Analysis of ANN Backpropagation Ability to Predict Expenditure Group Inflation

Mhd Ali Hanafiah(1*), Ni Luh Wiwik Sri Rahayu Ginantra(2), Achmad Daengs GS(3),

(1) Politeknik Bisnis Indonesia, Pematangsiantar, Indonesia
(2) STMIK STIKOM Indonesia, Denpasar, Indonesia
(3) Universitas 45 Surabaya, Surabaya, Indonesia
(*) Corresponding Author

Abstract


The Covid-19 pandemic that has hit the world, especially Indonesia, has greatly disturbed the stability of the inflation rate. Inflation that continues to increase will disrupt the economy in this country. Therefore this study aims to analyze the ability of ANN backpropagation which will be applied to predict the development of the inflation in Indonesia during the Covid-19 pandemic so that later it can be useful information for the government and society. The research data used is inflation data according to expenditure groups obtained from CBS (Central Statistics Agency) in January-May 2020. Prediction is done using the backpropagation neural network algorithm. This paper uses four network architectures, namely: 3-5-1, 3-10-1, 3-25-1 and 3-50-1. Based on the training and testing of the four models, the 3-10-1 model is the best architectural model that is suitable for predicting the development of the inflation in Indonesia with an accuracy of 75%. Also, this model performs an iteration of 25303 and an MSE test of 0.0362820326. Based on the prediction results in June-August 2020 and real data obtained from the Central Statistics Agency, ANN using the backpropagation method is highly recommended to be used to predict the development of Indonesian Inflation according to the Expenditure Group.


Full Text:

PDF

References


M. A. Musarat, W. S. Alaloul, M. S. Liew, A. Maqsoom, and A. H. Qureshi, “Investigating the impact of inflation on building materials prices in construction industry,” Journal of Building Engineering, p. 101485, 2020.

K. Amadeo, “Inflation, How It’s Measured and Managed,” 2020. [Online]. Available: https://www.thebalance.com/what-is-inflation-how-it-s-measured-and-managed-3306170. [Accessed: 11-Jun-2020].

Badan Pusat Statistik (BPS), “Inflasi Indonesia Menurut Kelompok Pengeluaran,” 2020. [Online]. Available: https://www.bps.go.id/statictable/2020/02/04/2083/inflasi-indonesia-menurut-kelompok-pengeluaran-2020.html. [Accessed: 11-Jun-2020].

B. Febriadi, Z. Zamzami, Y. Yunefri, and A. Wanto, “Bipolar function in backpropagation algorithm in predicting Indonesia’s coal exports by major destination countries,” IOP Conference Series: Materials Science and Engineering, vol. 420, no. 012087, pp. 1–9, 2018.

I. S. Purba et al., “Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

A. Wanto, M. Zarlis, Sawaluddin, and D. Hartama, “Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves in the Predicting Process,” in Journal of Physics: Conference Series, 2017, vol. 930, no. 1, pp. 1–7.

S. McKnight, A. Mihailov, and F. Rumler, “Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend,” Economic Modelling, vol. 87, no. May, pp. 383–393, 2020.

P. Pincheira-Brown, J. Selaive, and J. L. Nolazco, “Forecasting inflation in Latin America with core measures,” International Journal of Forecasting, vol. 35, no. 3, pp. 1060–1071, 2019.

D. Aparicio and M. I. Bertolotto, “Forecasting inflation with online prices,” International Journal of Forecasting, vol. 36, no. 2, pp. 232–247, 2020.

A. Wanto and J. T. Hardinata, “Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, pp. 1–8, 2020.

A. Wanto et al., “Forecasting the Export and Import Volume of Crude Oil , Oil Products and Gas Using ANN,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

A. Wanto et al., “Analysis of the Backpropagation Algorithm in Viewing Import Value Development Levels Based on Main Country of Origin,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.

G. W. Bhawika et al., “Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.




DOI: https://doi.org/10.30645/ijistech.v4i2.103

Refbacks

  • There are currently no refbacks.







Jumlah Kunjungan:

View My Stats

Published Papers Indexed/Abstracted By: