Ranking Method in Group Decision Support to Determine the Regional Prioritized Areas and Leading Sectors using Garrett Score
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 11
Abstract
The main objective of regional development is to achieve equal development in different regions. However, the long duration and complexity of the process may result in the unequal development of some regions. In order to achieve a fair development process for each region, a standard approach must be developed to select a suitable priority area that can support other underdeveloped regions that require attention. One of the approaches taken is to determine the prioritized areas and the leading sectors in the region where the region is expected to be a support for other regions that still need attention and handling on development priorities. This research was conducted to provide a new alternative in the process of determining the prioritized areas, not only by observing the development data, but also involving decision-making components consisting of government and community (including non-governmental organizations and academicians). This study used group decision support approach with the Garrett ranking technique. The results of the research on the determination of the prioritized areas using Garret Score showed that there are 5 of 29 Regencies/Municipalities in Papua Province that can be used as prioritized areas, namely Jayapura Regency, Jayapura Municipality, Mimika, Merauke and Nabire. Then, there are three leading sectors for development, namely agricultural, mining and Industrial and Processing sectors. The test of ranking results was conducted by calculating the Spearman's correlation coefficient of the Garrett ranking results and obtained a coefficient of 0.807 which means that the ranking results are very strong.
Authors and Affiliations
Heru Ismanto, Suharto Suharto, Azhari Azhari, Lincolin Arsyad
Keywords
Related Articles
- EP ID EP417604
- DOI 10.14569/IJACSA.2018.091114
- Views 85
- Downloads 0
Sea Lion Optimization Algorithm
This paper suggests a new nature inspired metaheuristic optimization algorithm which is called Sea Lion Optimization (SLnO) algorithm. The SLnO algorithm imitates the hunting behavior of sea lions in nature. Moreover, it...
Model for Time Series Imputation based on Average of Historical Vectors, Fitting and Smoothing
This paper presents a novel model for univariate time series imputation of meteorological data based on three algorithms: The first of them AHV (Average of Historical Vectors) estimates the set of NA values from historic...
Object-Oriented Context Description for Movie Based Context-Aware Language Learning
Context-aware ubiquitous learning is a promising way to learn languages; however, it requires developers and operators of much effort to construct, deploy, and use the specialized system. As its alternative, this paper p...
Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features
Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high divers...
An Efficient Segmentation of Retinal Blood Vessels using Singular Value Decomposition and Morphological Operator
The extensive study on retinal fundus images has become an essential part in medical domain to detect pathologies including diabetic retinopathy, cataract, glaucoma, macular degeneration,etc.which are the major causes of...