Performance Analysis of Machine Learning Algorithms Using the Ensemble Method in Predicting the Impact of Inflation on Indonesia's Economic Growth

Aug 5, 2026

Rising global recession concerns in 2023 have driven financial institutions to increase interest rates in an effort to reduce inflation, affecting macroeconomic conditions. This study evaluates how interest rates and inflation influence Indonesia’s economic growth and compares machine learning models—Random Forest and XGBoost—for analyzing inflation’s impact. A literature survey supports qualitative framing, while an ensemble-based quantitative implementation trains and evaluates regression models. Results indicate Random Forest outperforms XGBoost with lower MSE and MAE and a higher R-squared, suggesting stronger explanatory power for target variation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Inflation, interest rates, and economic growth

Machine learning and ensemble regression

Related research and motivation

INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION [journal homepage :](journal homepage : www.joiv.org/index.php/joiv)[ www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv) Performance Analysis of Machine Learning Algorithms Using the Ensemble Method in Predicting the Impact of Inflation on Indonesia's Economic Growth Ferian Fauzi Abdulloh a,*, Afrig Aminuddin a, Majid Rahardi a, Fetrus Jari Hariantoa a Faculty of Computer Science, Universitas Amikom Yogyakarta, Yogyakarta, Indonesia Corresponding author:*[ferian@amikom.ac.id](ferian@amikom.ac.id) 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. Manuscript received 19 Feb. 2024; revised 20 Apr. 2024; accepted 31 May 2024. Date of publication 31 Dec. 2024. International Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License. I. INTRODUCTION High inflation rates and sluggish economic growth are common economic issues developing nations face. It is common to link inflationary features to domestic variables. Economic growth rates can be considerably raised by investing in market output, infrastructure, education, and preventive healthcare, but not always by spending on investments [1]. Furthermore, other factors, including interest and unemployment, are also linked to inflation. Many scholars have recently contended that globalization has made the global economy have a larger influence on inflation production than domestic variables. Nations worldwide are taking notice of the warnings about a worldwide recession in 2023. The world's financial institutions are to blame for this issue since they are increasing interest rates to rein in inflation. Although many economists argue that Indonesia is still far from entering a recession, this does not mean the country will be immune to the effects of the global economic slump [2]. Prediction is a straightforward and powerful technique for assessing a model's predictive power that social scientists can use to their advantage in their empirical research [3]. In machine learning, an artificial intelligence field, computers are taught to use intricate mathematical algorithms to anticipate outcomes or spot patterns in data [4]. Machine learning techniques, including XGBoost Regression and Random Forest Regression, can forecast economic growth and give governments helpful information about how to plan for inflation [5]. Regression analysis is one of the procedures most often utilized by machine learning models. Using Bootstrap Aggregation, Random Forest

FAQs

What is the main goal of this study?

To evaluate the effect of interest rates and inflation on Indonesia’s economic growth and to compare the predictive performance of Random Forest and XGBoost in estimating the impact of inflation.

Which model performs better for predicting inflation’s impact on economic growth?

Random Forest performs better than XGBoost, with lower MSE and MAE and a higher R-squared value, indicating improved prediction accuracy and better explanation of target variation.

What methodology is used to build and assess the predictive models?

The study uses qualitative literature survey for framing and a quantitative approach to implement machine learning algorithms using the ensemble method, followed by performance evaluation using regression metrics.

Aug 5, 2026

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