Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia

Muktar Redy Susila; Mochamad Jamil; Bambang Hadi Santoso

doi:10.34312/jjom.v5i1.14889

Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia

Muktar Redy Susila, Mochamad Jamil, Bambang Hadi Santoso

Abstract

The purpose of this study is to model electronic money in Indonesia using a hybrid model and compare its accuracy with the non-hybrid model. The hybrid model used is Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network. The data used is the amount of electronic money circulation for the monthly period January 2009 to October 2021. The ARIMA model formed from research data is ARIMA (1,1,0) with additive outliers and level shift outliers. For Artificial Neural Network modeling is limited by using one hidden layer with three neurons. In the modeling process, 20 repetitions were carried out. The smallest repetition value was obtained, namely the 13th repetition with an error value of 2.569. In this study, it was found that the ARIMA- Artificial Neural Network hybrid model had a smaller Root Mean Squared Error (RMSE) in sample and out sample than the non-hybrid model. Based on the results of the study, it can be concluded that by combining the ARIMA model with Artificial Neural Network, it can increase the accuracy of the data fit results and forecast results.

Keywords

Electronic Money; ARIMA; Artificial Neural Network; Hybrid

Full Text:

PDF

References

M. Choiril Anam, “E-money (Uang Elektronik) Dalam Perspektif Hukum Syari’ah,” Qaw˜an¨ın Journal of Economic Syaria Law, vol. 2, no. 1, pp. 95–112, jan 2018, doi: 10.30762/q.v2i1.1049.

J. Syamsu, D. Oldisan, and Y. Efendi, “Persepsi Masyarakat Terhadap Implementasi Metode Pembayaran Menggunakan Kartu E-Money (BRIZZI) pada Bus Trans Padang,” Public Adm. Bus. Rural Develoment Plan, vol. 4, no. 1, pp. 1–8, 2022.

R. Tazkiyyaturrohmah, “Eksistensi Uang Elektronik Sebagai Alat Transaksi Keuangan Modern,” Muslim Heritage, vol. 3, no. 1, pp. 23–44, jun 2018, doi: 10.21154/muslimheritage.v3i1.1240.

A. P. Wijaya, “Pengaruh Penggunaan Uang Elektronik, Jumlah Uang Beredar, dan Inflasi Terhadap Pertumbuhan Ekonomi Indonesia,” in Prosiding Seminar Nasional & Call for Paper STIE AAS, 2021, pp. 206–212.

S. N. Janah, W. Sulandari, and S. B. Wiyono, “Penerapan Model Hybrid ARIMA backpropagation Untuk Peramalan Harga Gabah Indonesia,” Media Statistika, vol. 7, no. 2, pp. 63–69, dec 2014, doi: 10.14710/medstat.7.2.63-69.

F. Fejriani, M. Hendrawansyah, L. Muharni, S. F. Handayani, and S. Syaharuddin, “Forecasting Peningkatan Jumlah Penduduk Berdasarkan Jenis Kelamin Menggunakan Metode ARIMA,” Geogr. J. Kajian, Penelit. dan Pengemb. Pendidik., vol. 8, no. 1, pp. 27–36, 2020, doi: 10.31764/geography.v8i1.2261.

R. A. Wulandari and R. Gernowo, “Metode Autoregressive Integrated Movingaverage (ARIMA) Dan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam Analisis Curah Hujan,” Berkala Fisika, vol. 22, no. 1, pp. 41–48, 2019.

H. Panjaitan, A. Prahutama, and S. Sudarno, “Peramalan Jumlah Penumpang Kereta Api Menggunakan Metode ARIMA, Intervensi dan ARFIMA (Studi Kasus: Penumpang Kereta Api Kelas Lokal Ekonomi DAOP IV Semarang),” Jurnal Gaussian, vol. 7, no. 1, pp. 96–109, feb 2018, doi: 10.14710/j.gauss.v7i1.26639.

S. Suhartono, D. E. Ashari, D. D. Prastyo, H. Kuswanto, and M. H. Lee, “Deep Neural Network for Forecasting Inflow and Outflow in Indonesia,” Sains Malaysiana, vol. 48, no. 8, pp. 1787–1798, aug 2019, doi: 10.17576/jsm-2019-4808-26.

T.O. Olatayo and K. I. Ekerikevwe, “Performance Measures for Evaluating the Accuracy of Time Series Hybrid Model Using High Frequency Data,” Britain International of Exact Sciences (BIoEx) Journal, vol. 4, no. 3, pp. 244–259, sep 2022, doi: 10.33258/bioex.v4i3.760.

I. Khandelwal, R. Adhikari, and G. Verma, “Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition,” Procedia Computer Science, vol. 48, pp. 173–179, 2015, doi: 10.1016/j.procs.2015.04.167.

I. K. Hasan and Ismail Djakaria, “Perbandingan Model Hybrid ARIMA-NN dan Hybrid ARIMA-GARCH untuk Peramalan Data Nilai Tukar Petani di Provinsi Gorontalo,” Jurnal Statistika dan Aplikasinya, vol. 5, no. 2, pp. 155–165, dec 2021, doi: 10.21009/JSA.05204.

I. Tahyudin, R. Wahyudi, W. Maulana, and H. Nambo, “The mortality modeling of covid-19 patients using a combined time series model and evolutionary algorithm,” International Journal of Advances in Intelligent Informatics, vol. 8, no. 1, pp. 69–83, mar 2022, doi: 10.26555/ijain.v8i1.669.

M. R. Susila, M. Jamil, and B. H. Santoso, “Analisis Dampak COVID-19 dan FaktorFaktor yang Mempengaruhi Indeks Saham Bank Jatim Menggunakan Pendekatan Regresi Time Series,” Jambura Journal of Mathematics, vol. 4, no. 2, pp. 220–231, jun 2022, doi: 10.34312/jjom.v4i2.13401.

M. R. Susila, R. C. Putri, and D. Arini, “Pemodelan Regresi Spasial Investasi Luar Negeri yang Masuk ke Indonesia,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 14, no. 4, pp. 543–556, dec 2020, doi: 10.30598/barekengvol14iss4pp543-556.

D. N. Gujarati and D. C. Porter, Basic Econometrics, 5th ed. New York: McGraw-Hill Education, 2008.

W. W. S. Wei, Multivariate Time Series Analysis and Applications, 1st ed. New York: John Wiley & Sons Inc, 2019.

I. K. Putri and S. Suhartono, “Deteksi Outlier pada Model ARIMA Musiman Ganda untuk Peramalan Beban Listrik Jangka Pendek di Jawa Timur,” J. Sains dan Seni ITS, vol. 4, no. 1, pp. D31–D36, 2015, doi: 10.12962/j23373520.v4i1.8837.

I. Prayoga, S. Suhartono, and S. P. Rahayu, “Hierarchical Forecasting Method Based on ARIMAX and Recurrent Neural Network for Motorcycle Sales Prediction,” Int. J. Appl. Math. Stat., vol. 5, no. 3, pp. 116–124, 2015.

G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,”Neurocomputing, vol. 50, pp. 159–175, jan 2003, doi: 10.1016/S0925-2312(01)00702-0.

M. R. Susila, “Spatio-Temporal Analysis of Rupiah Loans Provided by Commercial Banks And Rural Banks,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 3, pp. 1003–1012, sep 2022, doi: 10.30598/barekengvol16iss3pp1003-1012.

M. R. Susila and F. A. Pradhani, “Analisis Pengaruh PDRB Per Kapita dan Jumlah Tenaga Kerja Terhadap Jumlah Pendapatan Pajak Daerah Provinsi di Indonesia, ”Jurnal Ilmiah Akuntansi dan Keuangan (JIAKu), vol. 1, no. 1, pp. 72–87, apr 2022, doi:10.24034/jiaku.v1i1.4996.

P. Wibowo, “Pengaruh Perbedaan Jumlah Hidden Layer dalam Jaringan Syaraf Tiruan Terhadap Prediksi Kebutuhan Captopril dan Paracetamol pada Rumah Sakit,” Media Apl., vol. 11, no. 2, pp. 118–131, 2019, doi: 10.33488/2.ma.2019.2.207.

DOI: https://doi.org/10.34312/jjom.v5i1.14889