Data Mining Analysis Using K-Means and Naive Bayes Methods for Drug Inventory Prediction

Filsha Rifi Maharani; Rini Sovia; Gunadi Widi Nurcahyo

doi:10.35134/komtekinfo.v13i3.708

Authors

Filsha Rifi Maharani Universitas Putra Indonesia YPTK Padang
Rini Sovia Universitas Putra Indonesia Yptk Padang
Gunadi Widi Nurcahyo Universitas Putra Indonesia Yptk Padang

DOI:

https://doi.org/10.35134/komtekinfo.v13i3.708

Keywords:

Drug inventory, Data Mining, K-Means, Naive Bayes, Prediction

Abstract

The management of drug inventory still encounters issues related to unstable stock fluctuations, such as drug shortages and overstocking. This condition has the potential to cause operational losses and degrade the quality of patient care. This study aims to analyze categorized drug inventory using the K-Means Clustering and Naive Bayes algorithms. The K-Means algorithm is employed to group drug data into three categories: low, sufficient, and excessive. The Naive Bayes algorithm is utilized to classify and predict the status of drug inventory requirements. The dataset was obtained from RSUD Tualang over the past two years, comprising 206 drug data points with variables including name, category, stock, price, demand, and expiration date. The test results indicate that the K-Means algorithm successfully grouped the data into three clusters, namely Low (14 items), Sufficient (125 items), and Excessive (67 items), while the Naive Bayes algorithm successfully classified the status of drug inventory requirements with an accuracy of 95.63%. The findings demonstrate that the combination of Data Mining utilizing K-Means and Naive Bayes is effective for drug inventory analysis at RSUD Tualang, thereby assisting management in identifying inventory status and supporting more accurate, efficient, and timely procurement decision-making compared to conventional methods previously employed.

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