Inflation data clustering of some cities in Indonesia

Setiawan, Adi; Susanto, Bambang; Mahatma, Tundjung

doi:10.1088/1742-6596/855/1/012046

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two studies [2] and [3] applied Partitioning around medoids (PAM) method to cluster regencies/cities in Indonesia based on inflation and poverty, respectively. Whereas [4] used the k-Means (KM) method and fuzzy c-means to cluster 15 cities in Indonesia. Furthermore, [5] clustering was based on poverty rate using multivariate analysis, and [6] clustering was based on commodity inflation patterns using the ward method.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Clustering Regencies-Cities Based on Inflation and Poverty Rates in Indonesia

Gustriansyah

Alie

Sanmorino

et al. 2022

Indonesian J. of Inf. Syst.

View full text Add to dashboard Cite

The COVID-19 pandemic has increased inflation and poverty rates in many cities, thus requiring considerable attention from the government as a policymaker. Therefore, this study aims to cluster regencies/cities that need mitigation priorities from the Indonesian government based on inflation and poverty rates in 2021. Four machine learning methods, namely k-Means (KM), Partitioning around medoids (PAM), Ward, and Divisive analysis (Diana) are utilized and compared to achieve that purpose. Clustering 90 regencies/cities in Indonesia produced five optimal clusters. Furthermore, the clustering results were validated using the Silhouette width (SW) and Dunn index (DI). The results showed that the k-means method produced the most compact cluster. Hence, this study's results can be utilized as a reference for the government in determining the steps and priorities of economic policy in Indonesia.

show abstract

Section: Introductionmentioning

confidence: 99%