Analisis dan Pemetaan Jumlah Penumpang Kereta Api di Indonesia Menggunakan Metode Statistik Deskriptif dan K-means Clustering Analisis dan Pemetaan Jumlah Penumpang Kereta Api di Indonesia Menggunakan Metode Statistik Deskriptif dan K-means Clustering Section Articles

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Benny Wijaya
Tresna Maulana Fahrudin
Aryo Nugroho

Abstract

The development of the population in Indonesia continues to increase, which will require more transportation facilities. PT. Kereta Api Indonesia (KAI) is one of the means of transportation in Indonesia. At present the railroad transportation facilities in Indonesia are still not comprehensive, the regions that have railroad transportation facilities are Java (Jabodetabek and outside Jabodetabek), and Sumatra. By taking data on the number of train passengers from the Central Statistics Agency (BPS), the analysis and mapping of the number of train passengers using descriptive statistics and K-means clustering was carried out in this study. This study produced 3 clusters in which each cluster has a measuring value. Cluster 0 is medium, cluster 1 is high, and cluster 2 is low. Calculated using k-means clustering produces a cluster of 0 there are 63, cluster 1 there is 47, and cluster 2 there are 46 with an accuracy of about 97.9%, and calculated using descriptive statistics to produce cluster 0 there are 108, cluster 1 there is 34, and cluster 2 exists 14 with an accuracy of about 93.6%

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How to Cite
Wijaya, B., Fahrudin, T. and Nugroho, A. (2019) “Analisis dan Pemetaan Jumlah Penumpang Kereta Api di Indonesia Menggunakan Metode Statistik Deskriptif dan K-means Clustering”, Jurnal Mantik, 3(2, Agustus), pp. 1-9. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/236 (Accessed: 30October2020).

References

[1] M. Ruland, “Peningkatan Kinerja Pt. Kereta Api Indonesia Pada Pelayanan Keamanan Dan Keselamatan Publik Dalam Rangka K1etahanan Nasional.pdf.” 2009.
[2] I. P. A. Pratama and A. Harjoko, “Penerapan Algoritma Invasive Weed Optimnization untuk Penentuan Titik Pusat Klaster pada K-Means,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 9, no. 1, p. 65, 2017.
[3] Widiarina, “Klastering Data Menggunakan Algoritma Dynamic K-Means,” Klastering Data Menggunakan Algoritm. Dyn. K-Means, vol. I, no. 2, pp. 260–265, 2015.
[4] A. Sholikhah, “Statistik Deskriptif Dalam Penelitian Kualitatif,” KOMUNIKA J. Dakwah dan Komun., vol. 10, no. 2, pp. 342–362, 1970.
[5] R. A. Vinarti and I. D. M. A. Baskara Joni, “Analisis Statistika Deskriptif pada Kepuasan Pengunjung Terminal Bus Purabaya,” S@Cies, vol. 5, no. 1, pp. 1–8, 2018.
[6] R. AKBAR, “Penerapan Data Mining dengan Menggunakan Metode Clustering K-Mean Untuk Mengukur Tingkat Ketepatan Kelulusan Mahasiswa Program Teknik Informatika S1 Fakultas Ilmu Komputer Universitas Dian Nuswantoro,” Dok. Karya Ilm., 2015.
[7] R. Lynda, S. S. Widya, and S. Esti, “ANALISA CLUSTERING MENGGUNAKAN METODE K-MEANS DAN HIERARCHICAL CLUSTERING ( STUDI KASUS : DOKUMEN SKRIPSI JURUSAN KIMIA , FMIPA , 2 . 3 Term Weighting dengan Term Frequency,” vol. Volume 3 N, 2014.
[8] Windha Mega Pradnya Dhuhita, “Clustering Menggunakan Metode K-Means untuk Menentukan Status Gizi Balita,” J. Inform., vol. 15, no. 2, pp. 160–174, 2016.
[9] M. A. Wahyu, “Penerapan metode k-means clustering untuk mengelompokan potensi produksi buah – buahan di provinsi daerah istimewa yogyakarta,” 2017.
[10] L. Zahrotun, “Analisis Pengelompokan Jumlah Penumpang Bus Trans Jogja Menggunakan Metode Clustering K-Means Dan Agglomerative Hierarchical Clustering (Ahc),” J. Inform., vol. 9, no. 1, pp. 1039–1047, 2015.

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