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

  • Awalia Nurfaida Universitas Mercu Buana, Indonesia
  • Riska Yulianty Ali Syabana Universitas Mercu Buana, Indonesia
  • Triseti Abimanyu Universitas Mercu Buana, Indonesia
  • Nurullah Husufa Universitas Mercu Buana, Indonesia
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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References

A. Yani, “Utilization of Technology in the Health of Community Health,” Promot. J. Kesehat. Masy., vol. 8, no. 1, p. 97, 2018, doi: 10.31934/promotif.v8i1.235.

“Lampiran berita acara pemusnahan obat kadaluarsa / rusak puskesmas kecamatan sawah besar”.

2012 Bustami, D. I. Teknik, and N. Bayes, “‘Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah,’” J. Penelit. Tek. Inform. Univ. Malikussaleh, vol. 146, no. Klasifikasi, pp. 128–146, 2018.

A. Budiyanto and S. Dwiasnati, “The Prediction of Best-Selling Product Using Naïve Bayes Algorithm ( A Case Study at PT Putradabo Perkasa ),” Ijctjournal.Org, vol. 5, no. 6, pp. 68–74, 2018, [Online]. Available: http://www.ijctjournal.org/Volume5/Issue6/IJCT-V5I6P10.pdf

F. Rahutomo, I. Y. R. Pratiwi, and D. M. Ramadhani, “Eksperimen Naïve Bayes Pada Deteksi Berita Hoax Berbahasa Indonesia,” J. Penelit. Komun. Dan Opini Publik, vol. 23, no. 1, 2019, doi: 10.33299/jpkop.23.1.1805.

N. Oktaviani, I. M. Widiarta, and Nurlaily, “Sistem Informasi Inventaris Barang Berbasis Web Pada Smp Negeri 1 Buer,” J. Inform. Teknol. dan Sains, vol. 1, no. 2, pp. 160–168, 2019, doi: 10.51401/jinteks.v1i2.422.

Suparni and Hadiyansyah, “Sistem Informasi Monitoring Inventory IT Aset (SIMONAS)Berbasis Web Pada PT. Metrocom Global Solusi Jakarta,” Penelit. Tek. Inform., vol. 3, no. 1, p. e-ISSN : 2541-2019, p-ISSN : 2541-044X, 2018.

F. E. Prabowo and A. Kodar, “Analisis Prediksi Masa Studi Mahasiswa Menggunakan Algoritma Naïve Bayes,” J. Ilmu Tek. dan Komput., vol. 3, no. 2, p. 147, 2019, doi: 10.22441/jitkom.2020.v3.i2.008.

A. Rahmawati, D. Wintana, S. Suhada, G. Gunawan, and H. Sulaiman, “Klasifikasi Naïve Bayes Untuk Mendiagnosis Penyakit Pneumonia Pada Anak Balita (Studi Kasus : Uptd Puskesmas Sukaraja Sukabumi),” Klik - Kumpul. J. Ilmu Komput., vol. 6, no. 3, p. 241, 2019, doi: 10.20527/klik.v6i3.202.

H. Murnawan and Mustofa, “PERNECANAAN PRODUKTIVITAS KERJA DARI HASIL EVALUASI PRODUKTIVITAS DENGAN METODE FISHBONE DI PERUSAHAAN PERCETAKAN KEMASAN PT . X Latar belakang Masalah,” J. Tek. Ind. HEURISTIC, vol. 11, no. 1, pp. 27–46, 2014.

I. J. Dewanto, “Planning Planning Analysis Analysis Detailed Detailed System System Design Design Implementation Implementation Maintenance Maintenance,” Fasilkom, vol. 2, no. 1, 2004.

Y. Firmansyah and U. Udi, “Penerapan Metode SDLC Waterfall Dalam Pembuatan Sistem Informasi Akademik Berbasis Web Studi Kasus Pondok Pesantren Al-Habib Sholeh Kabupaten Kubu Raya, Kalimantan Barat,” J. Teknol. dan Manaj. Inform., vol. 4, no. 1, 2017, doi: 10.26905/jtmi.v4i1.1605.

U. Hanifah, R. Alit, and Sugiarto, “Penggunaan Metode Black Box Pada Pengujian Sistem Informasi Surat Keluar Masuk,” SCAN - J. Teknol. Inf. dan Komun., vol. 11, no. 2, pp. 33–40, 2016, [Online]. Available: http://ejournal.upnjatim.ac.id/index.php/scan/article/view/643

A. Alfiani Mahardhika, R. Saptono, and R. Anggrainingsih, “Sistem Klasifikasi Feedback Pelanggan Dan Rekomendasi Solusi Atas Keluhan Di UPT Puskom UNS Dengan Algoritma Naive Bayes Classifier Dan Cosine Similiarity,” J. Teknol. Inf. ITSmart, vol. 4, no. 1, p. 36, 2016, doi: 10.20961/its.v4i1.1806.

Published
2022-11-14
Abstract views: 289 times
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How to Cite
Nurfaida, A., Syabana, R. Y., Abimanyu, T., & Husufa, N. (2022). Web-Based Inventory Application and Prediction Using Naive Bayes Algorithm (Case Study: Puskesmas Kecamatan Sawah Besar). Journal of Information Systems and Informatics, 4(4), 864-878. https://doi.org/10.51519/journalisi.v4i4.348