Abstract
The identification of regional development gaps is an effort to see how far the development conducted in every District in a Province. By seeing the gaps occurred, it is expected that the Policymakers are able to determine which region that will be prioritized for future development. Along with the regional gaps, the identification in Gross Regional Domestic Product (GRDP) sector is also an effort to identify the achievement in the development in certain fields seen from the potential GRDP owned by a District. There are two approaches that are often used to identify the regional development gaps and potential sector, Klassen Typology and Location Quotient (LQ), respectively. In fact, the results of the identification using these methods have not been able to show the proximity of the development gaps between a District to another yet in a same cluster. These methods only cluster the regions and GRDP sectors in a firm cluster based on their own parameter values. This research develops a new approach that combines the Klassen, LQ and hierarchical agglomerative clustering (HAC) into a new method named multi view hierarchical agglomerative clustering (MVHAC). The data of GRDP sectors of 23 Districts in West Java province were tested by using Klassen, LQ, HAC and MVHAC and were then compared. The results show that MVHAC is able to accommodate the ability of the three previous methods into a unity, even to clearly visualize the proximity of the development gaps between the regions and GRDP sectors owned. MVHAC clusters 23 districts into 3 main clusters, they are; Cluster 1 (Quadrant 1) consists of 5 Districts as the members, Cluster 2 (Quadrant 2) consists of 12 Districts and Cluster 3 (Quadrant 4) consists of 6 Districts.
Highlights
Development gap is a global issue faced by many countries in the world, including Indonesia
The results show that most Gross Regional Domestic Product (GRDP) sectors possessed by the Districts in West Java Province are included into the non-basic clusters that do not have a competitive advantage
Based on the test on all four approaches to identify the regional development gaps, it is known that the multiview agglomerative hierarchical clustering (MVHAC) method is able to cover some of the functions that are not able to conduct by Klassen, Location Quotient (LQ) and hierarchical agglomerative clustering (HAC)
Summary
Development gap is a global issue faced by many countries in the world, including Indonesia. The basic idea of this research is to develop a new approach to address the inability of Klassen, LQ and clustering techniques in identifying the regional development gaps and determining the potential sector. The previous research had identified regional development gaps based on the GRDP sectors of the Districts by using several approaches, one of which is by using Klassen typology, as conducted by (Hariyanti and Utha, 2016; Suwandi, 2015; Endaryanto et al, 2015; Fattah and Rahman, 2013; Karsinah et al, 2016). The Klassen clustering is only able to classify the data of GRDP sectors into four firm groups that have been determined based on the value of the interval value of the growth rate of development and the contribution rate of development between regions compared to the comparison regions.
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