Tingkat Efisiensi Penggunaan Resep Dokter Spesialis Menggunakan Metode K-Means Clustering
Main Article Content
Abstract
The National Formulary (Fornas) is a list of drugs stipulated in a Decree of the Minister of Health of the Republic of Indonesia, which is used as a guideline for hospitals in drug supply for participants of the National Health Insurance (JKN) program. Doctor's prescription is one indicator of the quality of hospital services. Prescribing drugs based on guidelines will provide efficiency in the supply of drugs. The purpose of this study was to facilitate controlling drug supplies, safe use of drugs and control costs and quality of treatment. K-Means Clustering is a method of grouping data into clusters using the K-Means algorithm. The data used in this study was a specialist doctor's prescription in December 2019 which was sourced from the Pharmacy department of the Meranti Islands District Hospital. The results of this research with the K-Means Clustering method consisted of 3 (three) clusters, namely cluster 0 obeying Fornas as many as 2 polyclinics, cluster 1 being less obedient to Fornas as many as 2 polyclinics and cluster 2 not obeying Fornas as many as 3 polyclinics. This research can be used as a reference and evaluation to hospital management on the efficiency level of using specialist doctor's prescriptions in improving the quality of hospital services.
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[2] Maulida, L. (2018). Penerapan Data Mining dalam Mengelompokkan Kunjungan Wisatawan Ke Objek Wisata Unggulan Di Prov. DKI Jakarta dengan K-Means. JISKA (Jurnal Informatika Sunan Kalijaga), 2(3), 167-174. DOI: http://doi.org/10.14421/jiska.2018.23-06 .
[3] Ali, A. (2019). Klasterisasi Data Rekam Medis Pasien Menggunakan Metode K-Means Clustering di Rumah Sakit Anwar Medika Balong Bendo Sidoarjo. MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 19(1), 186-195. DOI: http://doi.org/10.30812/matrik.v19i1.529 .
[4] Fatmawati, K., & Windarto, A. P. (2018). Data Mining: Penerapan Rapidminer dengan K-Means Cluster Pada Daerah Terjangkit Demam Berdarah Dengue (DBD) Berdasarkan Provinsi. Computer Engineering, Science and System Journal, 3(2), 173-178. DOI: http://doi.org/10.24114/cess.v3i2.9661 .
[5] Nalendra, A. K., Mujiono, M., Akhsani, R., & Utama, A. S. W. (2020). Implementasi Algoritma K-Mean dalam Pengelompokan Data Kecelakaan di Kabupaten Kediri. VOCATECH: Vocational Education and Technology Journal, 1(2), 53-60. DOI: http://doi.org/10.38038/vocatech.v1i2.28 .
[6] Oktarian, S., Defit, S., & Sumijan. (2020). Klasterisasi Penentuan Minat Siswa dalam Pemilihan Sekolah Menggunakan Metode Algoritma K-Means Clustering. Jurnal Informasi dan Teknologi, 2(3), 68-75. DOI: http://doi.org/10.37034/jidt.v2i3.65
[7] Aditya, A., Jovian, I., & Sari, B. N. (2020). Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama Di Indonesia Tahun 2018/2019. Jurnal Media Informatika Budidarma, 4(1), 51-58. DOI: http://doi.org/10.30865/mib.v4i1.1784 .
[8] Rosmini, R., Fadlil, A., & Sunardi, S. (2018). Implementasi Metode K-Means dalam Pemetaan Kelompok Mahasiswa Melalui Data Aktivitas Kuliah. It Journal Research And Development, 3(1), 22-31. DOI: http://doi.org/10.25299/itjrd.2018.vol3(1).1773 .
[9] Patel, E., & Kushwaha, D. S. (2020). Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model. Procedia Computer Science, 171, 158-167. DOI: http://doi.org/10.1016/j.procs.2020.04.017 .
[10] Franti, P., & Sieranoja, S. (2019). How Much Can K-Means Be Improved By Using Better Initialization and Repeats?. Pattern Recognition, 93, 95-112. DOI: http://doi.org/10.1016/j.patcog.2019.04.014 .
[11] Viloria, A., & Lezama, O. B. P. (2019). Improvements For Determining The Number of Clusters In K-Means For Innovation Databases In SMEs. Procedia Computer Science, 151, 1201-1206. DOI: http://doi.org/10.1016/j.procs.2019.04.172
[12] Benabdellah, A. C., Benghabrit, A., & Bouhaddou, I. (2019). A Survey of Clustering Algorithms For An Industrial Context. Procedia Computer Science, 148, 291-302. DOI: http://doi.org/10.1016/j.procs.2019.01.022 .
[13] Li, H., Zhang, N., Hai, M., & Zhang, Y. (2019). A General Feature Abstraction Method for Clustering Algorithm. Procedia Computer Science, 162, 438-443. DOI: http://doi.org/10.1016/j.procs.2019.12.008 .
[14] Parlina, I., Windarto, A. P., Wanto, A., & Lubis, M. R. (2018). Memanfaatkan Algoritma K-Means dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Center Untuk Clustering Program SDP. Computer Engineering, Science and System Journal, 3(1), 87-93. DOI: http://doi.org/10.24114/cess.v3i1.8192 .
[15] Gustientiedina, G., Adiya, M. H., & Desnelita, Y. (2019). Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan. Jurnal Nasional Teknologi dan Sistem Informasi, 5(1), 17-24. DOI: https://doi.org/10.25077/TEKNOSI.v5i1.2019.17-24
[16] Erlangga, N., Solikhun, S., & Irawan, I. (2019). Penerapan Data Mining dalam Mengelompokan Produksi Jagung Menurut Provinsi Menggunakan Algoritma K-Means. KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 3(1), 702-709. DOI: http://dx.doi.org/10.30865/komik.v3i1.1681 .
[17] Anjelita, M., Windarto, A. P., & Hartama, D. (2019). Pemanfaatan Data Mining Pada Pengelompokan Provinsi Terhadap Pencemaran Lingkungan Hidup. KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 3(1), 659-666. DOI: http://doi.org/10.30865/komik.v3i1.1675 .
[18] Silalahi, M. (2018). Analisis Clustering Menggunakan Algoritma K-Means Terhadap Penjualan Produk Pada PT Batamas Niaga Jaya. Computer Based Information System Journal, 6(2), 20-35. DOI: http://doi.org/10.33884/cbis.v6i2.709 .
[19] Indriyani, F., & Irfiani, E. (2019). Clustering Data Penjualan Pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means. JUITA : Jurnal Informatika, 7(2), 109-113. DOI: http://doi.org/10.30595/juita.v7i2.5529 .