Implementasi Analisis Markov pada R Studio untuk Model Prediksi Perpindahan Pengguna Transportasi Online

  • Yerymia Alfa Susetyo Universitas Kristen Satya Wacana
Keywords: Markov Analysis, Steady State, Prediction, Online Transportation, Probability

Abstract

The development of transportation in Indonesia has entered an era of collaboration with information technology. Online-based transportation has proven to facilitate the mobility of people's lives. The emergence of various online transportation providers in Indonesia requires these providers to have data-based programmed business planning. Predicting customer loyalty is one of the factors considered in business planning. This study aims to predict the switching behavior of online transportation users using Markov Analysis. The study uses data taken from 100 respondents in Jakarta. User switching patterns are analyzed based on the first, second, and third months of online transportation providers used by the respondents. Gojek and Grab are used as the online transportation providers examined in this study. The study results in a Steady State or equilibrium condition, showing that Gojek has a 66% user loyalty rate, while Grab has a 34% user loyalty rate.

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References

F. Z. Rahmanti, O. A. Permata, K. Amiroh, P. T. Daely, A. Ittaqullah, and D. B. Saputro, “Integrated Information System Based on Google Maps APIs: Design of Surabaya Public Transportation System,” in 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), IEEE, Oct. 2019, pp. 154–159. doi: 10.1109/ICOMITEE.2019.8921161.

K. Kevin and M. A. I. Pakereng, “PERANCANGAN APLIKASI TRANSPORTASI ONLINE DI KOTA KETAPANG MENGGUNAKAN PENDEKATAN USER CENTERED DESIGN,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 5, no. 2, p. 224, Dec. 2022, doi: 10.37600/tekinkom.v5i2.581.

D. S. Dewi, N. Ilmi, and R. S. Dewi, “Applying eye tracker technology for analyzing user interface design of online transportation applications (case study: grab application),” IOP Conf Ser Mater Sci Eng, vol. 722, no. 1, p. 012038, Jan. 2020, doi: 10.1088/1757-899X/722/1/012038.

Y. W. Riyadi Putra, F. Nur Styaningsih, and W. H. Herviana, “Analisis Perkembangan Transportasi Online di Indonesia di Era 4.0 Dengan Metode Penelitian Deskriptif,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 4, no. 1, pp. 162–170, Jan. 2022, doi: 10.47233/jteksis.v4i1.389.

A. Pirdaus, R. D. M. Danial, and A. M. Ramdan, “Analisis Efek Komunitas dan Electronic Word Of Mouth terhadap Brand Switching Produk Xiaomi Pocophone ke Oppo F9 di RNY Communication Kota Sukabumi,” Journal of Management and Bussines (JOMB), vol. 2, no. 1, pp. 1–8, Jun. 2020, doi: 10.31539/jomb.v2i1.1220.

T. A. Nurman, I. Syata, and C. D. Wulandari, “Prediksi Hasil Panen Kopi di Sulawesi Menggunakan Analisis Rantai Markov,” Jurnal MSA ( Matematika dan Statistika serta Aplikasinya ), vol. 9, no. 2, Dec. 2021, doi: 10.24252/msa.v9i2.25413.

B. Achmad, F. Faridah, and L. Fadillah, “Lip Motion Pattern Recognition for Indonesian Syllable Pronunciation Utilizing Hidden Markov Model Method,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 13, no. 1, p. 173, Mar. 2015, doi: 10.12928/telkomnika.v13i1.1302.

R. Asra, M. F. Mappiasse, and A. A. Nurnawati, “Penerapan Model CA-Markov Untuk Prediksi Perubahan Penggunaan Lahan Di Sub-DAS Bila Tahun 2036,” AGROVITAL : Jurnal Ilmu Pertanian, vol. 5, no. 1, p. 1, May 2020, doi: 10.35329/agrovital.v5i1.630.

D. Tamudia, Y. Langi, and J. Titaley, “Analisis Rantai Markov Untuk Memprediksi Perpindahan Merek Shampoo Di Hypermart Swalayan Manado Town Square,” d’CARTESIAN, vol. 3, no. 1, p. 58, Mar. 2014, doi: 10.35799/dc.3.1.2014.3997.

F. N. Masuku, Y. A. R. Langi, and C. Mongi, “ANALISIS RANTAI MARKOV UNTUK MEMPREDIKSI PERPINDAHAN KONSUMEN MASKAPAI PENERBANGAN RUTE MANADO-JAKARTA,” JURNAL ILMIAH SAINS, vol. 18, no. 2, p. 75, Jul. 2018, doi: 10.35799/jis.18.2.2018.20495.

I. Shabri and R. Yanti, “Analisis Kepuasan Mahasiswa Terhadap Pelayanan Akademik Prodi Sastra Inggris Universitas Dharma Andalas Padang,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 2, no. 1, pp. 51–56, Jan. 2020, doi: 10.47233/jteksis.v2i1.88.

I. N. Rizanti and S. Soehardjoepri, “Prediksi Produksi Kayu Bundar Kabupaten Malang Dengan Menggunakan Metode Markov Chains,” Jurnal Sains dan Seni ITS, vol. 6, no. 2, Sep. 2017, doi: 10.12962/j23373520.v6i2.27846.

T. Fritz, T. Gonda, P. Perrone, and E. Fjeldgren Rischel, “Representable Markov categories and comparison of statistical experiments in categorical probability,” Theor Comput Sci, vol. 961, p. 113896, Jun. 2023, doi: 10.1016/j.tcs.2023.113896.

F. Li, S. Xu, H. Shen, and Q. Ma, “Passivity-Based Control for Hidden Markov Jump Systems With Singular Perturbations and Partially Unknown Probabilities,” IEEE Trans Automat Contr, vol. 65, no. 8, pp. 3701–3706, Aug. 2020, doi: 10.1109/TAC.2019.2953461.

Y. Min, M. Ye, L. Tian, Y. Jian, C. Zhu, and S. Yang, “Unsupervised feature selection via multi-step markov probability relationship,” Neurocomputing, vol. 453, pp. 241–253, Sep. 2021, doi: 10.1016/j.neucom.2021.04.073.

S. Inayati and N. Muhaimi, “Penggunaan Rantai Markov Orde Dua untuk Menganalisis Ketersediaan Pemasaran Produk Shampoo Dove di Swalayan Pamella 1 Yogyakarta,” Jurnal Matematika Integratif, vol. 15, no. 1, p. 17, Jul. 2019, doi: 10.24198/jmi.v15.n1.20899.17-27.

Published
2023-07-01
How to Cite
Susetyo, Y. (2023). Implementasi Analisis Markov pada R Studio untuk Model Prediksi Perpindahan Pengguna Transportasi Online. Jurnal Teknologi Dan Sistem Informasi Bisnis, 5(3), 203-209. https://doi.org/10.47233/jteksis.v5i3.844
Section
Articles