Analysis of Public Service Satisfaction using Artificial Intelligence K-Means Cluster
DOI:
https://doi.org/10.55208/fmwvf958Keywords:
Public Services, IKM, Artificial Intelligence, K-MeansAbstract
Public service refers to the provision of goods, services, and support by the government to meet the community's desires and needs. In order to assess the efficacy of this service, a metric for gauging service quality, referred to as the Community Satisfaction Index, has been devised. This data offers insights into the level of satisfaction within the community regarding a particular service. This study utilizes the K-Means Cluster algorithm, a form of unsupervised machine learning, to categorize data based on similarities and dissimilarities into distinct clusters.
The objective of this study is to gain insight and conduct an analysis of the level of satisfaction within the community regarding the information services offered by the Communication and Information Department of West Java Province. Furthermore, the objective of this study is to ascertain the categorization of the public satisfaction index by using the K-Means Cluster technique, employing an artificial intelligence methodology. This approach will enable the identification of the public satisfaction index as well as the identification of specific indicators that necessitate enhancement.
The initial step in examining the public satisfaction index through the utilization of Artificial Intelligence involves the application of the K-Means Cluster algorithm, which will generate multiple clusters based on their shared characteristics. The values utilized by each group consist of the integers 1, 2, 3, and 4. Subsequently, an assessment is conducted on each formed group in order to ascertain the most favorable outcomes. The study yielded clusters that were deemed optimal, with smaller values indicating areas in which the services could be enhanced.
The present study aims to investigate the impact of Artificial Intelligence (AI) on public service quality, as measured by the Community Satisfaction Index (CSI). Specifically, we employ the K-Means clustering algorithm to analyze the data collected from a representative sample of community members. By utilizing AI techniques, we seek to gain insights into.
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Copyright (c) 2023 Fadli Emsa Zamani, Toni Kusnandar, Fikri Emsa Silmi, Rizal Rachman (Author)
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