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Abstract

Asia’s GDP experienced the most drastic decline during the COVID-19 compared to other economic crises. This study collected data on economic indicators for each province/city to observe economic growth in Indonesia, such as Gross Regional Domestic Product (GRDP), unemployment rate, and economic growth. The clustering method on time series data found several provinces/cities with similar economic growth patterns to observe the pandemic's impact on their economies. Knowing the pattern of economic growth will help the regulation holder support provinces with the right policy. For this purpose, we utilized the Dynamic Time Warping (DTW) distance with the k-medoids procedure. The DTW is an algorithm for measuring the similarity between two temporal sequences. The clustering of the three economic indicators had three clusters with the most optimal validation index. Each cluster had almost the same pattern since the trend tended to increase from before the pandemic and then decrease during the pandemic. The decrease in GRDP was less significant than the minimal data on GRDP that happened before the pandemic. Most provinces had negative economic growth during the pandemic, which skyrocketed even for the first quarter of 2023, almost the same as before the pandemic.

Keywords

clustering analysis dynamic time warping economic growth k-medoids

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How to Cite
Primandari, A. H., & Kusuma Arum, W. (2024). Analyzing the Impact of the Pandemic on Indonesia’s Economic Growth Using Dynamic Time Warping . Enthusiastic : International Journal of Applied Statistics and Data Science, 4(1), 71–84. https://doi.org/10.20885/enthusiastic.vol4.iss1.art7
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