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Decision support system for fish quarantine measures in Indonesia

Deden Sumirat Hidayat (E-Government and E-Business Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia and Research Center for Informatics, National Research and Innovation Agency (BRIN), Central Jakarta, Indonesia)
Winaring Suryo Satuti (E-Government and E-Business Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia)
Dana Indra Sensuse (E-Government and E-Business Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia)
Damayanti Elisabeth (E-Government and E-Business Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia)
Lintang Matahari Hasani (E-Government and E-Business Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia)

VINE Journal of Information and Knowledge Management Systems

ISSN: 2059-5891

Article publication date: 26 January 2022

Issue publication date: 19 January 2024

326

Abstract

Purpose

Fish 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/approach

This 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.

Findings

The 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/value

This 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.

Keywords

Acknowledgements

The authors would like to thank Direktorat Riset dan Pengabdian Masyarakat (DRPM) from University of Indonesia for funding this research through the “Publikasi Terindeks Internasional (PUTI) Q2 Tahun Anggaran 2020 Nomor: NKB1479/UN2.RST/HKP.05.00/2020” program. The first authors are the main contributors to this paper.

Citation

Hidayat, D.S., Satuti, W.S., Sensuse, D.I., Elisabeth, D. and Hasani, L.M. (2024), "Decision support system for fish quarantine measures in Indonesia", VINE Journal of Information and Knowledge Management Systems, Vol. 54 No. 2, pp. 299-323. https://doi.org/10.1108/VJIKMS-08-2021-0144

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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