Peramalan Data Indeks Harga Konsumen Berbasis Time Series MultivariateMenggunakan Deep Learning

Peramalan Data Indeks Harga Konsumen Berbasis Time Series MultivariateMenggunakan Deep Learning

consumer price index, time series forecasting, deep learning

Multivariate Time Series based forecasting is a type of forecasting that has more than one criterion changes from time to time that it can forecast based on historical patterns of data sequences.The Consumer Price Index(CPI)issued regularly every month by the Statistics Indonesiacalculated based on data observations. This study is a development of previous research that only used on type of algorithm to predict CPI value resulting poor of accuracy due to lack of architecture variations testing. This study developed a CPI forecasting model with a new approachabout usingseveral types of deep learning algorithms, namely LSTM, Bidirectional LSTM, and Multilayer Perceptron with architectural variations of the number of neurons and epochs. Furthermore, this study adapt ADDIE model of Research and Development method. Based on the results, the best accuracy is obtained from the LSTM Bidirectional with 10 neurons and 2000 epochresulting 3,519 of RMSE value.Meanwhile, based on the average RMSE value for the whole test, LSTM gets the smallest average of RMSE followed Bidirectional LSTM andMultilayer Perceptronwith the RMSE value4,334, 5,630, 6,304 respectively

Soffa Zahara1, Sugianto2

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/20

Universitas Islam Majapahi

13 Februari 2021

Fajar bagus W

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Indonesia

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