Article Sidebar

Download
PDF
Statistic
Read Counter : 116 Download : 96

Main Article Content

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

Article Details

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
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
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. “Covid-19 Pandemic and the Asia-Pacific Region: Lowest Growth Since the 1960s,” IMF. Access date: 23 November 2023. [Online]. Available: https://www.imf.org/en/Blogs/Articles/2020/04/15/blog-covid-19-pandemic-and-the-asia-pacific-lowest-growth-since-the-1960s
  2. S. Susilawati, R. Falefi, and A. Purwoko, “Impact of COVID-19’s Pandemic on the Economy of Indonesia,” Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences, Vol. 3, No. 2, pp. 1147–1156, May 2020, doi: 10.33258/birci.v3i2.954.
  3. O. Akinsomi, N. Mkhabela, and M. Taderera, “The Role of Macro-Economic Indicators in Explaining Direct Commercial Real Estate Returns: Evidence From South Africa,” Journal of Property Research, Vol. 35, No. 1, pp. 28–52, Jan. 2018, doi: 10.1080/09599916.2017.1402071.
  4. N.A. Karim, S.M.S.J. Al-Habshi, and M. Abduh, “Macroeconomics Indicators and Bank Stability: A Case of Banking in Indonesia,” Buletin Ekonomi Moneter dan Perbankan, Vol. 18, No. 4, pp. 431–448, Jul. 2016, doi: 10.21098/bemp.v18i4.609.
  5. M.A. Iswara, “1.2 Million Indonesian Workers Furloughed, Laid off as COVID-19 Crushes Economy,” The Jakarta Post. Access date: 11 August 2023. [Online]. Available: https://www.thejakartapost.com/news/2020/04/09/worker-welfare-at-stake-as-covid-19-wipes-out-incomes.html
  6. “Daftar Perusahaan yang PHK Karyawan Gegara Covid-19,” CNN Indonesia. Access date: 29 August 2023. [Online]. Available: https://www.cnnindonesia.com/ekonomi/20210804092131-92-676136/daftar-perusahaan-yang-phk-karyawan-gegara-covid-19
  7. O. Rizky, M. Fajar, O.R. Prasetyo, and S. Nonalisa, “Forecasting Unemployment Rate in the Time of COVID-19 Pandemic Using Google Trends Data (Case of Indonesia) Statistics Indonesia (Badan Pusat Statistik),” International Journal of Scientific Research in Multidisciplinary Studies, Vol. 6, No. 11, pp. 29–33, Nov. 2020.
  8. C.P. Candraningtyas and A.B. Santosa, “Open Unemployment Rate Modeling in the Province of Jawa Timur in 2019-2021 Using Data Panel Regression Method,” Jurnal Forum Analisis Statistik (FORMASI), Vol. 2, No. 2, pp. 92–103, Dec. 2022, doi: 10.57059/formasi.v2i2.37.
  9. N.K. Kaur, U. Kaur, and D. Singh, “K-Medoid Clustering Algorithm-A Review,” International Journal of Computer Application and Technology (IJCAT), Vol. 1, no. 1, pp. 42–45, Apr. 2014.
  10. S. Delimawati, H. Yozza, and Maiyastri, “Pengelompokan Kabupaten/Kota Di Sumatera Barat Berdasarkan Faktor Terkait Kejadian Demam Berdarah Dengue Dengan Metode Fuzzy Subtractive Clustering,” Jurnal Matematika UNAND, Vol. 10, No. 1, pp. 150–158, Jan. 2021, doi: 10.25077/jmu.10.1.150-158.2021.
  11. D.A. Margaretta, I. Rahmi Hg, and H. Yozza, “Pengklasteran Provinsi-Provinsi di Indonesia Berdasarkan Indikator yang Mempengaruhi Indeks Pembangunan Manusia Menggunakan Metode Fuzzy C-Means,” Jurnal Matematika UNAND, Vol. 10, No. 1, pp. 79–86, Jan. 2021, doi: 10.25077/jmu.10.1.79-86.2021.
  12. D. Yu, G. Liu, M. Guo, and X. Liu, “An Improved K-Medoids Algorithm Based on Step Increasing and Optimizing Medoids,” Expert Systems with Applications, Vol. 92, pp. 464–473, Feb. 2018, doi: 10.1016/J.ESWA.2017.09.052.
  13. P. Montero and J.A. Vilar, “TSclust: An R Package for Time Series Clustering,” Journal of Statistical Software, Vol. 62, No. 1, pp. 1–43, Nov. 2015, doi: 10.18637/JSS.V062.I01.
  14. F. Petitjean, G. Forestier, G.I. Webb, A.E. Nicholson, Y. Chen, and E. Keogh, “Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification,” in Proceedings - IEEE International Conference on Data Mining, ICDM, Jan. 2014, pp. 470–479, doi: 10.1109/ICDM.2014.27.
  15. Y. Chen et al., “Delineating Urban Functional Areas With Building-Level Social Media Data: A Dynamic Time Warping (DTW) Distance Based K-Medoids Method,” Landscape and Urban Planning, Vol. 160, pp. 48–60, Apr. 2017, doi: 10.1016/j.landurbplan.2016.12.001.
  16. S. Pramana, D.Y. Paramartha, G.Y. Ermawan, N.F. Deli, and W. Srimulyani, “Impact of COVID-19 Pandemic on Tourism in Indonesia,” Current Issues in Tourism, Vol. 25, No. 15, pp. 2422–2442, 2022, doi: 10.1080/13683500.2021.1968803.
  17. V.T. Huy and D.T. Anh, “An Efficient Implementation of Anytime K-Medoids Clustering for Time Series Under Dynamic Time Warping,” in SoICT '16: Proceedings of the 7th Symposium on Information and Communication Technology, Dec. 2016, pp. 22–29, doi: 10.1145/3011077.3011128.
  18. P. Arora, Deepali, and S. Varshney, “Analysis of K-Means and K-Medoids Algorithm for Big Data,” Physics Procedia, Vol. 78, pp. 507–512, 2016, doi: 10.1016/j.procs.2016.02.095.
  19. A.H. Primandari, T.A. Santoso, and A. Kesumawati, “Establishing Product Distribution Route of Coca-Cola, Semarang Indonesia,” International Journal of Advances in Soft Computing and Its Applications, Vol. 10, No. 2, pp. 148–158, Jul. 2018.
  20. U. Islamy, A. Novianti, F.P. Hidayat, M. Hasan, and S. Kurniawan, “Application of the Spatial Autoregressive (SAR) Method in Analyzing Poverty in Indonesia and the Self Organizing Map (SOM) Method in Grouping Provinces Based on Factors Affecting Poverty,” Enthusiastic: International Journal of Applied Statistics and Data Science, Vol. 1, No. 2, pp. 76–83, Nov. 2021, doi: 10.20885/enthusiastic.vol1.iss2.art4.
  21. M. Mughnyanti, S. Efendi, and M. Zarlis, “Analysis of Determining Centroid Clustering X-Means Algorithm With Davies-Bouldin Index Evaluation,” in IOP Conference Series: Materials Science and Engineering, Jan. 2020, pp. 1–6, doi: 10.1088/1757-899X/725/1/012128.
  22. X. Wang and Y. Xu, “An Improved Index for Clustering Validation Based on Silhouette Index and Calinski-Harabasz Index,” in IOP Conference Series: Materials Science and Engineering, Aug. 2019, pp. 1–6, doi: 10.1088/1757-899X/569/5/052024.
  23. E.L. Widarni and S. Bawono, “Human Capital, Technology, and Economic Growth: A Case Study of Indonesia,” Journal of Asian Finance, Vol. 8, No. 5, pp. 29–35, 2021, doi: 10.13106/jafeb.2021.vol8.no5.0029.
  24. A.H. Primandari, A. Kesumawati, and T. Purwaningsih, “Supporting of Waste Management in Indonesia Using Self Organizing Map for Clustering Analysis,” in Journal of Physics: Conference Series, Apr. 2021, pp. 1–12, doi: 10.1088/1742-6596/1863/1/012072.