the completion of the good receipt process or the bad thing is that there is a cancellation, so this causes a loss for the company. This study uses data mining techniques with the Naïve Bayes Classifier algorithm to predict the distribution of goods receipts based on distribution data, and uses the Sparrow Search Algorithm (SSA) algorithm to optimize the Nave Bayes Classifier by selecting features to improve accuracy. In this study, the results obtained that the SSA algorithm can improve the performance of NBC from 95.05% to 97.95%.

Keywords

Data Mining Naïve Bayes Classifier (NBC) Sparrow Search Algorithm (SSA)

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DOI: http://dx.doi.org/10.24014/ijaidm.v4i2.15339

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