Penerapan Algoritma K-Means untuk Pemetaan Kebutuhan Obat Sesuai Prioritas dalam Pengadaan

Laili Nur Azizah, Lis Utari, Leny Tritanto Ningrum, Dwi Rahmiyati

Abstract


ABSTRACT

Each Ministry/Institution that has a primary clinic has its own budget allocation to support the operational activities of the primary clinic health service. The budget is usually adjusted to the size of the organization and the needs of its employees. One of the budgets is for spending on medical supplies. It is expected that the Primary Clinic, which is within the Ministries or Institutions, can provide health services according to the standards and needs of the scope they serve in accordance with budget availability. For this reason, so that the available budget can be optimized according to existing needs and is right on target, it is necessary to have a careful drug procurement plan. Segmentation/mapping of drug use is said to be important to determine the priority level of drug needs and can be used as a reference in drug procurement. The priority scale of needs in drug procurement is a process for mapping the types of drugs based on their needs by considering the drugs needed and the available budget, to avoid buying drugs that are not needed by the employees. Ideally, the types of drugs with high use are groups of drugs which procurement needs to be prioritized with the aim of ensuring the availability of these drugs. This study uses drug use data for 1 (one) semester, namely January to June 2022, using total usage and number of transactions using the clustering method with the K-Means algorithm. The results of drug mapping resulted in 197 drugs with low priority, 13 drugs with medium priority and 3 drugs with high priority. The results of the clustering results evaluation test show the value of the silhouette coefficient results is 0.8630 or "strong structure

Keywords


Clustering , Data Mining, Drug Grouping, Segmentation

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References


D. Waluyo, Ilmu Pengetahuan Sosial. Jakarta: Pusat Perbukuan Departemen Pendidikan Nasional, 2008.

J. Han, Data Mining Concepts and Techniques, Fourth. United States: Elsevier, 2011.

B. Santoso, Data Mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis, 1st ed. Yogyakarta: Graha Ilmu, 2007.

E. T. Kusrini, Kusrini., Luthfi, Algoritma Data Mining, 1st ed. Yogyakarta: Andi Offset, 2009.

L. T. Ningrum and D. Rahmiyati, “Analysis Sentiment of Twitter User on Indonesia’s 2024 Presidential Election Using K-Means Algorithm,” J. Sisfotek Glob., vol. 13, no. 2, p. 102, 2023, doi: 10.38101/sisfotek.v13i2.9609.

L. T. Ningrum, I. Irmayansyah, and L. Utari, “Penerapan Metode K-Means dan Euclidean Distance Untuk Seleksi Metode Judul Tugas Akhir,” Acad. J. Comput. Sci. Res., vol. 6, no. 1, p. 13, 2024, doi: 10.38101/ajcsr.v6i1.10766.

M. S. Yang and K. P. Sinaga, “A feature-reduction multi-view k-means clustering algorithm,” IEEE Access, vol. 7, pp. 114472–114486, 2019, doi: 10.1109/ACCESS.2019.2934179.

L. U. Zogara, A. Sururi, and L. T. Ningrum, “Analysis of COVID-19 Information Based on Social Media Big Data Classification Using the K-Means Data Mining Method,” J. Sisfotek Glob., vol. 12, no. 1, p. 18, 2022, doi: 10.38101/sisfotek.v12i1.448.

C. S. D. B. Sembiring, L. Hanum, and S. P. Tamba, “Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Menentukan Judul Skripsi Dan Jurnal Penelitian (Studi Kasus Ftik Unpri),” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 5, no. 2, pp. 80–85, 2022, doi: 10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2393.

D. A. I. C. Dewi and D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,” Matrix J. Manaj. Teknol. dan Inform., vol. 9, no. 3, pp. 102–109, 2019, doi: 10.31940/matrix.v9i3.1662.

Y. Sopyan, A. D. Lesmana, and C. Juliane, “Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1464–1470, 2022, doi: 10.47065/bits.v4i3.2697.

S. Suraya, M. Sholeh, and D. Andayati, “Penerapan Metode Clustering Dengan Algoritma K-Means Pada Pengelompokan Indeks Prestasi Akademik Mahasiswa,” Skanika, vol. 6, no. 1, pp. 51–60, 2023, doi: 10.36080/skanika.v6i1.2982.

E. Irwansyah and M. Faisal, Advanced Clustering: Teori dan Aplikasi. Yogyakarta: Deepublish, 2015.

S. Paembonan and H. Abduh, “Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat,” PENA Tek. J. Ilm. Ilmu-Ilmu Tek., vol. 6, no. 2, p. 48, 2021, doi: 10.51557/pt_jiit.v6i2.659.

H. Sa’diah, U. Enri, and T. Nur Padilah, “Penerapan Algoritme K-Means Dalam Segmentasi Daerah Rawan Kekerasan Anak Di Jawa Barat,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1351–1357, 2023, doi: 10.36040/jati.v7i2.6838.




DOI: https://doi.org/10.31284/j.kernel.2025.v6i1.7943

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Copyright (c) 2025 Laili Nur Azizah, Laili Nur Azizah, Lis Utari, Lis Utari, Leny Tritanto Ningrum, Dwi Rahmiyati, Dwi Rahmiyati

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