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Feathered forecast:

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creativework.keywordsforecasting, machine learning, random forest
dc.contributor.advisorTorres, Doreen A.
dc.contributor.authorRivera, Mark Lydrion M.
dc.contributor.authorApilado, Bennidict M.
dc.contributor.authorDantay, Leiann Drew D.
dc.contributor.authorMarquez, Mark Emerson
dc.contributor.authorReolegio, Jan Rielan H.
dc.contributor.chairMalamion, Edelvar A.
dc.contributor.committeememberPimentel, Emmalou B.
dc.date.accessioned2026-03-13T05:55:24Z
dc.date.available2026-03-13T05:55:24Z
dc.date.issued2024-12
dc.descriptionFull text
dc.description.abstractThis study tackled the factors of poultry egg production and developed a predictive model and system prototype. Key factors included chicken age and seasonal patterns or variation. Using the CRISP-DM methodology, data sets were gathered, cleaned, and trained using different models. Random Forest, Support Vector Machines, and Neural Networks were utilized to predict egg yields. Among the four models, Random Forest had the best prediction and performance based on the different metrics. The researchers also developed a web-based system prototype that integrated the prediction result using Random Forest. The system can enhance resource planning and aid in better decision-making. The system had a usability score of 83.20 on the SUS, which makes it a valuable combination of technology and data insights to enhance poultry farming productivity.
dc.format.extentxi, 72 p. : ill. (col.).
dc.identifier.citationRivera, M. L. M., Apilado, B. M., Dantay, L. D. D., Marquez, M. E., & Reolegio, J. R. H. (2024). Feathered forecast: using analytics to predict egg yields. [Unpublished Undergraduate Thesis]. Don Mariano Marcos Memorial State University - Mid La Union Campus, City of San Fernando, La Union. Lakasa ti Sirib, DMMMSU Institutional Repository.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/1191
dc.language.isoen
dc.publisherDon Mariano Marcos Memorial State University - Mid La Union Campus
dc.rights.licenseCC BY 4.0
dc.sdgSDG 9
dc.sdgSDG 12
dc.subjectFORESTRY, AGRICULTURAL SCIENCES and LANDSCAPE PLANNING::Area economics::Agricultural economics
dc.subjectAnalytics
dc.subjectAgricultural science
dc.subjectMachine learning
dc.subject.ddcAgriculture--Data processing--Management applications
dc.subject.ddcOperations management--Production forecasting
dc.subject.ddcAgricultural forecasting
dc.subject.ddcMachine learning
dc.titleFeathered forecast:
dc.title.alternativeUsing analytics to predict egg yields
dc.typeThesis
dcterms.accessRightsOpen access
dspace.entity.typePublication
thesis.degree.disciplineCollege of Information Technology
thesis.degree.levelUndergraduate
thesis.degree.nameBachelor of Science in Information Technology
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