Performance Analysis Of Machine Learning Algorithms Using The Ensemble Method On Predicting The Impact Of Inflation On Indonesia's Economic Growth

Ferian Abdulloh - Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Afrig Aminuddin - Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Majid Rahardi - Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Fetrus Harianto - Universitas Amikom Yogyakarta, Yogyakarta, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.4.2567

Abstract


The warning of a global recession expected in 2023 is currently the world's concern. Global financial institutions have raised interest rates to lower inflation, which has led to this problem. This study aims to evaluate the effect of interest rates and inflation on Indonesia's economic growth and compare the performance of machine learning models, specifically Random Forest and XGBoost, in analyzing the impact of inflation. A qualitative methodology was used for the literature survey, while the quantitative approach involved the implementation of machine learning algorithms using the Ensemble Method. The results show that Random Forest performs better than XGBoost in predicting the impact of inflation on economic growth, with MSE values of 0.799 and 0.864 and MAE of 0.576 and 0.619, respectively. In addition, the R-squared value of Random Forest 0.908 is also higher than that of XGBoost 0.901, indicating that the model can better explain the variation in the target data. The practical implication of this study is that the Random Forest model can be more effectively used in analyzing the impact of inflation on Indonesia's economic growth. Recommendations for future research include exploring other methods and using more extended time series to deepen the understanding of the relationship between interest rates, inflation, and economic growth.

Keywords


Machine Learning; Ensemble Method; Regression; impact of inflation

Full Text:

PDF

References


A. Fatoureh Bonab, “A review of inflation and economic growth,” Journal of Management and Accounting Studies, vol. 5, no. 02, pp. 1–4, Aug. 2019, doi: 10.24200/jmas.vol5iss02pp1-4.

W.-K. Chen, Linear Networks and Systems.

M. D. Verhagen, “A Pragmatist’s Guide to Using Prediction in the Social Sciences,” Socius, vol. 8, p. 237802312210817, Jan. 2022, doi: 10.1177/23780231221081702.

E. R. Gottlieb, M. Samuel, J. V. Bonventre, L. A. Celi, and H. Mattie, “Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit,” Adv Chronic Kidney Dis, vol. 29, no. 5, pp. 431–438, Sep. 2022, doi: 10.1053/j.ackd.2022.06.005.

S. Pawaskar, “Stock Price Prediction using Machine Learning Algorithms,” Int J Res Appl Sci Eng Technol, vol. 10, no. 1, pp. 667–673, Jan. 2022, doi: 10.22214/ijraset.2022.39891.

S. Dhali, M. Pati, S. Ghosh, and C. Banerjee, “An Efficient Predictive Analysis Model of Customer Purchase Behavior using Random Forest and XGBoost Algorithm,” in 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), IEEE, Sep. 2020, pp. 416–421. doi: 10.1109/ICCE50343.2020.9290576.

I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, pp. 99129–99149, 2022, doi: 10.1109/ACCESS.2022.3207287.

Y. Yang, “Unsupervised Ensemble Learning and Its Application to Temporal Data Mining: Keynote Address,” in 2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD), IEEE, Sep. 2021, pp. 1–1. doi: 10.1109/BCD51206.2021.9581431.

Z. , M. T. , & Y. Y. Zheng, Inflation and income inequality in a variety-expansion growth model with menu costs. Economics Letters. 2020.

H. Guirguis, V. B. Dutra, and Z. McGreevy, “The impact of global economies on US inflation: A test of the Phillips curve,” Journal of Economics and Finance, vol. 46, no. 3, pp. 575–592, Jul. 2022, doi: 10.1007/s12197-022-09583-x.

E. O. Akande, E. O. Akanni, O. F. Taiwo, J. D. Joshua, and A. Anthony, “Predicting inflation component drivers in Nigeria: a stacked ensemble approach,” SN Business & Economics, vol. 3, no. 1, p. 9, Dec. 2022, doi: 10.1007/s43546-022-00384-2.

B. S. , & A. A. R., “Comparison Studies Between Machine Learning Optimisation Technique on Predicting Concrete Compressive Strength,” 2021.

S. , & P. V Kapoor, A Simple and Fast Baseline for Tuning Large XGBoost Models. 2021.

Y. Zhang, J. Liu, and W. Shen, “A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications,” Applied Sciences, vol. 12, no. 17, p. 8654, Aug. 2022, doi: 10.3390/app12178654.

S. Chakraborty and S. Bhattacharya, “Application of XGBoost Algorithm as a Predictive Tool in a CNC Turning Process,” Reports in Mechanical Engineering, vol. 2, no. 2, pp. 190–201, Sep. 2021, doi: 10.31181/rme2001021901b.

A. Rasha, “THE IMPACT OF INFLATION ON REAL TAX LIABILITIES IN THE WORLD ECONOMY,” EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA, vol. 5/2, no. 125, pp. 213–216, 2022, doi: 10.36871/ek.up.p.r.2022.05.02.033.

R. VP, “A Study on Inflation,” SSRN Electronic Journal, 2021, doi: 10.2139/ssrn.3816410.

Q. Lhoest et al., “Datasets: A Community Library for Natural Language Processing,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Stroudsburg, PA, USA: Association for Computational Linguistics, 2021, pp. 175–184. doi: 10.18653/v1/2021.emnlp-demo.21.

M. yasin Yasin, “ANALYSIS OF REGIONAL ORIGINAL INCOME LEVELS IN REGIONAL FINANCIAL PERFORMANCE ON ECONOMIC GROWTH IN EAST JAVA PROVINCE,” Archives of Business Research, vol. 7, no. 10, pp. 222–229, Nov. 2019, doi: 10.14738/abr.710.7320.

С. А. Аббасова, Д. З. Гусейнов, and А. А. Гасанов, “ОСНОВНІ ТЕНДЕНЦІЇ ЕКОНОМІЧНОГО ЗРОСТАННЯ ТА ЦИФРОВОЇ ГЛОБАЛІЗАЦІЇ,” TIME DESCRIPTION OF ECONOMIC REFORMS, no. 4, pp. 60–66, Jan. 2023, doi: 10.32620/cher.2022.4.09.

G. Jałtuszyk, “Inflation, Global Financial Crisis, and COVID-19 Pandemic,” SSRN Electronic Journal, 2022, doi: 10.2139/ssrn.4186354.

F. J. Harianto and F. F. Abdulloh, “Linear Regression Algorithm Analysis to Predict the Effect of Inflation on the Indonesian Economy.,” Indonesian Journal of Computer Science, vol. 12, no. 4, Aug. 2023, doi: 10.33022/ijcs.v12i4.3224.

P. Mahalingam, D. Kalpana, S. Sendhilkumar, and T. Thyagarajan, “Prefatory data analysis approach to synthetically generated pneumatic actuator data set,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 236, no. 10, pp. 1807–1818, Nov. 2022, doi: 10.1177/09596518221117326.

J. John, Outlier Detection and Spatial Analysis Algorithms. 2021.

T. N. Rincy and R. Gupta, “Ensemble Learning Techniques and its Efficiency in Machine Learning: A Survey,” in 2nd International Conference on Data, Engineering and Applications (IDEA), IEEE, Feb. 2020, pp. 1–6. doi: 10.1109/IDEA49133.2020.9170675.

]N. Beheshti, Random Forest Regression,. Medium, 2022.

L. Capitaine, R. Genuer, and R. Thiébaut, “Random forests for high-dimensional longitudinal data,” Stat Methods Med Res, vol. 30, no. 1, pp. 166–184, Jan. 2021, doi: 10.1177/0962280220946080.

S. M. Robeson and C. J. Willmott, “Decomposition of the mean absolute error (MAE) into systematic and unsystematic components,” PLoS One, vol. 18, no. 2, p. e0279774, Feb. 2023, doi: 10.1371/journal.pone.0279774.

T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geosci Model Dev, vol. 15, no. 14, pp. 5481–5487, Jul. 2022, doi: 10.5194/gmd-15-5481-2022.

C. Onyutha, “A hydrological model skill score and revised R-squared,” Hydrology Research, vol. 53, no. 1, pp. 51–64, Jan. 2022, doi: 10.2166/nh.2021.071.

F. He, “iPhone Sales Prediction Based on Multilinear Regression Model: Evidence from Statista,” in 2023 IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE), IEEE, Aug. 2023, pp. 341–346. doi: 10.1109/ICSECE58870.2023.10263525.

S. B. R. V, S. T, G. R, and R. R. Gondkar, “Assessing Academic Performance Using Ensemble Machine Learning Models,” in 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), IEEE, Aug. 2023, pp. 917–924. doi: 10.1109/ICCPCT58313.2023.10245216.