Application of Data Mining to Analyze BPJS Patient Satisfaction Levels Regarding the Service Attitude of PTPN II Tanjung Selamat General Hospital Using the K-Means Clustering Algorithm - Yohanni Syahra; Natasya Nabaceva (Ilmu Bersama Center, 2026)
The importance of the BPJS Kesehatan's role in providing affordable healthcare access to the community is undeniable. Rumah Sakit Umum PTPN II Tanjung Selamat collaborates with BPJS Kesehatan to deliver services to BPJS participants. However, patient satisfaction levels have been declining, with primary complaints including long registration processes, data recording errors, extended waiting times for doctor consultations, and inadequate hospital facilities. Understanding the factors affecting patient satisfaction is crucial for improving healthcare quality at this hospital. To address these issues, this study applies Data Mining methods, specifically the K-Means Clustering algorithm, to analyze BPJS patient satisfaction levels at Rumah Sakit Umum PTPN II Tanjung Selamat. K-Means Clustering is chosen for its ability to group data based on similar characteristics, allowing the identification of patterns influencing patient satisfaction. Utilizing a desktop application designed with Visual Basic 2010, this research provides flexibility in determining the number of clusters, aiding in more effective analysis and grouping of patient satisfaction data. The findings are expected to offer valuable insights for the management of Rumah Sakit Umum PTPN II Tanjung Selamat to enhance healthcare services for BPJS participants. By identifying patient groups based on their satisfaction levels, the hospital can take more targeted actions to improve unsatisfactory aspects. This study demonstrates that the application of Data Mining with the K-Means method can be an effective tool in evaluating and improving healthcare service quality.