Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning

Soffa Zahara; Sugianto

doi:10.29207/resti.v5i1.2562

Soffa Zahara Universitas Islam Majapahit
Sugianto Universitas Islam Majapahit

DOI: https://doi.org/10.29207/resti.v5i1.2562

Keywords: consumer price index, time series forecasting, deep learning.

Abstract

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 Indonesia calculated 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 approach about using several 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 epoch resulting 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 and Multilayer Perceptron with the RMSE value 4,334, 5,630, 6,304 respectively.

Downloads

Download data is not yet available.

References

B. Karlina, “Pengaruh Tingkat Inflasi, Indeks Harga Konsumen Terhadap PDB di Indonesia Pada Tahun 2011-2015,” J. Ekon. dan Manaj., vol. 6, no. 1, pp. 2252–6226, 2017, [Online]. Available: http://fe.budiluhur.ac.id/wp-content/uploads/2017/08/b.-berlian.pdf.

Departemen Statistik, “Indeks Harga Konsumen,” Metadata, no. March, pp. 3–6, 2016.

Listyowati and B. Sutidjo, “Pemodelan Indeks Harga Konsumen (IHK),” J. Sains dan Seni Pomits, vol. 2, no. 2, 2013.

Y. Yin and P. Shang, “Forecasting traffic time series with multivariate predicting method,” Appl. Math. Comput., vol. 291, pp. 266–278, 2016, doi: 10.1016/j.amc.2016.07.017.

A. Patterson, Josh and Gibson, Deep learning : A Practitioners Approach, vol. 29, no. 7553. CA: O’Reilly Media, 2017.

O. Al Qasem, M. Akour, and M. Alenezi, “The Influence of Deep Learning Algorithms Factors in Software Fault Prediction,” IEEE Access, vol. 8, pp. 63945–63960, 2020, doi: 10.1109/ACCESS.2020.2985290.

M. Nabipour, P. Nayyeri, H. Jabani, S. Shahab, and A. Mosavi, “Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data ; a comparative analysis on the Tehran stock exchange,” vol. XX, 2020, doi: 10.1109/ACCESS.2020.3015966.

J. Gozalvez, “A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction,” vol. 8, 2020, doi: 10.1109/ACCESS.2020.2994415.

I. A. Budiastuti, S. M. S. Nugroho, and M. Hariadi, “Predicting daily consumer price index using support vector regression method,” QiR 2017 - 2017 15th Int. Conf. Qual. Res. Int. Symp. Electr. Comput. Eng., vol. 2017-Decem, pp. 23–28, 2017, doi: 10.1109/QIR.2017.8168445.

M. Rohmah, “Meramal Indeks Harga Konsumen Kabupaten di Jawa Timur dengan Metode Support Vector Regression Data Mining,” no. x, pp. 30–36, 2019.

S. Zahara, Sugianto, and M. Bahril Ilmiddafiq, “Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 357–363, 2019, doi: 10.29207/resti.v3i3.1086.

L. Qadrini, A. Asrirawan, N. Mahmudah, M. Fahmuddin, and I. F. Amri, “Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung,” J. Mat. Stat. dan Komputasi, vol. 17, no. 2, pp. 166–177, 2020, doi: 10.20956/jmsk.v17i2.11803.

A. Battou, O. Baz, and D. Mammass, “Learning Design approaches for designing Learning environments: A comparative study,” Int. Conf. Multimed. Comput. Syst. -Proceedings, vol. 0, pp. 206–211, 2017, doi: 10.1109/ICMCS.2016.7905666.

H. Daoud and M. Bayoumi, “Efficient Epileptic Seizure Prediction based on Deep Learning,” IEEE Trans. Biomed. Circuits Syst., vol. PP, no. c, p. 1, 2019, doi: 10.1109/TBCAS.2019.2929053.

K. A. Althelaya and S. Mohammed, “Evaluation of Bidirectional LSTM for Short- and Long-Term Stock Market Prediction,” pp. 151–156, 2018.