JUSS (Jurnal Sains dan Sistem Informasi)
Vol. 1 No. 1 (2018): Jurnal Sains dan Sistem Informasi

Penerapan Metode Association Rules dalam Menentukan Pola Penyakit Dan Usia Pasien Berdasarkan Data Rekam Medis

Abidin, Zainil (Unknown)
Mauladi, Mauladi (Unknown)
Weni, Indra (Unknown)



Article Info

Publish Date
06 Mar 2018

Abstract

The need for public health services at the Rumah Sakit Umum Raden Mattaher-Jambi increasingly bolder increase with population, erratic weather changes, and the pattern of life of Jambi. This will have an impact on increasing the number of patients that can affect the volume of service provision such as medical personnel, medicines, facilities and others. To cope with the impact, it is necessary the calculation/estimation of more specific by using the patient's medical record data at regular intervals. The patient's medical record data modeling is required to get an idea of the pattern of the disease patients. Research data obtained from the patient's medical record periods 2016-2017 throughout the year. This data consists of patients, physicians, diagnose and treatment given. The methods used for the processing of data is to use the method of Association rules and the a priori algorithms. In the data analysis technique applied to medical record Data Mining which consists of problem analysis, data preparation, Data Exploration, Pattern pattern Generation, Deployment, and Monitoring patern. All these measures helped to use Weka applications. The research results showed that the Association patterns of diseases suffered by patients can be known by applying the algorithm of data mining based assocition rule. The existence of this system be known patterns of diseases suffered by patients and treatment given. In addition, it can be known also the range of percentages for each of the cases of the disease occur.

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Journal Info

Abbrev

JUSS

Publisher

Subject

Description

JUSS covers a broad range of topics in Information Systems and Computer Science, including but not limited to the following areas: 01. Software Engineering 02. Decision Support Systems 03. Information Systems Security 04. Artificial Intelligence 05. Data Analytics and Visualization 06. Data Science ...