Implementasi Data Mining Dalam Mengelompokkan Tingkat Kepuasan Pemakaian Jasa Cleaning Service Dengan Menggunakan Algoritma K-Means Clustering - Nadya Septiani Nadya; Sri Wahyuni (Forum Kerjasama Pendidikan Tinggi (FKPT), 2024)
Pinang Jaya Abadi Indonesia is a company providing cleaning services to various sectors, including hospitals, commercial businesses, offices, and shopping centers. However, problems arise when complaints regarding the quality of service provided by its employees occur. To improve service quality and assess customer satisfaction with the offered services, a system capable of accurately and efficiently clustering customer satisfaction data is needed. As a solution, this study applies the K-Means Clustering algorithm in the field of Data Mining to cluster customer satisfaction data regarding the cleaning services provided by PT. Pinang Jaya Abadi Indonesia. The K-Means algorithm was chosen for its ability to cluster data quickly and effectively, and its proven efficiency in various data clustering cases. By using this algorithm, the study aims to produce more structured and informative data clusters, providing a clearer understanding of customer satisfaction levels. The results of this study show that the system designed using the K-Means Clustering algorithm can effectively cluster customer satisfaction data, yielding efficient and accurate results. This system can serve as a tool for PT. Pinang Jaya Abadi Indonesia to enhance service quality and minimize customer complaints by focusing more on clusters with low satisfaction levels.