Determining Sister City Regency/City Non-Sample Cost of Living Survey (SBH) and Clustering Analysis of Consumption Patterns in West Java using the Machine Learning Method

Raditya Novidianto(1*), Erwin Tanur(2), Andrea Tri Rian Dani(3), Fachrian Bimantoro Putra(4)


(1) Badan Pusat Statistik (BPS) Kuningan Regency
(2) BPS Education and Training Center
(3) Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Mulawarman
(4) Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Mulawarman
(*) Corresponding Author

Abstract


Inflation is a significant data source in policy making. However, not all Regency/cities have inflation figures. As a result, Regency/cities must borrow inflation figures from dietary characteristics, GDP per capita, population, and distance between Regency and cities; this is called a sister city. With the help of machine learning, the similarity level method using distance measures, namely Euclidean distance, CID distance, and ACF distance, can help Regency/cities find sister cities. Furthermore, grouping was carried out using a biclustering algorithm to see the characteristic variables in West Java from the same consumption pattern data. The biclustering parameter with tuning parameter 𝛿=0.1 is the best bicluster with a total of 3 biclusters with a value of MSR/V=0.02433 with identical characteristic variables, namely Average Fish Consumption (X3), Average Meat Consumption (X4), Average Consumption of Eggs and Milk (X5), Average Consumption of Vegetables (X6), Average Consumption of Fruit (X8), Average Consumption of Oil and Coconut (X9), Average Consumption of Housing and Household Facilities (X15), Average Consumption of Various Goods and Services and Average Consumption of Taxes (X16), Levies and Insurance (X19).

Keywords


ACF, Biclustering, CID, Euclidean, Similarity

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