Pengelompokan Pembiayaan Nasabah Klaim Asuransi Pengguna Kendaraan Bermotor dengan Metode K-Medoids
- Lulu Aulanda STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
- Agus Perdana Windarto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
-
Harly Okprana
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
(*) Corresponding Author
Abstract
In general, insurance is providing risk coverage to the insurer, namely the insurance company for a predetermined period and agreements. Insurance or coverage is an agreement between two or more parties, in which the insurer binds himself to the insured, by receiving an insurance premium, to provide compensation to the insured due to loss, damage or loss. The k-medoids method is one of several clustering methods in data mining which is part of partitional clustering. This method uses objects in a collection of objects to represent a cluster. The k-medoids clustering method can be applied to customer financing data for insurance claims on motor vehicle users, so that the financing grouping can be seen based on these data. From the grouping data, the characteristics can be seen so that it is known that the cluster is low, cluster is medium and cluster is high
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References
Astria, C. et al. (2019) ‘Penerapan K-Medoid Pada Rumah Tangga Yang Memiliki Sumber’, 3, pp. 604–609. doi: 10.30865/komik.v3i1.1667.
Defiyanti, S., Jajuli, M., & W, N. rohmawati. (2017). Optimalisasi K - Medoid Dalam Pengklasteran Mahasiswa Pelamar Beasiswa Dengan Cubic Clustering Criterion. TEKNOSI, 03(01), 211–218.
Haviluddin. (2011). Memahami Penggunaan UML ( Unified Modelling Language ). Memahami Penggunaan UML (Unified Modelling Language), 6(1), 1–15. https://informatikamulawarman.files.wordpress.com/2011/10/01-jurnal-informatika-mulawarman-feb-2011.pdf
Hendini, A. (2015). Pemodelan Uml Sistem Informasi Monitoring Penjualan Dan Stok Barang. Journal of Chemical Information and Modeling, 53(9), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004
Heriyanto, Y. (2018). Perancangan Sistem Informasi Rental Mobil Berbasis Web Pada PT.APM Rent Car. Jurnal Intra-Tech, 2(2), 64–77.
Lambertus, S. (2018). Optimasi Kinerja Firewall Menggunakan Teknik Data Mining.
Pangan, P. T. (2015). Analisis Pengelompokan Daerah Menggunakan Metode Non-Hierarchical Partitioning K-Medoids Dari Hasil Komoditas Pertanian Tanaman Pangan (Studi Kasus Kabupaten/Kota Se-Jawa Tengah Tahun 2009 €“ 2013). None, 4(4), 825–836.
Pramesti, D. F., Furqon, M. T., & Dewi, C. (2017). Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan / Lahan Berdasarkan Persebaran Titik Panas ( Hotspot ). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 1(9), 723–732.
Pulungan, N., Suhada, S., & Suhendro, D. (2019). Penerapan Algoritma K-Medoids Untuk Mengelompokkan Penduduk 15 Tahun Keatas Menurut Lapangan Pekerjaan Utama. KOMIK (Konferensi Nasional Teknologi Informasi Dan Komputer), 3(1), 329–334. https://doi.org/10.30865/komik.v3i1.1609
Sari, D. R. et al. (2019) ‘Penerapan Metode Naive Bayes dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen’, Prosidiing Seminar Nasional Riset Information Science (SENARIS), pp. 287–297. doi: 10.30645/senaris.v1i0.34.
Sari, Y. P. (2018) Evaluasi Sistem Pengendalian Manajemen Pada Klaim Asuransi Kendaraan Bermotor Di Pt Asuransi Wahana Tata (Aswata) Cabang Palembang.
Sundari, S. et al. (2019) ‘Analisis K-Medoids Clustering Dalam Pengelompokkan Data Imunisasi Campak Balita di Indonesia’, pp. 687–6
Silitonga, D. A., Windarto, A. P., & Hartama, D. (2019). Penerapan Metode K-Medoid pada Pengelompokan Rumah Tangga Dalam Perlakuan Memilah Sampah Menurut Provinsi. Seminar Nasional Sains & Teknologi Informasi (SENSASI) SENSASI 2019 ISBN:, 313–318.
Sindi, S., Ratnasari, W., Ningse, O., Sihombing, I. A., Zer, F. I. R. H., Hartama, D., & Kunci, K. (2020). Analisis algoritma k-medoids clustering dalam pengelompokan penyebaran covid-19 di indonesia. 4(1), 166–173.
Tanjung, Q. (2017). Sistem Penunjang Keputusan Kelayakan Kredit Motor Menggunakan Metode Naive Bayes Pada NSC Finance Cikampek.
Verawati, & Liksha, P. D. (2018). Aplikasi Akuntansi Pengolahan Data Jasa Service Pada Pt. Budi Berlian Motor Lampung. Jurnal Sistem Informasi Akuntansi (JUSITA), 1(1), 1–14.
Wira, B., Budianto, A. E., & Wiguna, A. S. (2019). Implementasi Metode K-Medoids Clustering untuk Mengetahui Pola Pemilihan Program Studi. Jurnal Terapan Sains & Teknologi, 1(3), 54–69.
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