Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Al'Adzkiya International of Computer Science and Information Technology Journal
Herdawani Afdilla
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analysis and Comparison of the Performance of K-Means Algorithm and X-Means Algorithm in Disease Type Clustering in Mitra Medika Hospital Herdawani Afdilla; Hasibuan, Wilda Rina
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.696

Abstract

The system used by the hospital is currently still manual in managing patient data and information. What happened at Mitra Medika Hospital is that it is difficult to provide medical needs related to the patient's illness, considering the many types of illnesses that provide many medical needs. Several inpatients have used BPJS facilities with various illnesses suffered by patients to undergo further examinations in order to recover from the illness they are suffering from. Mitra Medika Hospital only sees medical needs based on the illness suffered by the patient, but seeing the large amount of patient history data makes it very difficult for Mitra Medika Hospital to find out the group of illnesses that patients often experience. This study uses a quantitative approach which starts from a theoretical framework, expert ideas, or researchers' understanding based on their experience, then developed into problems and their solutions that are submitted to obtain justification (verification) or assessment in the form of empirical data support in the field. Here, a data mining pattern is applied where this data mining is a very large data mining (big data). Cluster 0: From 245 Men (Suffering Between Diseases 1-5) Cluster 1: From 255 Women (Suffering Between Diseases 6-10) By using the K-Means Algorithm and the X-Means Algorithm, clustering can be produced. By using the Disease History data, the K-Means Algorithm and the X-Means Algorithm methods can be applied to determine clusters. By using web programming, it can produce an Analysis and Comparison of the Performance of the K-Means Algorithm and the X-Means Algorithm in Clustering Types of Diseases at Mitra Medika Hospital.
Analysis and Comparison of the Performance of the K-Means Algorithm and the X-Means Algorithm in Clustering Disease Types in Mitra Medika Hospital Herdawani Afdilla
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 5, No 2 (2024)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v5i2.321

Abstract

The system currently used by the hospital is still manual in managing patient data and information. What happens at Mitra Medika Hospital is that it is difficult to provide medical needs related to the diseases experienced by patients, considering that there are many types of diseases, so they provide many medical needs. Several inpatients have used BPJS facilities for various diseases suffered by the patient to carry out further examinations so that they can recover from the disease they are suffering from. Mitra Medika Hospital only looks at medical needs based on the disease suffered by the patient, however, seeing the large amount of patient history data makes it very difficult for Mitra Medika Hospital to find out the groups of diseases that patients often experience. This research uses a quantitative approach which starts from a theoretical framework, expert ideas, and researchers' understanding based on their experience, then developed into problems and solutions that are proposed to obtain justification (verification) or assessment in the form of empirical data support in the field. Here we apply a data mining pattern where data mining is extracting very large data (big data). Cluster 0: Of 245 Men (Suffering from 1-5 Diseases) Cluster 1: Of 255 Women (Suffering from 6-10 Diseases) Using the K-Means Algorithm and X-Means Algorithm can produce clustering. By using disease history data, you can apply the K-Means Algorithm and X-Means Algorithm methods to determine clusters. By using web programming, we can produce an analysis and comparison of the performance of the K-Means algorithm and the X-Means algorithm in clustering disease types in hospitals. Medika Partners.
Co-Authors Hasibuan, Wilda Rina