Mapping Indonesian potential fishing zone using hierarchical and non-hierarchical clustering

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Communications in Mathematical Biology and Neuroscience Pub Date : 2023-01-01 DOI:10.28919/cmbn/8088
R. Pontoh, Soffy Mulyani, Salma Zhahira, Octavia Aulia Wiratama, Mohamad Naufal Farras, R. Arisanti
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Abstract

: Indonesia, a maritime nation whose ocean area exceeds its land area, has an abundance of ocean-based natural resources, such as fish, seaweed, coral reefs, and other marine organisms. The fisheries industry is one of the potential sources of extraordinary marine resources for the Indonesian economy. The annual increase or decrease in fish production in Indonesia can be attributed to several factors, including natural influences such as climate and ocean waves, inadequate management of marine resources, unequal distribution of facilities to support increased fish production in Indonesia, and the characteristics of areas that have a significant impact on the resulting fish production. Consequently, the objective of this research is to classify provinces in Indonesia using clustering analysis so that government policy programs can be more focused and directed according to the characteristics of the clusters formed. The application of cluster analysis was based on the development of fish production data for each province in Indonesia from 2017 to 2019 obtained from the website of the Central Statistics Agency (BPS). Clustering analysis using hierarchical and non-hierarchical methods produces a dendrogram using the average linkage DTW hierarchical method, indicating the formation of two optimal clusters. Non-hierarchical clustering with two clusters produces the
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利用分层和非分层聚类方法绘制印度尼西亚潜在渔区图
印度尼西亚是一个海洋面积超过陆地面积的海洋国家,拥有丰富的海洋自然资源,如鱼类、海藻、珊瑚礁和其他海洋生物。渔业是印尼经济非凡海洋资源的潜在来源之一。印度尼西亚鱼类产量的年度增减可归因于若干因素,包括气候和海浪等自然影响、海洋资源管理不足、支持印度尼西亚鱼类产量增加的设施分布不均,以及对鱼类产量产生重大影响的地区的特点。因此,本研究的目的是使用聚类分析对印度尼西亚的省份进行分类,以便政府政策方案可以根据形成的集群的特征更加集中和指导。聚类分析的应用是基于从中央统计局(BPS)网站上获得的2017年至2019年印度尼西亚各省鱼类生产数据的发展。使用分层和非分层方法进行聚类分析,使用平均链接DTW分层方法生成树图,表明形成了两个最优聚类。具有两个簇的非分层聚类产生
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来源期刊
Communications in Mathematical Biology and Neuroscience
Communications in Mathematical Biology and Neuroscience COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.10
自引率
15.40%
发文量
80
期刊介绍: Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.
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