Clustering Outpatient and Inpatient Medical Records Based on Patient Complaints using K-Means

Nery Adelia Br Purba; Magdalena Simanjuntak; Marto Sihombing

doi:10.59934/jaiea.v6i1.2818

Authors

Nery Adelia Br Purba STMIK Kaputama
Magdalena Simanjuntak STMIK KAPUTAMA
Marto Sihombing STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v6i1.2818

Keywords:

Clustering; K-means; Medical records; Patient complaints; Service type

Abstract

Medical record archives contain patterns that can support hospital service planning, but large datasets are difficult to analyze manually. This study developed a clustering application to group outpatient and inpatient medical records at Delia Hospital based on age, patient complaint, and type of service. A total of 600 medical records from 2025 were selected, cleaned, transformed into numerical codes, and processed in MATLAB using the K-means algorithm with squared Euclidean distance. Three scenarios were evaluated, namely K = 3, K = 4, and K = 5, using cluster variance and the distribution of members as comparison measures. The cluster variance decreased from 4.7356 at K = 3 to 2.8841 at K = 4 and 2.0662 at K = 5. The five-cluster solution produced 129, 95, 122, 127, and 127 members, respectively, with dominant complaints of asthma, vertigo, diabetes, fractures, and gastritis. The results indicate that K = 5 provides the most compact and detailed grouping. The resulting information can support the evaluation of outpatient services, inpatient capacity, and hospital resource planning.

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References

S. Apriliyani, “Penggunaan rekam medis elektronik guna menunjang efektivitas pendaftaran pasien rawat jalan di Klinik Dr. Ranny,” Cerdika: Jurnal Ilmiah Indonesia, vol. 1, no. 10, pp. 1399–1410, 2021, doi: 10.36418/cerdika.v1i10.209.

T. F. H. Farida and S. Setiatin, “Analisis lama waktu tunggu pelayanan pasien rawat jalan di bagian pendaftaran Rumah Sakit X Bandung,” Akrab Juara: Jurnal Ilmu-Ilmu Sosial, vol. 6, no. 5, pp. 119–126, 2021, doi: 10.58487/akrabjuara.v6i5.1692.

M. E. Putra, N. Desreza, and R. A. Atika, “Hubungan kualitas pelayanan terhadap kepuasan pasien rawat inap di Rumah Sakit Umum Teungku Fakinah Banda Aceh tahun 2025,” Future Academia: The Journal of Multidisciplinary Research on Scientific and Advanced, vol. 3, no. 4, pp. 1547–1558, 2025, doi: 10.61579/future.v3i4.575.

R. K. Barus, “Perlindungan hak pasien sebagai konsumen dalam rekam medis,” Jurnal Juristic, vol. 4, no. 2, p. 171, 2024.

M. F. Amalia and D. B. Arianto, “Implementasi algoritma K-means clustering dalam klasterisasi kabupaten/kota Provinsi Jawa Barat berdasarkan faktor pemicu stunting pada balita,” Simkom, vol. 9, no. 1, pp. 36–46, 2024, doi: 10.51717/simkom.v9i1.356.

E. Prasetyo, Data Mining: Konsep dan Aplikasi Menggunakan MATLAB. Yogyakarta, Indonesia: Andi, 2012.

Kusrini and E. T. Luthfi, Algoritma Data Mining. Yogyakarta, Indonesia: Andi, 2009.

F. Marisa, “Educational data mining: Konsep dan penerapan,” Jurnal Teknologi Informasi, vol. 4, no. 2, pp. 91–93, 2013.

F. H. Nasution, R. Kurniawan, and R. A. Putri, “Sistem informasi rekam medis pada puskesmas dengan menerapkan algoritma K-means berbasis web,” Jurnal Ilmiah Sains dan Teknologi, vol. 9, no. 2, 2025.

S. Monika, “Pengelompokan pelayanan pasien rawat inap dan pasien rawat jalan berdasarkan jenis penyakit pasien menggunakan metode clustering (Studi Kasus: Rumah Sakit Delia),” in Prosiding Seminar Nasional Informatika (SENATIKA), 2021, pp. 144–153.

P. R. S. Br. Ginting, R. Buaton, and S. Ramadani, “Pengelompokkan pasien BPJS dan pasien umum berdasarkan pelayanan rumah sakit dengan menggunakan metode clustering (Studi Kasus: Rumah Sakit Delia),” Jurnal Ilmiah Kaputama (JIKA), vol. 6, no. 2, pp. 89–98, 2022.

D. N. Br. Purba, M. Sihombing, and I. Ambarita, “Pengelompokan data rekam medis pada pasien penyakit dalam untuk meningkatkan manajemen informasi kesehatan berdasarkan wilayah Kota Binjai menggunakan algoritma clustering K-means (Studi Kasus: Artha Medika Binjai),” Jurnal Penelitian Teknologi Informasi dan Sains, vol. 2, no. 3, pp. 103–117, 2024, doi: 10.54066/jptis.v2i3.2378.

G. Alasi and R. A. Putri, “Penerapan K-means clustering untuk pengelompokan pasien rumah sakit berdasarkan tingkat keparahan penyakit,” Jurnal Multimedia dan Teknologi Informasi (Jatilima), vol. 7, no. 3, pp. 475–486, 2025, doi: 10.54209/jatilima.v7i03.1598.