PENERAPAN ALGORITMA K-MEANS UNTUK KLASTERISASI DATA OBAT PASIEN RAWAT JALAN BERDASARKAN 3 PENYAKIT TERBANYAK DI RUMAH SAKIT M.NATSIR SOLOK

Hendra Nusa Putra, Ade Wisandra, Fransiska Fransiska

Sari


Abstract: The problem at the M. Natsir Solok Hospital is that the officers cannot see many drugs used by the patient, but can only see what drugs the patient has received, so researchers will research so that officers can see what drugs are used by many of them. these 3 diseases. The purpose of this study was to determine the application of drug data clustering based on the 3 most common diseases using the k-means algorithm. This type of research uses descriptive quantitative data. The population of medical record data taken is 1 month, namely in January 2020 as many as 366 medical record data, and the sample is total sampling where all the population is sampled as much as 366 medical record data. The type of data used is secondary data, data collection by observation, and Data analysis using yahoo k-means. The results of the study obtained were the determined clusters of 3 clusters. Among them are Clusters of Low Drug Use, Medium Drug Use, and High Drug Use. Low drug use in cluster A with low drug data use there are 5 types of drugs with a percentage (6%), cluster B high drug data use, there are 74 types of drugs with a percentage (86%), and cluster C moderate drug use data, there are 7 types of drugs with a presentation (8%). It is hoped that the M.Natsir Solok Hospital can apply classification in processing data based on the most diseases so that the hospital can classify types of drugs based on the lowest level of use to the highest level of officers so that they can provide drugs before the drug stock is used up, and can assist officers in reporting SP2TP in M. Natsir Hospital Solok

Keywords : Clustering, AlgorithmK-meaning, Disease, Drug, WEKA.


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Referensi


Ardhyanti, J., Nugraha, M., Kusumawati, Y., Informasi, S., Komputer, F. I., & Nuswantoro, U. D. (2014). Data Mining Dengan Metode Clustering Untuk Pengolahan Informasi Persediaan Obat Pada Puskesmas Pandanaran Semarang. UDiNus Repository, 1(1), 1.

Handiwidjojo, W. (2009). Rekam medis elektronik.

Putri, D. L., & Santoso. (2016). Implementasi Algoritma K-Means Untuk Pengelompokan Penyakit Pasien ( Studi Kasus : Puskesmas Kajen ) K-Means Algorithm Implementation for Classification of Disease Patient ( Case Study : Health Centers Kajen Regency Pekalongan )

Penelitian, L., Hasil, P., Ensiklopedia, P., & Padang, D. L. (2021). Klasterisasi Data Rekam Medis Pada Diagnosa Penyakit Berdasarkan Usia Pasien Menggunakan Algoritma K-Means Di Puskesmas Lubuk Alung Hendra Nusa Putra 1 , Dinda Putri Anisa 2 Rekam Medis STIKES. 3(5), 128–133.

Rahmayani, M. T. I. (2018). Analisis Clustering Tingkat Keparahan Penyakit Pasien Menggunakan Algoritma K-Means. Jurnal Inovasi Teknik Informatika, 1(2), 40–44.

Taslim, T., & Fajrizal, F. (2016). Penerapan algorithma k-mean untuk clustering data obat pada puskesmas rumbai. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 7(2), 108–114. https://doi.org/10.31849/digitalzone.v7i2.602

Wandana, J., & Defit, Sarjon, S. (2020). Klasterisasi Data Rekam Medis Pasien Pengguna Layanan BPJS Kesehatan Menggunakan Metode K-Means. 2, 4–9

Wardani, N. W., Murni, N. N., Luka, S. S. P., & G.Indrawan. (2016). Analisis Penerapan K-means Untuk Pengelompokkan Diagnosa Penyakit Kulit dan Kelamin Berdasarkan Rentang Usia. Senapati, Senapati.




DOI: https://doi.org/10.33559/eoj.v4i3.209

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