Perpindahan Minat Mahasiswa Terhadap Penggunaan Aplikasi E-Wallet Sebelum dan Setelah Perkuliahan Luring di Masa Pandemi Covid
DOI:
https://doi.org/10.31102/zeta.2023.8.1.39-46Keywords:
E-wallet, Stochactic Process, Students, Markov Chain, Electronic TransactionsAbstract
Technological advances in the field of financial innovation, namely e-wallets, provide convenience and practicality in making transactions online. The use of e-wallets is popular among students, one of which is the Faculty of Science and Technology, Airlangga University. Students with interests or tastes in the type of e-wallet and the running of all activities offline make it very likely that there will be a change in student interest from one e-wallet to another e-wallet. The change in interest will be analyzed with the Markov chain transition probability. The data in this study is primary data by conducting an online survey of 100 respondents in the form of a g-form given to students of the Faculty of Science and Technology, Airlangga University. Based on the results of the Markov chain transition opportunities, it can be concluded that the use of e-wallet applications can move according to the advantages of e-wallet applications in each region. This change in interest in e-wallet application users occurs because the application of choice is an application that provides easy access, lots of discounts, small admin fees, and others.
Downloads
References
Allo, dkk. (2012). Analisis Rantai Markov Untuk Mengetahui Peluang Perpindahan Merek Kartu Seluler Pra Bayar GSM. Jurnal MIPA UNSRAT ONLINE. 2(1):17-22.
Hillier, F. S., dan Lieberman, G. J. (2008). Introduction to Operation Research. 8th Edition Jilid 2. Penerbit Andi, Yogyakarta.
Howard, A. dan Rorres, C. (2004). Aljabar Linier Elementer versi Aplikasi.Edisi ke-8, jilid 2.Terjemahan Izham Harmein dan Julian Gresdando.Erlangga. Jakarta.
Kuganathan, K. V. dan Wikramanayake, G. N. (2014). Next Generation Smart Transaction Touch Points. International Conference on Advances in ICT for Emerging Regions (ICTer). 96-102.
Langi, Y. (2009). Penentuan Klasifikasi State pada Rantai Markov. Jurnal Ilmiah Sains. 9(1):63-67.
Megadewandanu, dkk. (2016). Exploring Mobile Wallet Adoption in Indonesia Using UTAUT2 An Approach from Consumer Perspective. 2nd International Conference on Science and TechnologyComputer. 1-6.
Nurhamiddin dan Sulisa, "Peramalan Cuaca Menggunakan Metode Rantai MArkov (Studi Kasus : Rekaman Cuaca Harian di Kantor BMKG Kota Ternate)," BIOSAINSTEK, vol. 2, no. 1, pp. 16-22, 2019.
Nurjana, dkk. (2016). Penerapan Rantai Markov Dalam Pemilihan Minat Masuk Siswa SMA Ke Universitas Di Indonesia. Jurnal De Cartesian 5(1):50-56.
Sharma, dkk, 2017. Structural Equation Model (Sem) Neural Network (Nn) Model for Predicting Quality Determinants of E-Learning Management Systems Behav. Inf. Technol. 36 (10), 1053- 1066.
Siswanto. (2007). Operations Research, Jilid Kedua. Erlangga. Jakarta.
Sugiyono. (2016). Metode Penelitian Kuantitatif, Kualitatif, dan Kombinasi (Mixed Methods). Bandung: Alfabeta.
