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All Journal JOIV : International Journal on Informatics Visualization The Indonesian Journal of Computer Science
Harianto, Fetrus Jari
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Performance Analysis Of Machine Learning Algorithms Using The Ensemble Method On Predicting The Impact Of Inflation On Indonesia's Economic Growth Abdulloh, Ferian Fauzi; Aminuddin, Afrig; Rahardi, Majid; Harianto, Fetrus Jari
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 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.
Linear Regression Algorithm Analysis to Predict the Effect of Inflation on the Indonesian Economy.: Analysis of the accuracy level of RMSE using Linear Regression Algorithm to Predict the Effect of Inflation on the Indonesian Economy. Harianto, Fetrus Jari; Abdulloh, Ferian Fauzi
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3224

Abstract

Tujuan penelitian ini adalah untuk melihat perkembangan penelitian mengenai dampak inflasi global terhadap perkembangan perekonomian Indonesia. Penelitian ini melihat hubungan antara pengaruh inflasi dan perkembangan ekonomi di Indonesia. Metode penelitian yang digunakan dalam penelitian ini adalah metode penelitian kuantitatif yang dimulai dari pengumpulan data, preprocessing, Proses Implementasi Algoritma Regresi Linier, dan pengujian model. Root mean square error (RMSE) adalah model regresi prediktif yang melihat seberapa akurat PDB tahunan berdasarkan tingkat inflasi tahunan. Dalam hal ini, nilai RMSE sekitar 0,60. Artinya, secara rata-rata model peramalan memiliki kesalahan sebesar 0,66 dalam memperkirakan nilai PDB tahunan. Semakin rendah nilai RMSE, semakin baik kinerja model karena menunjukkan kesalahan yang lebih kecil.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Majid Rahardi