Segment Risks Based on Age, Wages, Working Hours, and Claim Costs using K-Medoids Clustering
Main Article Content
Abstract
Occupational risk plays a crucial role in employment insurance management due to its direct impact on claim frequency and the sustainability of insurance systems. However, workers’ risk levels are heterogeneous and influenced by factors such as age, wages, working hours, and claim costs. This study aims to determine the optimal number of clusters and analyze worker risk segmentation using the K-Medoids Clustering method. Secondary data, obtained from Kaggle and consisting of 54,000 observations, were used. From this dataset, 300 samples were selected using the Slovin formula. The analyzed variables included age, weekly wages, hours worked per week, initial claim cost, and final claim cost. Prior to clustering, the data were standardized using a robust scaler and tested for multicollinearity. The optimal number of clusters was determined using the Silhouette Coefficient method. The results indicated that the optimal clustering structure consisted of three clusters, with a Silhouette Coefficient value of 0.703. These clusters represented low-risk, medium-risk, and high-risk worker groups. The findings offer valuable insights for insurers to enhance risk segmentation, claims management, and more targeted premium policy formulation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Alfiah, F., Almadayani, A., Al Farizi, D., & Widodo, E. (2021). Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020. JURNAL ILMIAH SAINS, 22(1), 1. https://doi.org/10.35799/jis.v22i1.35911
Amna, S, W., Sudipa, I. G. I., Putra, T. A. E., Wahidin, A. J., Syukrilla, W. A., Wardhani, A. K., Heryana, N., Indriyani, T., & Santoso, L. W. (2022). Data Mining (D. Ediana & A. Yanto, Eds.; 1st ed.). PT Global Eksekutif Teknologi.
Aulanda, L., Windarto, A. P., & Okprana, H. (2021). Pengelompokan Pembiayaan Nasabah Klaim Asuransi Pengguna Kendaraan Bermotor dengan Metode K-Medoids. TIN: Terapan Informatika Nusantara, 2(4), 263–270. https://doi.org/https://ejurnal.seminar-id.com/index.php/tin/article/view/902
Billa, C., Husaini, A., Kuliah, M., Risiko, M., & Syariah, E. (2023). Pemahaman Resiko Dan Manajemen Resiko. 1(3), 318–325. https://doi.org/10.61132/nuansa.v1i3%20September.272
Fajriana. (2021). Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara. Jurnal Edukasi Dan Penelitian Informatika, 7(2), 263–269.
Gani, E. S. (2015). Sistem Perlindungan Upah di Indonesia. Tahkim, 11(1), 127–143.
Hidayah, N., & Widyawati, D. (2016). Pengaruh Profitabilitas, Leverage, Dan Kebijakan Dividen Terhadap Nilai Perusahaan Food And Beverages. Jurnal Ilmu Dan Riset Akuntansi, 9(5), 1–19.
Kementerian Ketenagakerjaan Republik Indonesia. (2024). Kasus Kecelakaan Kerja Tahun 2024. Jakarta: Kemnaker RI.
Meiriza, A., Ali, E., Rahmiati, & Agustin. (2023). Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Program BPJS Ketenagakerjaan. Indonesian Journal of Computer Science, 12, 714–728. https://doi.org/https://doi.org/10.33022/ijcs.v12i2.3184
Mirantika, N., Syamfithriani, T. S., & Trisudarmo, R. (2023). Implementasi Algoritma K-Medoids Clustering Untuk Menentukan Segmentasi Pelanggan. JURNAL NUANSA INFORMATIKA, 17, 2614–5405. https://doi.org/10.25134/nuansa
Nugraha, W., Sabaruddin, R., & Murni, S. (2024). Teknik Scaling Menggunakan Robust Scaler Untuk Mengatasi Outlier Data Pada Model Prediksi Serangan Jantung. Techno.COM, 23(2), 319–327. https://doi.org/https://doi.org/10.62411/tc.v23i2.10463
Rahmawati, T., Wilandari, Y., & Kartikasari, P. (2024). Analisis Perbandingan Silhouette Coefficient dan Metode Elbow Pada Pengelompokkan Provinsi di Indonesia Berdasarkan Indikator IPM dengan K-Medoids. Jurnal Gaussian, 13(1), 13–24.
Riduan, A., & Setiawati, L. (2021). Analisis Pengaruh Kepribadian dan Etos Kerja Terhadap Kinerja Karyawan. Jurnal Indonesia Sosial Sains , 2(9), 2179–2185.
Suliman. (2021). Implementasi Data Mining Terhadap Prestasi Belajar Mahasiswa Berdasarkan Pergaulan dan Sosial Ekonomi dengan Algoritma K-Means Clustering. Jurnal Sistem Informasi Dan Sistem Komputer, 6(1), 1–11.
Sumarningsih, T. (2015). Pengaruh Kerja Lembur pada Produktivitas Tenaga Kerja Konstruksi. Jurnal Ilmu Dan Terapan Bidang Teknik Sipil, 20(1), 63–69.
Yeldan, M., & Saykan, Y. (2024). Reserve Estimation Using Paid and Incurred Claims Information. Sigma Journal of Engineering and Natural Sciences, 42(3), 747–754.