Python-Powered Precision: Unraveling Consumer Price Index Trends in Makassar City through a Duel of Long Short-Term Memory and Gated Recurrent Unit Models

Abd. Rahman; First Wanita; Rose Arisha; Aditya Halim Perdana Kusuma; Zulmy Azhary

doi:10.56134/jst.v2i2.44

Authors

Abd. Rahman Universitas Teknologi Akba Makassar
First Wanita Universitas Teknologi Akba Makassar
Rose Arisha University of Malaya
Aditya Halim Perdana Kusuma Universitas Muslim Indonesia
Zulmy Azhary Universitas Teknologi Akba Makassar, Indonesia

Keywords:

Prediction, Long Short Term Memory, Gated Recurrent Unit, Consumer price index, Mean Absolute Error
doi

https://doi.org/10.56134/jst.v2i2.44

Abstract

This research aims to carry out a predictive analysis of the Consumer Price Index in the city of Makassar to anticipate possible impacts on inflation and deflation in the future. The Consumer Price Index is an indicator that can be used as a basis for measuring changes in the prices of goods and services purchased by consumers which have an impact on inflation in a region. The CPI is very useful for knowing the level of increase in prices, services, and income, as well as measuring the amount of production costs. This data was obtained through the official website of the Central Statistics Agency (BPS) for the Makassar city area. The methods used in this research are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results of this research show that based on analysis and testing, the LSTM model has an MAE of 1.0849 and the GRU model has an MAE of 0.9915, which shows that there is no significant difference between the two methods and both methods can work very well, however, The lowest error value was obtained in the GRU model using a 70:30 dataset ratio, 9 number of sequences, 16 neurons in hidden layer 1 and 32 neurons in hidden layer 2, and 1000 number of epochs.

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References

Alkahfi, I., & Chiuloto, K. (2021). Penerapan Model Gated Recurrent Unit Pada Masa Pandemi Covid-19 Dalam Melakukan Prediksi Harga Emas Dengan Menggunakan Model Pengukuran Mean Square Error. Snastikom Ke, 8, 225–232.

A, M. A., J, P. I., & S, R. M. (2023). Forecasting Consumer Price Index (CPI) Using Deep Learning and Hybrid Ensemble Technique. 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 1–8. https://doi.org/10.1109/ACCAI58221.2023.10200153.

Ardi, P. H. (2023). Peramalan permintaan ekspor nonmigas Indonesia menggunakan long short term memory. Universitas Islam Negeri Maulana Malik Ibrahim.

Arif, D. (2014). Pengaruh produk domestik bruto, jumlah uang beredar, inflasi dan BI rate terhadap indeks harga saham gabungan di Indonesia periode 2007-2013. Jurnal Ilmiah Ekonomi Bisnis, 19(3).

Banerjee, T., Sinha, S., & Choudhury, P. (2022). Long term and short term forecasting of horticultural produce based on the LSTM network model. Applied Intelligence, 1–31.

BPS. (2023). Badan Pusat Statistik Kota Makassar. Badan Pusat Statistik Kota Makassar.

Cahyono, R. E., Sugiono, J. P., & Tjandra, S. (2019). Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen. JTIM: Jurnal Teknologi Informasi Dan Multimedia, 1(2), 106–116.

Carnegie, M. D. A., & Chairani, C. (2023). Perbandingan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Memprediksi Curah Hujan. JURNAL MEDIA INFORMATIKA BUDIDARMA, 7(3), 1022–1032.

Cui, Q., Rong, S., Zhang, F., & Wang, X. (2023). Exploring and predicting China’s consumer price index with its influence factors via big data analysis. Journal of Intelligent & Fuzzy Systems, Preprint, 1–11.

Egi Nuraini, M. (2022). Perbandingan Metode Long-Short Term Memory Dan Gated Recurrent Unit Untuk Memprediksi Nilai Ekspor Migas-Nonmigas Di Indonesia.

Fitriyana, D. E., Triyani, I., Zahra, N. H., Oktaviani, R. S., & LP3I, S. A. P. J.--P. (2023). Riset Akunttansi dan Bisnis.

Hamdan. (2018). Analisis faktor-faktor yang mempengaruhi Inflasi menurut Indeks Harga Konsumen dan Implikasinya terhadap pertumbuhan ekonomi di provinsi Kepulauan Bangka Belitung. JEM JURNAL EKONOMI DAN MANAJEMEN, 3(1), 89–101.

Iman, F. N., & Wulandari, D. (2023). Prediksi Harga Saham Menggunakan Metode Long Short Term Memory. LOGIC: Jurnal Ilmu Komputer Dan Pendidikan, 1(3), 601–616.

Johny, K., Pai, M. L., & Adarsh, S. (2022). A multivariate EMD-LSTM model aided with Time Dependent Intrinsic Cross-Correlation for monthly rainfall prediction. Applied Soft Computing, 123, 108941.

KILINÇ, H. Ç., & Polat, A. (2022). Comparison of Long-Short Term Memory and Gated Recurrent Unit Based Deep-Learning Models in Prediction of Streamflow Using Machine Learning. Avrupa Bilim ve Teknoloji Dergisi, 38, 158–164.

Maharani, N. E. (2022). Mengenal Indeks Harga: Pengertian, Tujuan dan Jenis-Jenisnya. Tirto.Id.

Meliana, C., & others. (2021). Perbandingan Metode Long Short Term Memory (LSTM) DAN Genetic Algorithm-Long Short Term Memory (GA-LSTM) Pada Peramalan Polutan Udara. UNIVERSITAS MUHAMMADIYAH SEMARANG.

Nagakusuma, J., Palit, H. N., & Juwiantho, H. (2022). Prediksi Penjualan Pada Data Penjualan Perusahaan X Dengan Membandingkan Metode GRU, SVR, DAN SARIMAX. Jurnal Infra, 10(2), 319–325.

Ningsih, D., & Andiny, P. (2018). Analisis pengaruh inflasi dan pertumbuhan ekonomi terhadap kemiskinan di Indonesia. Jurnal Samudra Ekonomika, 2(1), 53–61.

Noor, H. S., & Komala, C. (2019). Analisis Indeks Harga Konsumen (IHK) Menurut Kelompok Pengeluaran Nasional Tahun 2018. Jurnal Perspektif, 3(2), 110. https://doi.org/10.15575/jp.v3i2.48

Paputungan, C. K. N., & Jacobus, A. (2021). Analisis Sentimen Pengguna Sosisal Media Menggunakan Metode Long Short Term Momory. Jurnal Teknik Elektro Dan Komputer, 10(2), 99–106.

Rahmawati, W. E., & Setyobudi, S. (2023). Analisis Inflasi-Kurs dan BI Rate Terhadap Indeks Harga Saham Gabungan di Bursa Efek Indonesia Tahun 2015-2019. Jurnal Akuntansi Dan Teknologi Keuangan, 1(2), 64–82.

Raya, B., Utara, C. I., Bank, D., Bank, L., Bank, N., & Mint, R. (2022). Pound sterling. Sumber, 4.

Sumantri, F., & Latifah, U. (2019). Faktor-faktor yang Mempengaruhi Indeks Harga Konsumen. Sekretari Dan Manajemen, 3(1), 25–34.

Sautomo, S., Pardede, H. F., & others. (2021). Prediksi belanja pemerintah Indonesia menggunakan long short-term memory (LSTM). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 99–106.

Sen, S., Sugiarto, D., & Rochman, A. (2020). Komparasi metode multilayer perceptron (MLP) dan long short term memory (LSTM) dalam peramalan harga beras. Ultimatics: Jurnal Teknik Informatika, 12(1), 35–41.

Seth, S., Singh, G., & Kaur, K. (2022). Smart Intrusion Detection System Using Deep Neural Network Gated Recurrent Unit Technique. ICCCE 2021: Proceedings of the 4th International Conference on Communications and Cyber Physical Engineering, 285–293.

Siregar, S. R., & Widyasari, R. (2023). Peramalan Harga Crude Oil Menggunakan Metode Long Short-Term Memory (LSTM) Dalam Recurrent Neural Network (RNN). Jurnal Lebesgue: Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 4(3), 1478–1489.

Tussifah, S. A. (2023). Analisis perbandingan kinerja model ARIMA, LSTM dan GRU pada stock price forecasting. Fakultas Sains dan Teknologi Universitas Islam Negeri Syarif Hidayatullah~….

Wanto, A., & Windarto, A. P. (2017). Analisis prediksi indeks harga konsumen berdasarkan kelompok kesehatan dengan menggunakan metode backpropagation. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 2(2), 37–43.

Wijaya, A. J., Swastika, W., & Kelana, O. H. (2021). Prediksi Harga Foreign Exchange Mata Uang EUR/USD dan GBP/USD Menggunakan Long Short-Term Memory. Sainsbertek Jurnal Ilmiah Sains & Teknologi, 2(1), 16–31.

Yudistira, N., Alfiansih, L. M. D., Andriyani, N. I., Essayem, W., Nurdian, I. W., Maghfiroh, N. A., & Maulida, N. (2023). Prediksi Deret Waktu Menggunakan Deep Learning. Universitas Brawijaya Press.