KLASIFIKASI INFLASI 34 IBUKOTA PROVINSI DI INDONESIA SEBELUM DAN SAAT COVID-19 MELALUI PENGELOMPOKAN WILAYAH DENGAN K-MEANS CLUSTERING

  • Novi Etrisia Badan Pusat Statistik Kota Metro
  • Muhammad Findi Alexandi IPB University, Indonesia
  • Alla Asmara IPB University, Indonesia
Keywords: Consumer Price Index, K-Means Clustering, Regional Inflation

Abstract

The magnitude of the national inflation rate is the formation of aggregate figures from regional inflation. Formulating policies that target inflation is increasingly complex in the era of regional autonomy because each region has different factors and conditions that make it difficult to control. For this reason, special attention is needed in dealing with regional inflation. This study aims to classify regional inflation that occurred in Indonesia before and during the Covid-19 pandemic through a clustering approach using the K-Means Clustering method. The secondary data used in this study comes from the Central Bureau of Statistics (BPS) publications of the Republic of Indonesia. The results showed that after clustering, there were 19 provinces (55.88%) that experienced a decrease in the cluster level, 6 provinces (17.64%) experienced an increase in the cluster level and 9 provinces (26.4%) were stable at the cluster level. Overall, the Covid-19 outbreak that has hit the world economy has harmed provinces in Indonesia. This can be observed from the largest percentage of clusters, namely provinces that experienced a decrease in the inflation category, which was 55.88% and the highest number of provinces which were originally in 2017 were in cluster 3 (high inflation category) in 2021 which shifted to cluster 1 (inflation category). low). Based on the Paired Sample T-Test, there was a significant difference in clustering before Covid-19 (2017) and during Covid-19 (2021).

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Published
2023-07-28
How to Cite
Etrisia, N., Alexandi, M., & Asmara, A. (2023, July 28). KLASIFIKASI INFLASI 34 IBUKOTA PROVINSI DI INDONESIA SEBELUM DAN SAAT COVID-19 MELALUI PENGELOMPOKAN WILAYAH DENGAN K-MEANS CLUSTERING. Jurnal Ekonomi Pembangunan, 12(2), 120-133. https://doi.org/https://doi.org/10.23960/jep.v12i2.1597