Analysis of Royal Prima Hospital service with a comparison between the K-Means Algorithm method and K-Medoids Clustering

Christnatalis Christnatalis; Eric Claudyo; Lucky Lucky; Hery Kristover Manullang; Arus Iman Zebua

doi:10.55942/pssj.v3i8.229

Authors

Christnatalis Christnatalis Prima Indonesia University
Eric Claudyo Prima Indonesia University
Lucky Lucky Prima Indonesia University
Hery Kristover Manullang Prima Indonesia University
Arus Iman Zebua Prima Indonesia University

DOI:

https://doi.org/10.55942/pssj.v3i8.229

Keywords:

Hospital, Service, K-Means Algorithm, K-Medoids Clustering

Abstract

Hospitals are an integral component of the health system, aimed at providing comprehensive individual healthcare. This includes inpatient, outpatient, and emergency services. For hospitals to be competitive, it is imperative that they offer high-quality services, adhere to standards, and serve all demographics. Drawing data from 341 Hospital Service Quality Surveys, the development of hospitals allows patients to determine which institution best meets their care needs. One of the challenges is ensuring patient satisfaction, thus building trust in the provided services. Factors influencing the quality of health services include the availability of resources, service facilities during the insurance period, ease of accessing information, and timeliness of services. The Royal Prima Medan Hospital emphasizes the utmost importance of public safety in its services. The study aims to understand the influence of responsibility and reliability on insurance company safety. This research is a descriptive analytical study conducted at the Royal Prima Medan Hospital, located at Puskesmas Padang Bulan. Data collection methods ranged from observation, questionnaires, interviews, documentation to literature review. The analysis compared the results of the K-Means and K-Methoids Clustering algorithms, with the K-Means method yielding five clusters and a satisfaction score of 2.701288, while the K-Methoids method resulted in two clusters with a satisfaction score of 2.71. The comparative analysis revealed that the K-Methoids Clustering method produces fewer clusters than the K-Means method.

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