Penerapan Metode K-Means untuk Pengelompokan Data Obat Berdasarkan Tingkat Kebutuhan

Zikri Alhadi Adha; Hidayati Rusnedy; Emon Azriadi; Kasini; R. Joko Musridho

doi:10.55681/sentri.v5i8.7141

Authors

Zikri Alhadi Adha Universitas Pahlawan Tuanku Tambusai
Hidayati Rusnedy Universitas Pahlawan Tuanku Tambusai
Emon Azriadi Universitas Pahlawan Tuanku Tambusai
Kasini Universitas Pahlawan Tuanku Tambusai
R. Joko Musridho Universitas Pahlawan Tuanku Tambusai

DOI:

https://doi.org/10.55681/sentri.v5i8.7141

Keywords:

Penambangan Data, K-Means, Pengelompokan, Permintaan Obat, Pengadaan Obat

Abstract

Abstract: This study aims to analyze and classify the level of drugdemand and provide drug procurement recommendations at UPT Puskesmas Air Tiris using the K-Means Clustering method. The main problem identified is that the drug demand analysisprocess is still conducted through data recapitulation, making it difficult to provide optimal information regarding drug demandlevels. The data used in this study were obtained from the DrugUsage and Request Report (LPLPO) for the period from January 2025 to January 2026, consisting of initial stock, receipts, inventory, usage, and remaining stock variables. The research methodology applies the Knowledge Discovery in Database (KDD) process, including data selection, preprocessing, transformation, data mining, and evaluation. The clustering process uses three clusters representing high, medium, and low demand levels. The results show that 309 drugrecords were successfully grouped into three clusters, consistingof 4 drugs in Cluster 1, 27 drugs in Cluster 2, and 278 drugs in Cluster 3. The clustering results were subsequently used as a basis for procurement recommendations by considering the average monthly demand, remaining stock, and a safety stock of 10%. The results indicate that the implementation of K-Meanscan assist in identifying drug demand levels and provideprocurement recommendation information as a reference for improving drug inventory management at Puskesmas Air Tiris.

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References

D. Astuti and Muqorobin, “Optimasi Metode K-Means Clustering untuk Pengelompokan Obat Di Puskemas Mertoyudan I Magelang,” Sist. J. Sist. Inf., vol. 13, no. 5, pp. 2144–2160, 2024.

H. Rusnedy, G. W. Nurcahyo, and S. Sumijan, “Identifikasi Tingkat Pemakaian Obat Menggunakan Metode Fuzzy C-Means,” J. Inf. dan Teknol., vol. 3, pp. 196–201, 2021, doi: 10.37034/jidt.v3i4.152.

Z. Rahimi, N. Li, D. G. Down, and D. M. Arnold, “Clustering-based demand forecasting with an application to immunoglobulin products,” Oper. Res. Data Anal. Logist., vol. 45, p. 200469, 2025.

V. Khalil and S. Foo, “A lean approach for improving medicines management in Australia’s first cardiac hospital inventory system,” Int. J. Health Plann. Manage., vol. 39, no. 5, pp. 1642–1651, 2024.

L. Izzah and A. Jananto, “Penerapan Algoritma K-Means Clustering Untuk Perencanaan Kebutuhan Obat Di Klinik Citra Medika,” Progresif J. Ilm. Komput., vol. 18, no. 1, pp. 69–76, 2022.

S. Nurani, Y. Syahra, and A. Calam, “Penerapan Data Mining Dalam Clustering Pencapaian Target Penjualan Menggunakan Algoritma K-Means,” J. Sist. Inf. TGD, vol. 2, no. 3, pp. 355–363, 2023.

M. Ulfah, “Penerapan Data Mining Clustering Menggunakan Metode K-Means Dalam Pengelompokan Buku Perpustakaan Politeknik Negeri Balikpapan,” vol. 4, no. 3, pp. 62–68, 2022.

A. N. Shabrina, M. Afdal, and S. Monalisa, “Comparison Of K-Means, K-Medoids, and Fuzzy C-Means Algorithms for Clustering Drug User’s Addiction Levels,” J. Sist. Cerdas, vol. 6, no. 2, pp. 113–122, 2023.

R. A. Farissa, R. Mayasari, and Y. Umaidah, “Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient,” vol. 5, no. 2, pp. 109–116, 2021.

Y. S. Siregar, D. Handoko, M. Khairani, N. I. Syahputri, H. Harahap, and H. Artikel, “Implementasi Data Mining Klasifikasi Algoritma Chaid Dalam Menentukan Pola Penerima Mahasiswa Baru,” Digit. Transform. Technol. (Digitech)| e, vol. 3, no. 2, pp. 1–12, 2023.

M. Sholeh, D. Andayati, and R. Y. Rachmawati, “DATA MINING MODEL KLASIFIKASI MENGGUNAKAN K-NEAREST NEIGHBOR WITH NORMALIZATION FOR DIABETES PREDICTION,” J. TeIKa, vol. 12, no. 2, pp. 77–87, 2022.

P. P. Allorerung, A. Erna, M. Bagussahrir, and S. Alam, “Analisis Performa Normalisasi Data untuk Klasifikasi K-Nearest Neighbor pada Dataset Penyakit,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 9, no. 3, pp. 178–191, 2024.

H. Rusnedy, Kasini, L. S. Tanjung, and Y. Yusmita, “Application of TOPSIS and K-Means Clustering Methods in Recommendations and Analysis of Study Program Interest for New Students,” J. Eng. Sci. Technol. Manag., vol. 5, no. 1, pp. 131–136, 2025.

Kasini, H. Rusnedy, L. S. Tanjung, and N. Y. S. Munti, “Penerapan algoritma k-means untuk pengelompokan data mahasiswa baru Program Studi Teknik Informatika di Universitas Pahlawan Tuanku Tambusai,” JUTIN J. Tek. Ind. Terintegrasi, vol. 8, no. 1, pp. 1378–1387, 2025.