Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia

Alwan Rahmana Subian; Drajat Ali Mulkan; Haidar Hilmy Ahmady; Fitri Kartiasih

doi:10.32520/stmsi.v13i1.3445

Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia

Alwan Rahmana Subian, Drajat Ali Mulkan, Haidar Hilmy Ahmady, Fitri Kartiasih

Abstract

The success of an economy can bring significant benefits to a country and its society. One way to measure economic growth is by looking at the value of gross domestic product (GDP). The value of a country's GDP is influenced by many factors, including inflation, exports, and imports. Therefore, predicting future economic growth requires forecasting the value of GDP. GDP forecasts are crucial as they provide information about the economic development of a country over a specific period of time. By forecasting GDP, governments and investors can make informed decisions to optimize profits or minimize risks when investing or doing business in a country. This research aims to forecast Indonesia's GDP for the second, third, and fourth quarters of 2023 using the best models from machine learning or deep learning methods. Forecasts are made for each method with and without additional variables. The results indicate that the SimpleRNN algorithm from deep learning without additional variables is the method with the smallest RMSE and MAPE for GDP forecasting. Therefore, the best method used to forecast GDP is the SimpleRNN algorithm, and the forecasted GDP values for Indonesia's second, third, and fourth quarters are 5.350.840,00, 5.483.895,00, and 5.610.077,50 billion rupiah.

Full Text:

PDF

References

Adwendi, S. J., & Kartiasih, F. (2016). Penggunaan Error Correction Mechanism dalam Analisis Pengaruh Investasi Langsung Luar Negeri Terhadap Pertumbuhan Ekonomi Indonesia. Statistika: Journal of Theoretical Statistics and Its Applications, 16(1), 17–27. https://doi.org/10.29313/jstat.v16i1.1767

Barlow, H. B. (1989). Unsupervised learning. Neural computation, 1(3), 295-311.

Bawono, B., & Wasono, R. (2019, 3). Perbandingan Metode Random Forest dan Naïve Bayes untuk Klasifikasi Debitur Berdasarkan Kualitas Kredit. Seminar Nasional Edusaintek (hal. 343-348).

Berata, I. K. O. (2014). Panduan Praktis Ekspor Impor. RAS.

Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.

Callen, T. (2022). Gross Domestic Product: An Economy’s All. International Monetary Fund, Finance and Development Publishing.

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.

Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

Cunningham, P., Cord, M., & Delany, S. J. (2008). Supervised learning. Machine learning techniques for multimedia: case studies on organization and retrieval, 21-49.

Cryer, J. D. (1986). Time series analysis (Vol. 286). Boston: Duxbury Press.

Dayan, P., Sahani, M., & Deback, G. (1999). Unsupervised learning. The MIT encyclopedia of the cognitive sciences, 857-859.

Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and trends® in signal processing, 7(3–4), 197-387.

Divya, K. H., & Devi, V. R. (2014). A Study on Predictors of GDP: Early Signals. Symbiosis Institute of Management Studies Annual Research Conference (SIMSARC 13), 375-382.

El Naqa, I., & Murphy, M. J. (2015). What is machine learning? (pp. 3-11). Springer International Publishing.

Gharte, T., Patil, H., & Gawade, S. (2022). GDP Prediction and Forecasting using Machine Learning.

Ginting, A. M., & Dewi, G. P. (2013). Pengaruh Pertumbuhan Ekonomi dan Pertumbuhan Sektor Keuangan terhadap Pengurangan Kemiskinan di Indonesia. Jurnal Ekonomi & Kebijakan Publik, 4(2), 117-130.

Guo, T., Xu, Z., Yao, X., Chen, H., Aberer, K., & Funaya, K. (2016). Robust Online Time Series Prediction with Recurrent Neural Networks. 2016 IEEE International Conference on Data Science and Advance Analytics, 816-825.

Guenette, J. D., Kose, M. A., & Sugawara, N. (2022). Is a Global Recession Imminent?. The World Bank Press Release.

Gujarati, D. N. (2022). Basic econometrics. Prentice Hall.

Hawari, R., & Kartiasih, F. (2017). Kajian Aktivitas Ekonomi Luar Negeri Indonesia Terhadap Pertumbuhan Ekonomi Indonesia Periode 1998-2014. Media Statistika, 9(2), 119. https://doi.org/10.14710/medstat.9.2.119-132

Hewamalage, H., Bergmeir, C., & Bandara, K. (2020). Recurrent Neural Network for Time Series Forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 1-40.

Johansen, S. (1985). The Mathematical Structure of Error Correction Models. JOHNS HOPKINS UNIV BALTIMORE MD DEPT OF MATHEMATICAL SCIENCES.

Jonathan, D. C., & Kung-Sik, C. (2008). Time series analysis with applications in R.

Karlina, B. (2017). Pengaruh Tingkat Inflasi, Indeks Harga Konsumen Terhadap PDB di Indonesia Pada Tahun 2011-2015. Jurnal Ekonomika Dan Manajemen, 6(1), 16-27.

Kartiasih, F. (2019a). Dampak Infrastruktur Transportasi Terhadap Pertumbuhan Ekonomi Di Indonesia Menggunakan Regresi Data Panel. Jurnal Ilmiah Ekonomi Dan Bisnis, 16(1), 67–77. https://doi.org/10.31849/jieb.v16i1.2306

Kartiasih, F. (2019b). Transformasi Struktural dan Ketimpangan Antardaerah di Provinsi Kalimantan Timur. INOVASI: Jurnal Ekonomi, Keuangan Dan Manajemen, 15(1), 105–113. https://doi.org/https://doi.org/10.30872/jinv.v15i1.5201

Kumari, A., & Sood, M. (2021). Implementation of SimpleRNN and LSTMs based prediction model for coronavirus disease (Covid-19). In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012015). IOP Publishing.

Kusumasari, A., & Kartiasih, F. (2017). Aglomerasi Industri Dan Pengaruhnya Terhadap Pertumbuhan Ekonomi Jawa Barat 2010-2014. Jurnal Aplikasi Statistika & Komputasi Statistik, 9(2), 28–41. https://doi.org/https://doi.org/10.34123/jurnalasks.v9i2.143

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.

Maccarone, G., Morelli, G., & Spadaccini, S. (2021). GDP Forecasting: Machine Learning, Linear or Autoregression. Frontiers in Artificial Intelligence, (5), 1-9.

Malik, S., Harode, R., & Kunwar, A. S. (2020). XGBoost: A deep dive into boosting. no. February.

Manullang, S. (2018). Analisis Runtun Waktu Menggunakan Model Markov. Generasi Kampus, 11(1).

Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press.

Nabillah, I., & Ranggadara, I. (2020). Mean Absolute Percentage Error untuk Evaluasi Hasil Prediksi Komoditas Laut. Journal of Information System, 5(2), 250-255.

Ningsih, Y. P., & Kartiasih, F. (2019). Dampak Guncangan Pertumbuhan Ekonomi Mitra Dagang Utama terhadap Indikator Makroekonomi Indonesia. Jurnal Ilmiah Ekonomi Dan Bisnis, 16(1), 78–92. https://doi.org/https://doi.org/10.31849/jieb.v16i1.2307

OECD. (2016). OECD Factbook 2015-2016: Economic, Environmental and Social Statistics. Paris. OECD Publishing.

Permana, S. H., & Rivani, E. (2013). Pengaruh produk domestik bruto, inflasi, infrastruktur, dan risiko politik terhadap investasi langsung asing di Indonesia. Jurnal Ekonomi & Kebijakan Publik, 4(1), 75-87.

Priyanka, Kumari, A., & Sood, M. (2021). Implementation of SimpleRNN and LSTMs Based Prediction Model for Coronavirus Disease (Covid-19). IOP Conference Series: Materials Science and Engineering. 1022. 012015.

Putra, J. W. G. (2019). Pengenalan konsep pembelajaran mesin dan deep learning. Tokyo. Jepang.

Raswatie, F. D. (2014). Hubungan Ekspor-Produk Domestik Bruto (PDB) di Sektor Pertanian Indonesia. Journal of Agriculture, Resource and Environmental Economics, 1(1), 28-42.

Richardson, A., & Mulder, T. (2018). Nowcasting New Zealand GDP using machine learning algorithms.

Risa, M. (2018). Ekspor dan impor. Deepublish.

Santosa, A. B. (2017). Analisis Inflasi di Indonesia.

Silitonga, D. (2021). Pengaruh Inflasi terhadap Produk Domestik Bruto (PDB) Indonesia pada Periode Tahun 2010-2020. Jurnal Manajemen Bisnis, 24(1), 111-122.

Sudarmawan, B. N. (2022). The Correlation of International Trade and Growth in Indonesia. Jurnal Ekonomi dan Pembangunan, 30(1), 31-46.

Sukono, S., Albra, W., Zulham, T., Majid, I., Saputra, J., Subartini, B., & Thalia, F. (2019). The effect of gross domestic product and population growth on CO2 emissions in Indonesia: An application of the ant colony optimisation algorithm and cobb-douglas model. International Journal of Energy Economics and Policy, 9(4), 313.

Suparmoko. (2000). Pengantar Ekonomika Makro (Edisi 4). Yogyakarta: BPFE.

Time Series Analysis: Definition, Types & Techniques. (n.d.). Tableau. Retrieved May 10, 2023, from https://www.tableau.com/learn/articles/time-series-analysis

Tjandrasa, B. B., & Dewi, V. I. (2022). The Model of GDP Growth in ASEAN-4 Countries: Control of Corruption as an Intervening Variable. Journal of Economics, Business, & Accountancy Ventura, 25(1), 1-9.

Tjukanov, T. (2011). Gross Domestic Product as a Modern-day Economic Indicator. Bachelor Thesis. Helsinki Metropolia University of Applied Sciences.

Tkacz, G., & Hu, S. (1999). Forecasting GDP Growth Using Artificial Neural Networks. Bank of Canada Working Paper 99-3.

Van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine learning, 109(2), 373-440.

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53, 5929-5955.

Verma, P., & Diamantidis, S. (2021). What is reinforcement learning?. Synopsys, updated April, 27.

Wasono, R. (2022). Perbandingan Metode Random Forest dan naive bayes untuk Klasifikasi Debitur Berdasarkan Kualitas Kredit.

What is Model Evaluation? | Domino Data Science Dictionary. (n.d.). Domino Data Lab. Retrieved July 16, 2023, from https://www.dominodatalab.com/data-science-dictionary/model-evaluation

Willmott, C. J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63(11), 1309-1313.

Yoon, J. (2020). Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach. Springer Computational Economics Article, 57, 247-265.

Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342.

DOI: https://doi.org/10.32520/stmsi.v13i1.3445

Article Metrics

Abstract view : 102 times
PDF - 43 times

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Username
Password
Remember me