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Modified Agglomerative Clustering by Klassen Disparities for Identification Hierarchical Cluster of Regional Developing Countries

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Tb. Ai Munandar, Azhari Azhari, Aina Musdholifah, Lincolin Arsyad

Aina Musdholifah Tb. Ai Munandar Azhari Azhari and Lincolin Arsyad. Modified Agglomerative Clustering by Klassen Disparities for Identification Hierarchical Cluster of Regional Developing Countries. International Journal of Computer Applications 141(9):1-5, May 2016. BibTeX

	author = {Tb. Ai Munandar, Azhari Azhari, Aina Musdholifah and Lincolin Arsyad},
	title = {Modified Agglomerative Clustering by Klassen Disparities for Identification Hierarchical Cluster of Regional Developing Countries},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {9},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2016909730},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Inequality of regional development is a global problem and faced by many countries, including Indonesia. Various attempts were made to reduce inequality in the region, one of them is by analyzing the imbalance with appropriate methods that can be used as a basis for policy making prioritization of future development. Klassen methods typically used to analyze the inequality of the region according to the indicators Gross Regional Domestic Product (GRDP). However, the division of the region inequality using Klassen deemed too rigid, given the existence of a possible relationship between the regions and in each of the groups formed by Klassen. This research aims to develop a new approach that can be used to analyze the inequality of development of the region. Aggromerative cluster hierarchical cluster technique modified with Klassen named Modified Agglomerative Hierarchical Clustering with Klassen (MHACK). The results shows that the use of algorithms MHACK, besides being able to classify the area into four main clusters, are also capable of forming the new group hierarchy for each region in each of the main cluster. Cophenet distance coefficient showed that MHACK algorithm has 0.9950 for Quadrant I, and 0.9154 for Quadrant II. In addition, the city of Magelang is indicated as an advanced and rapidly growing region with a poor value of GRDP, while Cilacap, Kudus, Boyolali, Brebes and Wonogiri indicated as a potential and growing region but has the worst value of GRDP.


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inequality of development, GDP, Klassen, agglomerative hierarchical clustering, MHACK.