Open AccessPenerapan Clustering Pada Laju Inflasi Kota Di Indonesia Dengan Algoritma K-Means
Author(s) -
Yudi Prayoga,
Heru Satria Tambunan,
Iin Parlina
Publication year - 2019
Publication title -
brahmana
Language(s) - English
Resource type - Journals
ISSN - 2715-9906
DOI - 10.30645/brahmana.v1i1.4
Subject(s) - inflation (cosmology) , economics , cluster analysis , cluster (spacecraft) , market liquidity , value (mathematics) , consumption (sociology) , monetary economics , exchange rate , statistics , mathematics , social science , physics , sociology , theoretical physics , computer science , programming language
Inflation is a process of increasing prices in general and continuously, related to market mechanisms that can be caused by various factors, among others, increased public consumption, excess liquidity in the market which triggers consumption or even speculation, to include the consequences of inability to distribute goods. Inflation is an indicator to see the level of change, and is considered to occur if the process of price increases takes place continuously and influences each other. Inflation stability is a prerequisite for sustainable economic growth which ultimately benefits the improvement of people's welfare. With the large amount of data generated from the inflation rate of cities in Indonesia it is difficult for the government to classify the inflation rate. The author took the initiative to conduct research on classifying the inflation rate of cities in Indonesia by using the K-Means Clustering Data Mining algorithm, with the number of clusters being 3. The high value group is in cluster 1 (above average), the value group is in cluster 2 (around the average based on the distance used from the centroid), and the low value group is in cluster 3 (below average). flat). By grouping the rate of inflation of cities in Indonesia, it will be known which cities in Indonesia have high, medium and low inflation rates.