Clustering Data Rekam Medis Untuk Penentuan Penyakit Endemi di Daerah Kabupaten Bengkulu Selatan Dengan Mengimplementasikan Metode Fuzzy C-Means (Studi kasus Puskesmas Kabupaten Bengkulu Selatan)

Prastio, Krisna and Andreswari, Desi and Rusdi, Efendi (2022) Clustering Data Rekam Medis Untuk Penentuan Penyakit Endemi di Daerah Kabupaten Bengkulu Selatan Dengan Mengimplementasikan Metode Fuzzy C-Means (Studi kasus Puskesmas Kabupaten Bengkulu Selatan). Undergraduated thesis, Universitas Bengkulu.

Text krisna.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons GNU GPL (Software). Download (3MB)

Abstract

ABSTRACT Medical record is a history of treatment of patients treated in hospitals or clinics. Medical language commonly used by doctors to diagnose and then act on a patient's illness. The process of recording medical records in the South Bengkulu area still uses the conventional method, namely by writing on paper so that the data in the medical record can only be used to view the patient's medical history. One way to group data is by clustering which is one of the data mining techniques, namely the clustering algorithm. Clustering application of medical record data for the determination of endemic diseases in the South Bengkulu Regency area was built by implementing the website-based Fuzzy C-Means method with a codeigniter framework. The amount of data used is 110 data consisting of 11 sub-district data consisting of the name of the sub-district, the area, population and 10 disease data, namely the name of the disease and the number of patients. The application can group sub-districts based on the potential for endemic diseases using the Fuzzy C-Means clustering method in tabular form. The use of the Fuzzy C-Means clustering method can produce data groups in 3 clusters but uses a random value as the initial value so that the calculation results can change slightly depending on the size of the data and the amount of data. Keyword : Medical record, Clustering, Fuzzy C-means

Actions (login required)

View Item