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Decision support system for fish quarantine measures in Indonesia

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Publicado en:VINE Journal of Information and Knowledge Management Systems vol. 54, no. 2 (2024), p. 299-323
Autor principal: Hidayat, Deden Sumirat
Otros Autores: Satuti, Winaring Suryo, Dana Indra Sensuse, Damayanti, Elisabeth, Lintang Matahari Hasani
Publicado:
Emerald Group Publishing Limited
Materias:
Indonesia
Research
Decision support systems
Fisheries
Data mining
Classification
Knowledge discovery
Pests
Decision making
Knowledge management
Commodities
Quarantine
Systems development
Factor analysis
Algorithms
Explicit knowledge
Fish
Contingency
Decision trees
Risk assessment
Measures
Management
Research methodology
Knowledge
Support networks
Regulation
Research design
Rules
Decisions
Information retrieval
Computerized decision support systems
Acceso en línea:Citation/Abstract
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Resumen:PurposeFish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.Design/methodology/approachThis research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.FindingsThe highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.Originality/valueThis study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.
ISSN:2059-5891
2059-5905
0305-5728
1474-1032
DOI:10.1108/VJIKMS-08-2021-0144
Fuente:ABI/INFORM Global