Electronic Money Transactions Forecasting with Support Vector Regression (SVR) and Vector Autoregressive Moving Average (VARMA)
Abstract views: 208
,
pdf downloads: 178
Abstract
In today's digital era, the trend of payments with electronic money is rising. Some people have switched to do their way to the modern method such as electronic money. This is to improve the efficiency of the financial system. However, with the convenience and speed provided, if the use of electronic money is not being controlled properly, this can cause an unmanageable price of goods. In the context of controlling the risk of the use of electronic money, it is required to predict the use of electronic money in Indonesia. This paper, by using multivariate data analysis with the variable of electronic money transaction and Money supply (M1) as supporting variables in order to predict the nominal of electronic money transactions. The methods used are Vector Autoregressive Moving Average (VARMA) and Support Vector Regression (SVR). The results of the forecasting model will be compared using Mean Absolute Percentage Error (MAPE). According to the research that had been done, the SVR model had a better result compared to VARMA with a MAPE value of 3.577 %. This shows that the prediction data of the SVR model is close to actual data
Downloads
References
[2] Nicodemus, Syahwier, Coki Ahmad. "ANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI PERMINTAAN UANG ELEKTRONIK (E-MONEY)DI INDONESIA." Diss. Universitas Sumatera Utara, 2019.
[3] Putera, D. Egi Kurnia. "Peran Uang Elektronik dalam Laju Inflasi di Indonesia Tahun 2010-2016." Jurnal Ilmiah Mahasiswa FEB 5.2 (2017).
[4] Assiddiki, Muhammad Rizki. PENERAPAN METODE BACKPROPAGATION DENGAN ALGORITMA GENETIKA DALAM PREDIKSI PEREDARAN UANG ELEKTRONIK DI INDONESIA. Diss. Universitas Islam Negeri Sultan Syarif Kasim Riau, 2020.
[5] Kadri, Farid, Fouzi Harrou, and Ying Sun. "A multivariate time series approach to forecasting daily attendances at hospital emergency department." 2017 IEEE symposium series on computational intelligence (SSCI). IEEE, 2017.
[6] Kanchymalay, Kasturi, et al. "Multivariate time series forecasting of crude palm oil price using machine learning techniques." IOP Conference Series: Materials Science and Engineering. Vol. 226. No. 1. IOP Publishing, 2017.
[7] Ulya, Atiyatul. Peramalan harga saham penutupan menggunakan metode vector autoregressive moving average (VARMA). Diss. Universitas Islam Negeri Maulana Malik Ibrahim, 2019.
[8] Artha, Komang Sidhi, Edi Winarko, and D. Ilmu. "Perbandingan Eros, Euclidean Distance dan Dynamic Time Warping dalam Klasifikasi Data Multivariate Time Series Menggunakan kNN." Prosiding Seminar Nasional Pendidikan Teknik Informatika. 2016.
[9] https://www.bi.go.id, “Apa itu Uang Elektronik â€, https://www.bi.go.id/id, 2020. https://www.bi.go.id/id/edukasi/Pages/Apa-itu-Uang-Elektronik.aspx (accessed Nov. 20, 2021).
[10] Artha, Komang Sidhi, Edi Winarko, and D. Ilmu. "Perbandingan Eros, Euclidean Distance dan Dynamic Time Warping dalam Klasifikasi Data Multivariate Time Series Menggunakan kNN." Prosiding Seminar Nasional Pendidikan Teknik Informatika. 2016.
[11] Zivot, Eric. Introduction to computational finance and financial econometrics. Chapman & Hall Crc, 2017.
[12] Suharsono, Agus, Isma Muthahharah, and Aryo Wibisono. "ASEAN STOCK PRICE MODELING USING VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)." International Journal of Academic Research 8 (2016).
[13] Muthahharah, Isma. Pemodelan Harga Saham Negara ASEAN Menggunakan VARMA dan VARMAX. Diss. Institut Teknologi Sepuluh Nopember, 2015.
[14] William, Wei, and S. Wei. "Time series analysis: univariate and multivariate methods." USA, Pearson Addison Wesley, Segunda edicion. Cap 10 (2006): 212-235.
[15] Dewi, Krishnanti, Putra Pandu Adikara, and Sigit Adinugroho. "Prediksi Indeks Harga Konsumen (IHK) Kelompok Perumahan, Air, Listrik, Gas Dan Bahan Bakar Menggunakan Metode Support Vector Regression." Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN 2548 (2018): 964X.
[16] Yasin, Hasbi, Alan Prahutama, and Tiani Wahyu Utami. "Prediksi harga saham menggunakan support vector regression dengan algoritma grid search." Media Statistika 7.1 (2014): 29-35.
[17] DHEA, LIVITA CAHYA. MODEL OPTIMASI KEUNTUNGAN PRODUKSI MENGGUNAKAN METODE REGRESI LAGRANGE MULTIPLIER. Diss. UIN Raden Intan Lampung, 2021.
[18] J. Rizal and S. Akbar, “Perbandingan Uji Stasioner Data Timeseries Antara Metode: Control Chart, Correlogram, Akar Unit Dickey Fuller, dan Derajat Integrasi,†GRADIEN J. Ilm. MIPA, vol. 11, no. 1, pp. 1040–1046, 2015.
[19] I. Muthahharah, “Pemodelan Harga Saham Negara ASEAN Menggunakan VARMA dan VARMAX,†Institut Teknologi Sepuluh Nopember, 2015.
[20] N. H. Miswan, R. M. Said, and S. H. H. Anuar, “ARIMA with regression model in modelling electricity load demand,†J. Telecommun. Electron. Comput. Eng., vol. 8, no. 12, pp. 113–116, 2016.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT.
.png)
.jpg)
