Penerapan Algoritma K-Means Clustering Pada Tingkat Inflasi Kota Di Indonesia
DOI:
https://doi.org/10.55606/akuntansi.v2i2.235Keywords:
Inflation, Data Mining, K-Means, ClusteringAbstract
Inflation is a general tendency to increase the prices of goods and services, and it happens all the time. when the prices of domestic goods and services rise, inflation will rise. Depreciation causes the prices of goods and services to rise. Uncontrolled inflation can result in losses for society and the government. Therefore, an appropriate study is needed to map the dynamics of inflation in a region. One way to map the inflation rate is clustering. Clustering is dividing data into groups with the same characteristics. The author took the initiative to analyze the urban inflation rate in Indonesia from 2020 to 2022. The data is sourced from the Central Statistics Agency (BPS) website. This analysis uses the K-Means Clustering method with 5 clusters. the group with the highest inflation is in cluster 0, the high inflation group is in cluster 1, the moderate inflation group is in cluster 2, the low inflation group is in cluster 3, and the lowest inflation is in cluster 4. by categorizing the inflation rate of cities in Indonesia, it can be seen which cities in Indonesia have very high, high, medium, low and very low inflation rates.
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