Clustering Koridor Transjakarta Berdasarkan Jumlah Penumpang Dengan Algoritma K-Means

Adi Supriyatna(1*), Irmawati Carolina(2), Suhar Janti(3), Ali Haidir(4),

(1) Universitas Bina Sarana Informatika
(2) Universitas Bina Sarana Informatika
(3) Universitas Bina Sarana Informatika
(4) Universitas Bina Sarana Informatika
(*) Corresponding Author

Abstract


Transportation is one of the facilities that make it easy for humans to carry out activities to move places using vehicles that are driven by humans or machines. Based on data obtained from data.jakarta.go.id, the number of Transjakarta bus passengers in corridors 1 to 13 of 2017 amounted to 114,239,960, and in 2018 there were 121,918,964 passengers. The algorithm used in this research is K-Means Cluster, which is implemented using Microsoft Excel and Rapidminer Studio. The purpose of this study is to cluster Transjakarta corridors based on the number of passengers divided into 3 clusters: high, medium, and low. The results of data processing show that the Transjakarta corridor data cluster is based on the number of passengers using the K-Means cluster algorithm using Microsoft Excel and Rapidminer Studio to produce 3 clusters, namely cluster 1 with the highest number of passengers, one corridor, cluster 2 with the number of passengers being nine corridors and cluster 3 or 0 with a low number of passengers there are three corridors. The highest number of passengers is corridor one which serves the Blok M - Kota route, indicating that the Blok M - Kota route is the most used by Transjakarta passengers.

Full Text:

PDF

References


Wikipedia, “Transjakarta.” [Online]. Available: https://id.wikipedia.org/wiki/Transjakarta. [Accessed: 25-Jan-2020].

D. Perhubungan, “Data Jumlah Penumpang Transjakarta,” Jakarta, 2018.

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.

A. Hazman, F. T. Elektro, and U. Telkom, “Penerapan Metode K-Means Clustering Untuk Mengelompokkan Data Pelabuhan Dan Bongkar Muat Barang Di Indonesia,” e-Proceeding Eng., vol. 6, no. 1, pp. 1450–1454, 2019.

M. I. Firdaus, R. D. Octaviani, and I. Yusnita, “Clastering Calon Penumpang Kereta Cepat Jakarta-Bandung,” J. Manaj. Transp. DAN LOGISTIK, vol. 4, no. 2, p. 193, Sep. 2017.

S. Sukamto, I. D. Id, and T. R. Angraini, “Penentuan Daerah Rawan Titik Api di Provinsi Riau Menggunakan Clustering Algoritma K-Means,” JUITA J. Inform., vol. 6, no. 2, p. 137, 2018.

E. Dianawati, P. P. Yanti, and Y. Suryandari, “Klustering Jumlah Penumpang pada Halte Bus Rapid Transit Kota Tangerang,” J. Sist. Cerdas, vol. 2, no. 3, pp. 163–172, 2019.

K. Handoko and L. S. Lesmana, “Data Mining Pada Jumlah Penumpang Menggunakan Metode Clustering,” Snistek, no. 1, pp. 97–102, 2018.

F. Indriyani and E. Irfiani, “Clustering Data Penjualan pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means,” JUITA J. Inform., vol. 7, no. 2, p. 109, 2019.

A. Supriyatna, I. Carolina, W. Widiati, and C. Nuraeni, “Rice Productivity Analysis by Province Using K-Means Cluster Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 771, p. 012025, 2020.

G. Qiu, R. Song, S. He, W. Xu, and M. Jiang, “Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 9, pp. 3351–3360, 2019.

V. Adikariwattage, A. G. de Barros, S. C. Wirasinghe, and J. Ruwanpura, “Airport classification criteria based on passenger characteristics and terminal size,” J. Air Transp. Manag., vol. 24, pp. 36–41, 2012.

K. Handoko and L. S. Lesmana, “Pengelompokkan Data Mining Pada Jumlah Penumpang Di Bandara Hang Nadim,” Comput. Based Inf. Syst. J., vol. 6, no. 2, p. 60, 2018.




DOI: http://dx.doi.org/10.30645/j-sakti.v4i2.259

Refbacks

  • There are currently no refbacks.


J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats