Web-Based Inventory Application and Prediction Using Naive Bayes Algorithm (Case Study: Puskesmas Kecamatan Sawah Besar)

Authors

  • Awalia Nurfaida Mercu Buana University, Indonesia
  • Riska Yulianty Ali Syabana Mercu Buana University, Indonesia
  • Triseti Abimanyu Mercu Buana University, Indonesia
  • Nurullah Husufa Mercu Buana University, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v4i4.348

Keywords:

Drug Stock, Bootstrap Framework, Stock Prediction, PHP-ML, Naive Bayes

Abstract

The development of information systems and technology affects various fields, including health, namely Puskesmas. The Sawah Besar District Health Center has several work units, one of which is a stock management unit. The stock management unit in carrying out its duties has problems including data management that still uses Microsoft Excel. When using Microsoft Excel, information about stock data not integrated between different work units, thus hampering the process of accessing documents. The process of approval and stock prediction is also still done manually using physical documents. PHP programming language with Bootstrap Framework and MySQL used to develop applications. While PHP-ML library which implement Naive Bayes algorithm is used for stock prediction. The features developed in this study can be used by related units to simplify the stock data collection process, assist in the stock approval process and speed up the preparation of stock inventory reports. In addition, there is a stock prediction feature that can help users so to support while making decisions in the procurement of drug stock at the Sawah Besar District Health Center to determine whether certain drugs need to be added or not.

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Published

2022-11-14

Issue

Vol. 4 No. 4 (2022): Journal of Information Systems and Informatics

Section

Articles

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How to Cite

[1]
A. Nurfaida, R. Y. A. Syabana, T. Abimanyu, and N. Husufa, “Web-Based Inventory Application and Prediction Using Naive Bayes Algorithm (Case Study: Puskesmas Kecamatan Sawah Besar)”, journalisi, vol. 4, no. 4, pp. 864–878, Nov. 2022, doi: 10.51519/journalisi.v4i4.348.

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