Analysis of Patient Medical Records Using the K-Means Clustering Algorithm Based on Visit Time as a Service Strategy Approach

Authors

  • Eva Mufida Padilla Universitas Pembangunan Panca Budi
  • Aidul Safii Universitas Pembangunan Panca Budi
  • Indra Nasution Universitas Pembangunan Panca Budi
  • Maisya Fitri Anugrah Universitas Pembangunan Panca Budi
  • Nurlina Sari Harahap Universitas Pembangunan Panca Budi
  • Muhammad Syahputra Novelan Universitas Pembangunan Panca Budi

Keywords:

Medical Records, Patients, Visit Time, K-Means Clustering Algorithm

Abstract

This study develops an analytical framework to optimize service strategies in primary healthcare, focusing on midwifery clinics that use electronic medical records. It employs the K-Means clustering algorithm to segment patients by visit time, diagnosis, and demographic characteristics, addressing the limitations of intuition-based decision-making for fluctuating patient volumes and resource needs. The clustering results provide an objective basis for designing adaptive interventions in staffing schedules, queue management, and pharmaceutical inventory, with the overall aim of improving patient satisfaction and operational efficiency

Downloads

Download data is not yet available.

References

Alasi, T. S. (2024). Ilmu Komputer (M. Ihsan (ed.); 1st ed.). https://www.media-publikasi-idpress.my.id/2023/12/ilmu-komputer.html

Asminanda, C. R., Nisa, W., Idris, I., Sari, R. A., & Khair, R. (2025). Optimization of hospital patient management through a combined RFM and fuzzy C-Means approach: enhancing service efficiency and quality. Journal of Science and Education (JSE), 5(2), 458–466.

Grant, R. W., McCloskey, J., Hatfield, M., Uratsu, C., Ralston, J. D., Bayliss, E., & Kennedy, C. J. (2020). Use of latent class analysis and k-means clustering to identify complex patient profiles. JAMA Network Open, 3(12), e2029068--e2029068.

Haraty, R. A., Dimishkieh, M., & Masud, M. (2015). An enhanced k-means clustering algorithm for pattern discovery in healthcare data. International Journal of Distributed Sensor Networks, 11(6), 615740.

Hidayah, A. H., Novitasari, D., Kamila, R., Nasrudin, M., & others. (2025). Cluster Modeling with K-Means on Provincial Data in Indonesia Based on Environmental Indicators. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 4(3), 2245–2249.

Hillerman, T. P., Carvalho, R. N., & Reis, A. C. B. (2015). Analyzing suspicious medical visit claims from individual healthcare service providers using k-means clustering. International Conference on Electronic Government and the Information Systems Perspective, 191–205.

Lai, H., Huang, T., Lu, B., Zhang, S., & Xiaog, R. (2025). Silhouette coefficient-based weighting k-means algorithm. Neural Computing and Applications, 37(5), 3061–3075.

Mutiara S. Simanjuntak, Nurafni Damanik and Allwine (2022) “Performance Analysis Of Support Vector Machine In Identifying Comments And Ratings On E-Commerce”, International Journal of Basic and Applied Science, 11(1), pp. 37–46. doi: 10.35335/ijobas.v11i1.79.

Nofriansyah, D. (2015). Konsep Data Mining Vs Sistem Pendukung Keputusan.

Novaliendry, D., Wibowo, T., Ardi, N., Evi, T., & Admojo, D. (2023). Optimizing Patient Medical Records Grouping through Data Mining and K-Means Clustering Algorithm: A Case Study at RSUD Mohammad Natsir Solok. International Journal of Online & Biomedical Engineering, 19(12).

P, M., Allwine and Tarigan, I. J. (2023) “Sistem Penunjang Keputusan Seleksi Calon Karyawan Menggunakan Metode SAW (Simple Additive Weighting) Pada PT. Delta Kristalis”, JURNAL MAHAJANA INFORMASI, 7(2), pp. 159–165. doi: 10.51544/jurnalmi.v7i2.3600.

Prayogi, S. Y., Alasi, T. S., & Rahmat, R. F. (2025). Pengantar Machine Learning (M. Ihsan (ed.); 1st ed.). Media Publikasi Idpress. https://www.media-publikasi-idpress.my.id/2025/03/4.html

Ramadhan, G., & Astuti, Y. (2024). Perbandingan Kinerja Algoritma K-means dan Agglomerative Clustering Untuk Segmentasi Penjualan Online Pada Customer Retail. Jurnal Informatika: Jurnal Pengembangan IT, 9(1), 92–96.

Ravuvar, H. N., Goda, H., Chinnasamy, P., & others. (2020). Smart health predicting SystemUsing K-means algorithm. 2020 International Conference on Computer Communication and Informatics (ICCCI), 1–4.

Sembiring, A. S., Alasi, T. S., & others. (2023). Penyedia Layanan Konsultasi Kesehatan dengan Metode TOPSIS. Jurnal Armada Informatika, 7(1), 274–280.

Yahya, A., & Kurniawan, R. (2025). Implementasi Algoritma K-Means untuk Pengelompokan Data Penjualan Berdasarkan Pola Penjualan: Implementation of K-Means Algorithm for Clustering Sales Data Based on Sales Patterns. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(1), 350–358.

Published

2025-01-12

How to Cite

Mufida Padilla, E., Safii, A. ., Nasution, I., Fitri Anugrah, M., Sari Harahap, N., & Syahputra Novelan, M. (2025). Analysis of Patient Medical Records Using the K-Means Clustering Algorithm Based on Visit Time as a Service Strategy Approach . Jurnal Bisantara Informatika, 9(2), 1-10. Retrieved from https://bisantara.amikparbinanusantara.ac.id/index.php/bisantara/article/view/161

Issue

Vol. 9 No. 2 (2025): Jurnal Bisantara Informatika (JBI) Vol. 9 No. 2 Tahun 2025

Section

Articles

Copyright (c) 2025 Jurnal Bisantara Informatika

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.