Hybrid Naive Bayes Classifier and Markov Chain for Predicting the Level of Quick Response Indonesian Standard (QRIS) Usage

  • Chairina Chairina Universitas Islam Negeri Sumatera Utara
  • Rina Widyasari Universitas Islam Negeri Sumatera Utara
  • Machrani Adi Putri Siregar Universitas Islam Negeri Sumatera Utara
Keywords: Classification, Prediction, Naïve Bayes Classifier Method, Markov Chain

Abstract

Digital based payment systems have provided a variety of payment formats and interactions. Considering the benefits and effectiveness of the Quick Response code, Bank Indonesia has designated this technology as the standard for using payment methods. The Hybrid Naϊve Bayes Classifier and Markov Chain methods are classification and prediction methods that can be used to predict the level of QRIS use in this research. The data used comes from Bank Indonesia data for Medan City from January 2020 to February 2023 with 3 parameters for the level of QRIS usage. The research results show that with testing data the classification results of the level of use of QRIS as an electronic payment tool in Indonesia have increased and only in December 2022 has it decreased. Then, based on the percentage probability calculation value using the Markov Chain method with n going to infinity, the probability value in the transition probability matrix converges to a value. This value is the same as the opportunity value calculated by limiting probability in the Markov Chain. The percentage chance value for the level of use of QRIS as an electronic payment tool in Indonesia has an increase of 0.7076 or 70.76%, and a percentage chance of a decrease of 29.24 or 29.24%.

References

Y. S. Atmaja and D. H. Paulus, “PARTISIPASI BANK INDONESIA DALAM PENGATURAN DIGITALISASI SISTEM PEMBAYARAN INDONESIA,” J. Masal. Huk., vol. 51, no. 3, pp. 271–286, 2022.

M. Z. Batubara and M. I. P. Nasution, “Sistem Informasi Online Pengelolaan Dana Sosial Pada Rumah Yatim Sumatera Utara,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 3, pp. 164–171, 2023.

P. Muniarty, M. S. Dwiriansyah, Wulandari, M. Rimawan, and Ovriyadin, “Efektivitas Penggunaan QRIS Sebagai Alat Transaksi Digital Di Kota Bima,” Own. Ris. J. Akunt., vol. 7, no. 3, pp. 2731–2739, 2023.

N. Widowati and M. Khusaeni, “ADOPSI PEMBAYARAN DIGITAL QRIS PADA UMKM BERDASARKAN TECHNOLOGY ACCEPTANCE MODEL,” J. Dev. Econ. Soc. Stud., vol. 1, no. 2, pp. 325–347, 2022.

A. F. S. K. Rahman and Supriyanto, “ANALISIS FAKTOR YANG MEMPENGARUHI MINAT PENGGUNAAN QRIS SEBAGAI METODE PEMBAYARAN PADA MASA PANDEMI,” Ina. Indones. Sci. J. Islam. Financ., vol. 1, no. 1, pp. 1–21, 2022.

D. M. Hutahuruk, “Per September, Transaksi QRIS Tembus 281 Juta Kali dengan Nilai Rp 29,7 Triliun,” KONTAN.CO.ID, Jakarta, Oct. 31, 2022.

M. M. Aulia, D. T. Setiyoko, D. Sunarsih, and A. Purnomo, “Penanaman Nilai Multikultural dengan Metode Hybrid Learning pada Masa Pandemi Covid-19,” JAMU J. Abdi Masy. UMUS, vol. 1, no. 2, pp. 71–79, 2021.

M. Fatchan, N. Tedi, Alfiyan, Kurniawan, and E. Widodo, “Perbandingan Dalam Memprediksi Penyakit Liver Menggunakan Algoritma Naïve Bayes Dan K-Nearest Neighbor,” J. Pelita Teknol., vol. 16, no. 1, pp. 15–21, 2021.

A. T. Dwilaga, “Decision Model and Industry Optimization in Production: A Systematic Literature review,” Sainteks J. Sain dan Tek., vol. 5, no. 1, 2023.

O. B. Saputri, “Preferensi konsumen dalam menggunakan quick response code indonesia standard (qris) sebagai alat pembayaran digital,” Kinerja, vol. 17, no. 2, pp. 237–247, 2020.

F. Giawa, R. S. Lubis, and R. Widyasari, “ANALISIS PENJUALAN DAN PERSAINGAN AIR MINERAL KEMASAN BOTOL SELAMA PANDEMI COVID-19 DI KOTA MEDAN MENGGUNAKAN RANTAI MARKOV ORDE DUA,” Math Educ. J., vol. 5, no. 1, pp. 74–81, 2021.

R. A. Azzahroo and S. D. Estiningrum, “Preferensi Mahasiswa dalam Menggunakan Quick Response Code Indonesia Standard (QRIS) sebagai Teknologi Pembayaran,” J. Manaj. Motiv., vol. 17, no. 1, pp. 10–17, 2021.

Syahranitazli and Samsudin, “SISTEM INFORMASI GEOGRAFIS PERSEBARAN PONDOK PESANTREN KABUPATEN LANGKAT DAN BINJAI MENGGUNAKAN LEAFLET,” J. Pendidik. Teknol. Inf., vol. 6, no. 1, pp. 2621–1467, 2023.

S. Ramadani, N. Z. S. Ayu, N. Nurhayati, F. Azzahra, and A. P. Windarto, “Analisis Data Mining Naive Bayes Klasifikasi Pada Kelayakan Penerima PKH,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 4, no. 1, pp. 374–381, 2020, doi: 10.30865/komik.v4i1.2725.

I. K. Hasan, Resmawan, and J. Ibrahim, “erbandingan K-Nearest Neighbor dan Random Forest dengan Seleksi Fitur Information Gain untuk Klasifikasi Lama Studi Mahasiswa,” Indones. J. Appl. Stat., vol. 5, no. 1, pp. 58–66, 2022.

Published
2024-03-27
How to Cite
Chairina, C., Widyasari, R., & Siregar, M. A. P. (2024). Hybrid Naive Bayes Classifier and Markov Chain for Predicting the Level of Quick Response Indonesian Standard (QRIS) Usage. Journal of Information Technology, 4(1), 150-156. https://doi.org/10.46229/jifotech.v4i1.875