Development of Decision Support System for Duck and Chicken Culinary Businesses Based On Good Manufacturing Practices Using Data Mining
DOI:
https://doi.org/10.32628/CSEIT261213Keywords:
Duck and Chicken Culinary, Decision Support System (DSS), Good Manufacturing Practice (GMP), Clustering K-Means, Association RulesAbstract
This study presents the development of a Decision Support System (DSS) for duck and chicken culinary businesses through the integration of Good Manufacturing Practices (GMP) and data-driven analysis using K-Means clustering and Association Rule Mining. The system processes production, nutritional, economic, and sensory data collected from processed poultry products to support business decision-making. K-Means clustering successfully segmented 32 samples into four meaningful product groups based on meat attributes, cooking characteristics, cost levels, and quality evaluations. The clustering results distinguished fast-cooking, low-cost chicken products from slow-cooked, high-cost duck dishes, revealing clearly differentiated culinary market segments. Association rules further identified strong correlations among product type, fat level, production cost, and cooking duration, providing valuable knowledge for identifying premium or mainstream culinary categories. When combined, the clustering and rule-based insights enhance GMP implementation by supporting standardization, quality control, and traceability throughout food preparation processes. The resulting DSS enables producers to optimize resource usage, refine menu strategies, and strengthen product consistency while aligning with food safety and hygiene standards. Overall, this research demonstrates that integrating GMP with AI-based analytics provides a practical and scalable approach for improving governance, operational efficiency, and competitiveness in small and medium-scale duck and chicken culinary enterprises.
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L. Z. H. Jansen, E. J. Van Loo, K. E. Bennin, and E. Van Kleef, “Exploring the role of decision support systems in promoting healthier and more sustainable online food shopping : A card sorting study,” Appetite, vol. 188, no. December 2022, p. 106638, 2023, doi: 10.1016/j.appet.2023.106638. DOI: https://doi.org/10.1016/j.appet.2023.106638
M. Mackiewicz and D. Kuberska, “Unleashing the power of clustering: a qualitative study of cluster organizations’ role in advancing green transformation,” Competitiveness Review, 2024, doi: 10.1108/CR-03-2023-0069. DOI: https://doi.org/10.1108/CR-03-2023-0069
E. Yanti and E. Saputra, “Decision Support System for Culinary Tourism Recommendations in Batubara Regency Using the Simple Additive Weighting,” vol. 6, no. 1, pp. 970–979, 2025.
K. B. Seminar et al., “AI-based system for food and beverage selection towards precision nutrition in Indonesian restaurants,” no. c.
M. A. Hussain and I. H. Khan, “Machine Learning in Transforming the Food Industry,” pp. 1–15, 2026. DOI: https://doi.org/10.3390/foods15010090
P. Deepika, S. Sasikala, S. Saranya, and A. Kiruthika, “A Noval Classification And Prediction Algorithm For Heart Disease Identification,” vol. 7, no. 2, pp. 12–17, 2018.
S. Sumarlinda and W. Lestari, “Decision support system for lecturer publication mapping using k- means clustering method,” vol. 5, no. 4, pp. 140–145, 2022.
M. Michel, A. L. Eldridge, C. Hartmann, P. Klassen, J. Ingram, and G. W. Meijer, “Trends in Food Science & Technology Benefits and challenges of food processing in the context of food systems, value chains and sustainable development goals,” Trends in Food Science & Technology, vol. 153, no. August, p. 104703, 2024, doi: 10.1016/j.tifs.2024.104703. DOI: https://doi.org/10.1016/j.tifs.2024.104703
N.Prytulska, et al, “Application Of Good Manufacturing Practices In The Production Of Food Products For Special Medical Purposes, Eastern-European Journal of Enterprise Technologies ISSN 1729-3774, 3/13 ( 123 ) 2023. DOI: https://doi.org/10.15587/1729-4061.2023.279371
Novanda, A. D. ., Jariyah, & Hidayat, A. W. . (2022). Study of Implementation of Good Manufacturing Practices (GMP) in CV “XYZ”. Nusantara Science and Technology Proceedings, 51-57.
Yopi, R. M. S., et al, “Implementation Of Good Manufacturing Practices (GMP) in The Home Industry of Kampung Lontong Surabaya, Pancasakti Journal of Public Health Science and Research, Vol. 5, No. 2 May 2025: Page. 210–215.
Yu,S. C., et al, “Apossible systematic culinary approach for spent duck meat: sous-vide cuisine and its optimal cooking condition”, Published by Elsevier Inc. on behalf of Poultry Science Association Inc. This is an open access article under the CC BY-NC-ND license,2023.
Gavin.C., et al,“Poultry ConsumptionandHumanHealth:How MuchIsReallyKnown?ASystematicallySearched ScopingReviewandResearchPerspective, 2022.
Z. Suhaemi., and S. G. Hidayati, “Improvement of the Quality of Duck’s and Chicken’s Meat Using African Leaf (Vernonia amygdalina), Proceedings of the International Seminar on Promoting Local Resources for Sustainable Agriculture and Development (ISPLRSAD 2020). DOI: https://doi.org/10.2991/absr.k.210609.026
D. Singh. Rajput., et al, “Duck Farming, Fascinating Option in India”,J Veterinar Sci Technolo, Volume 5 • Issue 3 • 1000181.
A. Dal. Bosco., et al, “Extensive Rearing Systems in Poultry Production: The Right Chicken for the Right Farming System. A Review of Twenty Years of Scientific Research in Perugia University, Italy, Animals 2021, 11, 1281. DOI: https://doi.org/10.3390/ani11051281
Z. Zhang., et al, “AComprehensive Review on the Recent Technological Advancements in the Processing, Safety, and Quality Control of Ready-to-Eat Meals”, 2025. DOI: https://doi.org/10.3390/pr13030901
S. BISWAS., et al., “Technological investigation into duck meat and its products- a potential alternative to chicken”, World's Poultry Science Journal, Vol. 75, December 2019. DOI: https://doi.org/10.1017/S004393391900062X
H. Atthifa.Ryantika., et al, “Application Of The K-Means Method For Clustering Best Selling Products In Ice Cream Sales”, Jurnal TAM (Technology Acceptance Model) Volume 14, Number 1, July 2023. DOI: https://doi.org/10.56327/jurnaltam.v14i1.1499
Ioan, D. H., et al, “Efficient Discovery of Association Rules in E-Commerce: Comparing Candidate Generation and Pattern Growth Techniques”, applied sciences, 2025.
Wiji. L., et al, “Implementation Of Association Rules Using Apripori Algorithm For Angkringan”, International Conference of Health, Science and Technology (ICOHETECH), 2023.
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