IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING TINGKAT KEPENTINGAN TAGIHAN RUMAH SAKIT DI PT PERTAMINA (PERSERO)

ema ainun novia; Woro Isti Rahayu; Syafrial Fachri Pane

doi:10.33884/jif.v8i01.1844

Authors

ema ainun novia Politeknik Pos Indonesia
Woro Isti Rahayu Politeknik Pos Indonesia
Syafrial Fachri Pane Politeknik Pos Indonesia

DOI:

https://doi.org/10.33884/jif.v8i01.1844

Keywords:

Tagihan, Clustering, K-Means, Rumah Sakit

Abstract

Claims are claims that can be in the form of money, services or goods that are the obligations of another party to an entity. Problems that occur in this department do not yet have information about the bills to be paid based on their level of importance.Therefore this study aims to create a billing grouping system based on the level of importance in each hospital using a data mining algorithm with the K-means method. This method is considered appropriate because of group data based on the closest cluster center point with the data. Billing based on hospitals into 2 clusters namely urgent cluster (C1) and non-urgent cluster (C2).From the calculation of 104 data samples consisting of 4 hospitals, 14 data are in the "Urgent" cluster (C1), 90 data are in the "Not Urgent" cluster (C2). The results are then grouped again based on hospitals so that the grouping obtained at Pertamina Center hospitals cluster 1 there are 3 data and cluster 2 there are 2 data. Pertamina Jaya hospital for cluster 1, there is 1 data and in cluster 2 there are 44 data. In Pertamina Balikpapan hospital for cluster 1, there is 7 data and for cluster 2 there are 25 data. And at Pertamina Plaju hospital for cluster 1 there is 1 data and for cluster 2 there is 13 data.

References

[1] K. Tangkuman, B. Tewal, and I. Trang, “Karyawan Pada Pt . Pertamina ( Persero ),” Penilai. Kinerja,Reward dan Punishm., vol. 3, no. 2, pp. 884–895, 2015.
[2] “Tinjauan Sistem Pembayaran Klaim Biaya Pengobatan Karyawan PT Antam, Tbk Makassar,” 2018.
[3] R. Irma, “Tagihan,” in Akuntansi Dasar Jilid II, Sidoarjo: Uwais Inspirasi Indonesia, 2017.
[4] S. Kasus, U. Dehasen, S. Haryati, A. Sudarsono, and E. Suryana, “IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI MASA STUDI MAHASISWA MENGGUNAKAN ALGORITMA C4 . 5,” vol. 11, no. 2, pp. 130–138, 2015.
[5] I. M. Maharani, “K-Means Clustering,” Medium, 2019. [Online]. Available: https://medium.com/@16611053/k-means-clustering-c5ab3351184b. [Accessed: 08-Jan-2020].
[6] S. Singh, “An Introduction To Clustering,” Medium, 2018. [Online]. Available: https://medium.com/datadriveninvestor/an-introduction-to-clustering-61f6930e3e0b. [Accessed: 08-Jan-2020].
[7] R. Khandelwal, “K-Means Clustering,” Medium, 2018. [Online]. Available: https://medium.com/datadriveninvestor/k-means-clustering-6f2dc458cce8. [Accessed: 08-Jan-2020].
[8] C. Series, “The data mining analysis to determine the priorities of families who receiving assistance The data mining analysis to determine the priorities of families who receiving assistance,” 2019.
[9] R. D. Ramadhani, “Memahami K-Mean Clustering Dengan R,” Medium, 2019. [Online]. Available: https://medium.com/@16611129/belajarbersama-kides-episode-2-cd51d0bfd121. [Accessed: 08-Jan-2020].
[10] S. Index, “Penerapan Algoritma K-Means Untuk Clustering Penilaian Dosen Berdasarkan Indeks Kepuasan Mahasiswa,” vol. 16, no. 1, pp. 17–24, 2017.
[11] D. A. N. Telepon, B. Android, and P. Wisma, “Tegalrejo Kota Salatiga.”
[12] D. I. Rumah, S. Umum, D. M. Yunus, and K. Bengkulu, “ANALISIS MANAJEMEN BENCANA GEMPA THE ANALYSIS OF EARTHQUAKE MANAGEMENT PENDAHULUAN WHO ( World Health Organization ) menganggap perlu untuk membangun rumah sakit yang aman , terutama pada situasi menyelamatkan jiwa dan dapat terus bagi masyarakat . Rumah ,” vol. 8, no. 1, pp. 28–33, 2017.
[13] T. N. Hermawan, M. Ugiarto, and N. Puspitasari, “Sistem Evaluasi Kinerja Asisten Laboratorium Menggunakan Metode K-Means,” Pros. Semin. Nas. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 2, pp. 3–6, 2017.