Classification of Drug Data Usage Using the K-Means Deep Algorithm to Minimize Drug Stock Shortages (Case Study: South Cikarang Community Health Center)
DOI:
https://doi.org/10.35870/ijsecs.v4i1.2366Keywords:
Drug Data, Products, Machine Learning, K -Means, ClusteringAbstract
Efficient utilization of medicines is essential for effective health service delivery, especially in community health centers. This research explores the application of the K-Means clustering algorithm to categorize drug usage data and minimize stock shortages. This research, conducted at the South Cikarang Community Health Center, analyzed drug use patterns to identify drugs with high and low demand. Through data collection, cleaning, and pre-processing, medication use data is converted into a format suitable for clustering analysis. The clustering method approach can be applied to analyze the level of drug use produced by utilizing data sets to record the process of drug data results. The K-Means algorithm model applied has results that show new insights, namely grouping usage levels based on 2 clusters; cluster 1 (C0) is a high potential category consisting of 3.4 data from the tested dataset, and cluster 2 (C1) is Low Potential. Consists of 7.2 tested data, right? Collaborative testing can also produce collaborative testing results that show an average figure of 0.545.
Downloads
References
Syaripudin, G. A., & Faizal, E. (2017). Implementasi Algoritma Apriori Dalam Menentukan Persediaan Obat. JIKO (Jurnal Informatika dan Komputer), 2(1), 10–14. https://doi.org/10.26798/Jiko.2017.V2i1.56.
Dacwanda, D. O., & Nataliani, Y. (2021). Implementasi k-Means Clustering untuk Analisis Nilai Akademik Siswa Berdasarkan Nilai Pengetahuan dan Keterampilan. Aiti, 18(2), 125-138. https://doi.org/10.24246/Aiti.V18i2.125-138.
Purnamayanti, A., Winantari, A. N., Parfati, N., Diana, I., Latifah, N., & Setyowati, T. (2016). Kesalahan Penggunaan Obat Ibu dan Balita Peserta Posyandu di Kecamatan Sukolilo, Surabaya. MPI (Media Pharmaceutica Indonesiana), 1(1), 35-44. https://doi.org/10.24123/Mpi.V1i1.51.
Musthafa, A., & Wibowo, A. (2020, July). Analisis Pola Penjualan Produk Vitamin Menggunakan Algoritma Apriori. In Prosiding Seminar Nasional Riset Information Science (SENARIS) (Vol. 2, pp. 62-74).
Saputra, R., & Sibarani, A. J. (2020). Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Pola Penjualan Obat. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 7(2), 262-276. https://doi.org/10.35957/Jatisi.V7i2.195.
Yanto, R., & Khoiriah, R. (2015). Implementasi Data Mining dengan Metode Algoritma Apriori dalam Menentukan Pola Pembelian Obat. Creative Information Technology Journal, 2(2), 102-113. https://doi.org/10.24076/Citec.2015v2i2.41.
Widi, G., Nofriansyah, D., & Dicky. (2015). Algoritma dan Pengujian Data Mining. CV Budi Utama.
Anderson, K. D. (2011). Appraisal learning networks: How university archivists learn to appraise through social interaction. University of California, Los Angeles.
Listriani, D., Setyaningrum, A. H., & Eka, F. (2016). Application of the Association Method Using the Apriori Algorithm in the Consumer Shopping Pattern Analysis Application (Case Study of the Gramedia Bintaro Bookstore). Journal of Informatics Engineering, 9(2), 120-127. https://doi.org/10.15408/Jti.V9i2.5602.
Djamaludin, I., & Nursikuwagus, A. (2017). Analisis pola pembelian konsumen pada transaksi penjualan menggunakan algoritma apriori. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 8(2), 671-678. https://doi.org/10.24176/Simet.V8i2.1566.
Hegland, M. (2007). The apriori algorithm–a tutorial. Mathematics and computation in imaging science and information processing, 209-262.
Syahril, M., Erwansyah, K., & Yetri, M. (2020). Penerapan Data Mining untuk menentukan pola penjualan peralatan sekolah pada brand wigglo dengan menggunakan algoritma apriori. Jurnal Teknologi Sistem Informasi Dan Sistem Komputer TGD, 3(1), 118-136. https://doi.org/10.53513/jsk.v3i1.202.
Sintia, S., Poningsih, P., Saragih, I. S., Wanto, A., & Damanik, I. S. (2019, September). Penerapan algoritma apriori dalam memprediksi hasil penjualan sparepart pc (studi kasus: toko sentra computer). In Prosiding Seminar Nasional Riset Information Science (SENARIS) (Vol. 1, pp. 910-917). https://doi.org/10.30645/Senaris.V1i0.99.
Mudakir, Turmudi Zy, A., & Sunge, A. S. (2023). Penerapan Data Mining Untuk Klasifikasi Pengangkatan Karyawan Menggunakan Algoritma K-Means. Jurnal Informatika Teknologi dan Sains (Jinteks), 5(3), 489-497. https://doi.org/10.51401/Jinteks.V5i3.3369.
Kristanto, B., Zy, A. T., & Fatchan, M. (2023). Analisis Penentuan Karyawan Tetap Dengan Algoritma K-Means Dan Davies Bouldin Index. Bulletin of Information Technology (BIT), 4(1), 112-120. https://doi.org/10.47065/Bit.V4i1.521.
Syaikhuddin, M. M., & Prihandoko, P. (2017). Penerapan Algoritma K-Means dan Cure Dalam Menganalisa Pola Perubahan Belanja Dari Retail ke E-Commerce. Energy: Jurnal Ilmiah Ilmu-Ilmu Teknik, 7(2), 44-49.
Downloads
Published
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
