PENERAPAN ALGORITMA NAÏVE BAYES CLASSIFIER UNTUK KLASIFIKASI PENJUALAN OBAT DI APOTEK NUGRAHA

Abstract

This study discusses the application of the Naive Bayes Classifier algorithm in analyzing drug sales transaction data at Apotek Nugraha. The purpose is to assist stock management and decision-making related to drug availability based on consumer purchasing patterns. The data used consists of sales transaction records, which are then processed and classified to predict drug demand trends. The Naive Bayes Classifier method was chosen for its ability to perform classification quickly, simply, and with relatively good accuracy even when using limited datasets. The results indicate that this algorithm can identify frequently purchased drug categories and provide predictions that support efficient inventory management. Thus, the implementation of data mining using Naive Bayes Classifier can improve the effectiveness of pharmacy services while minimizing the risks of both stock shortages and overstocking.