Predictive Analytic Klasifikasi Penentuan Tarif Sewa Bus
Abstract
Currently, the bus rental business has become the choice of consumers in traveling, because of the decision of flexibility and better availability. The government regulates that consumer and business owner agreements determine bus rental rates without routes. In this study intends to do clustering from the history of raw data that already exists before. Data is obtained from companies in the form of spreadsheet files originating from non-information systems. The raw data is combined and normalized, to eliminate the noise data and the data is not abnormal. The clustering results using the K-Means algorithm and Louvain clustering produce several tariff groups that can be used as a reference for determining fare. In this paper also concludes about unbalanced data, which can cause data clustering errors.
Downloads
References
Kementerian Perhubungan nomor 108 tahun 2017, Tentang Penyelenggaraan Angkutaan Orang Dengan Kendaraan Bermotor Umum Tidak Dalam Trayek.
Surat Keputusan Menteri Perhubungan Darat, Pos, Telekomunikasi dan Pariwisata Nomor L. 25/1/18/1963 tanggal 17 Juni 1963.
Blue Bird Sasar Peluang Bisnis Bus Wisata, data diperoleh melalui situs internet: https://www.arah.com/article/26999/blue-bird-sasar-peluang-bisnis-bus-wisata.html
Andrew B. Osmond., Suhono Harso Supangkat., Platform dan Pemodelan Kerjasama Multi Agen untuk Layanan Pengiriman Barang, Jurnal Sistem Cerdas 2019 Volume 02 No. 01, 2019, 22-24.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., dan Lefebvre, E. (2008) : Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, 2008(10), 1–12.
McCall, M. R., Mehta, T., Leathers, C. W., dan Foster, D. M. (2002): An Efficient k-Means Clustering Algorithm: Analysis and Implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 881–892.
