Analysis of district cluster based on the indicator of Gross Regional Domestic Product (GRDP) using unsupervised learning

Jaelanie, Agung, Nugroho, Supeno Mardi Susiki, Hariadi, Mochamad and Kurniawan, Fachrul ORCID: https://orcid.org/0000-0002-3709-8764 (2016) Analysis of district cluster based on the indicator of Gross Regional Domestic Product (GRDP) using unsupervised learning. Presented at The 2nd Internasional Seminar on Science and Technology (ISST) for Sustainable Infrastructure Empowering Research and Technology for Sustainable Infrastructure – 2nd August 2016.

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Abstract

The economic lameness among regions is an unsolved problem all this time. To determine policies, to plan economic development right on target, and to solve the economic lameness, we need sufficient and accurate data and information. In this research, there’s data of Gross Regional Domestic Product (GRDP) would be analyzed here to get the economic pattern of District cluster in Indonesia. The tool for this analysis in this research is clustering technic in which it has a method called K-Means. K-means groups the obtained data based on its features for each. This research results in cluster 5 as the optimal ones. With the value of DBI 0.9640.

Item Type:	Conference (Paper)
Keywords:	Clustering; K-Means Algorithm; Davies-Bouldin Index (DBI); Gross Regional Domestic Product (GRDP)
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity 08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Fachrul Kurniawan
Date Deposited:	02 Mar 2021 12:18

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