The Utilization of Deep Learning in Forecasting The Inflation Rate of Education Costs in Malang

Authors

  • Ashri Shabrina Afrah UIN Maulana Malik Ibrahim Malang, Malang, Indonesia
  • Merinda Lestandy Universitas Muhammadiyah Malang, Malang, Indonesia
  • Juwita P. R. Suwondo Universitas Merdeka Malang, Malang, Indonesia

DOI:

https://doi.org/10.31961/eltikom.v7i1.729

Keywords:

deep learning, long short-term memory, education cost, inflation

Abstract

The public needs information about the predicted inflation rate for education costs to manage family finances and prepare education funds. This information is also beneficial for the government to create policies in education. Malang is one of the educational cities in Indonesia, but research on the prediction of the inflation rate of education costs in the city still needs to be made available. Besides, the researchers have yet to find previous studies on forecasting that used the specific inflation rate for education costs in Indonesia by applying the Deep Learning method, especially those using the Consumer Price Index (CPI) data for the Education Expenditure Group. This research aims to develop a model to forecast the inflation of education costs in Malang using the Deep Learning Method. This research was conducted using Consumer Price Index (CPI) data for the Education Expenditure Group in Malang during 1996-2021s taken from the Central Bureau of Statistics (BPS) Malang. The forecasting method used is the Long and Short-Term Memory (LSTM) method, which is a development of the Recurrent Neural Network (RNN). The results showed that it achieved the best accuracy by a model with one hidden layer and four hidden nodes, namely MAPE=2.8765% and RMSE=8.37.

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Published

30-06-2023

How to Cite

[1]
Afrah, A.S. et al. 2023. The Utilization of Deep Learning in Forecasting The Inflation Rate of Education Costs in Malang. Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer. 7, 1 (Jun. 2023), 93–103. DOI:https://doi.org/10.31961/eltikom.v7i1.729.

Issue

Vol. 7 No. 1 (2023)

Section

Articles

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