Analysis Of Public Satisfaction Levels Towards Hospital  Services Using The K-Nearest Neighbors Method  (Case Study: XYZ Regional Public Hospital)

Novia  Ramadani Sibutar-Butar; Muhammad  Halmi Dar; Mila  Nirmala Sari Hasibuan

doi:10.46729/ijstm.v6i2.1284

Novia Ramadani Sibutar-Butar Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara Indonesia.
Muhammad Halmi Dar Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara Indonesia.
Mila Nirmala Sari Hasibuan Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara Indonesia.

DOI: https://doi.org/10.46729/ijstm.v6i2.1284

Keywords: Classification, Hospital, K-Nearest Neighbors, Performance Evaluation, Public Satisfaction.

Abstract

The level of public satisfaction with health services is an important indicator that reflects the quality of services provided by a hospital. XYZ Hospital, as one of the main health service providers, strives to continuously improve the quality of its services by understanding and evaluating the level of patient satisfaction. However, challenges arise when it comes to accurately identifying and predicting patient satisfaction, given the diverse characteristics of patients and the complexity of the services provided. Therefore, this study aims to analyze the level of public satisfaction with XYZ Hospital services using the K-Nearest Neighbors method. This study employs a quantitative approach by utilizing patient satisfaction data obtained through a survey. We then analyze the data using the K-Nearest Neighbors method, known for its effectiveness in classifying based on data proximity. We carry out the model performance evaluation process through an evaluation matrix that includes accuracy, precision, recall, and F1-score. The results of the study show that the K-Nearest Neighbors model is able to classify patient satisfaction with an accuracy value of 94%, precision of 97.67%, recall of 95.45%, and F1-Score of 96.55%. These results indicate that the K-Nearest Neighbors model is not only accurate in predicting patient satisfaction but also consistent in classifying patients who are satisfied and dissatisfied. The study's conclusion is that the K-nearest neighbors method is very effective in analyzing and predicting the level of patient satisfaction at XYZ Hospital. This study makes a significant contribution by utilizing the K-Nearest Neighbors model as a potent predictive tool for assessing patient satisfaction, a tool hospitals can employ to enhance service quality. We hope that further development will enable the larger-scale implementation of this model, thereby enhancing the quality of health services across various hospitals.

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