skip to main content

Implementasi forecastHybrid Package menggunakan R Studio Cloud untuk Prediksi Pertumbuhan Dana Pihak Ketiga dan Pembiayaan Pada Bank Muamalat Indonesia

Niken Probondani Astuti  -  Manajemen, STIEM Bongaya, Indonesia
*Rizal Bakri orcid scopus  -  Akuntansi, STIEM Bongaya, Indonesia

Citation Format:
Abstract
This study aims to find out how to forecast the growth performance of third party funds (TPF) and financing which is measured on a quarterly by applied the hybrid method with R Studio Cloud using ‘forecastHybrid’ package. This prediction is expected to provide information and data on the growth of third party fund and financing for Bank Muamalat which is experiencing problems of lack of capital and non-performing funds (NPF). Forecasting with Hybrid methods combines ARIMA auto forecasting methods, exponential smoothing forecasting methods, theta forecasting methods, neural network forecasting methods, seasonal and trend decomposition forecasting methods, and TBATS forecasting methods. The forecast results show that the Hybrid method is able to provide information as a decision-making material for Bank Muamalat

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
CTA Agreement
Subject
Type Research Instrument
  Download (211KB)    Indexing metadata
Keywords: Hybrid Forecast; Third Party Fund Growth; Financing Growth; ForecastHybrid package; R Studio Cloud

Article Metrics:

  1. Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K. V., Mohamed, N.A and Arshad, H., 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938
  2. Ali, H., Miftahurrohman., 2015. Analisis pengaruh dana pihak ketiga ( DPK ), non performing financing dan tingkat suku bunga kredit terhadap pembiayaan berbasis bagi hasil, The Journal of Tawhidinomics, 1(2):151–66
  3. Astasia, A., Wulandary, S., Istinah, A.N., Yuliati, I.F., 2020. Peramalan tingkat profitabilitas bank syariah dengan menggunakan model fungsi transfer single input, Jurnal Statistika Dan Aplikasinya, 4(1):11–22
  4. Auliasari, K., Kertaningtyas, M., Kriswantono, M., 2019. Penerapan metode peramalan untuk identifikasi potensi permintaan konsumen, Informatics Journal, 4(3):121–29
  5. Bank Muamalat, 2020. 1_komposisi-Pemegang-Saham-Bmi_20170906160038.Pdf. Retrieved ( https://www.Bankmuamalat.co.id/hubungan-investor/pemegang-saham)
  6. Bakri, R., Data, U dan Astuti, N.P., 2019. Aplikasi Auto sales forecasting berbasis computational intelligence website untuk mengoptimalisasi manajemen strategi pemasaran produk. Jurnal Sistem Informasi Bisnis, 9(2):244–51
  7. Chang, P.C., Wang, Y.W dan Liu, C.H., 2007. The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32:86-96
  8. Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., Yasmeen, F., 2019. forecast: Forecasting functions for time series and linear models. R package version 8.9, CRAN
  9. Kurniati, I.N., 2015. Forecasting Pertumbuhan Dana Pihak Ketiga, Bank Indonesia, Jakarta
  10. Lai, K.K., Yu, L., Wang, S., Huang, W., 2006., Hybridizing Exponential Smoothing and Neural Network. ICCS 2006, Part IV, LNCS 3994, 493–500
  11. Lammers, B., 2019. ANN2: Artificial Neural Networks for Anomaly Detection, R Package Version 2.3.2, CRAN
  12. Naim, I., Mahara, T., Idrisi, A.R. 2018. Effective Short-term forcasting for daily time series with complex seasonal patterns. International Comference on Computational Intelligence and Data Science (ICCIDS 2018)
  13. Nikolopoulos, K., Assimakopoulos, V., Bougioukos, N., Litsa, A., Petrapoulus, F., 2011. The Theta Model : An Essential Forecasting Tool for Supply Chain Planning. 2:431–32
  14. Otoritas Jasa Keuangan (OJK), 2020. Snapshot Perbankan Syariah Indonesia September 2020, Jakarta
  15. Team, R.C., 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Austria
  16. Richard, M., 2020. Temuan BPK dan Pekerjaan Rumah Bank Muamalat, finansial.bisnis.com
  17. Rusydiana, A.S., 2020. Prediksi pertumbuhan perbankan syariah di Indonesia Tahun 2020 Dengan quantitative methods, Jurnal Ekonomi Syariah, 4(2):75–91
  18. David, S., Ellis, P., 2019. Convenient Functions for Ensemble Time Series Forecasts, R package version 4.2.17, CRAN
  19. Shumway, R.H., Stoffer, D.S., 2011. Time Series Analysis and Its Applications with R Examples 3nd. Springer New York USA
  20. Smyl, S. 2020., A Hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1):75–85
  21. Stock, J.H., Watson, M.W., 2003. Forecasting output and inflation: the role of asset prices. Journal of Economic Literature, 41(3):788–829
  22. Terui, N., Dijk, H.V. 2002. Combined forecast from linear and nonlinear time series model, International Journal of Forecasting, 18(3):421–38
  23. Ulfah, M. 2010. Analisa perkembangan aset, dana pihak ketiga dan pembiayaan perbankan syariah di Indonesia (Tesis), Universitas Gunadarma

Last update:

No citation recorded.

Last update:

No citation recorded.