Decision Support System for Gamefowl Performance Forecasting
- Lea P. Ymalay
- Guimaras State College
This study is directed towards the development of the Decision Support System for Gamefowl Performance Forecasting for RJT Cock Farm in Barangay Salvacion, Buenavista, Guimaras, Philippines. The software is provided with years’ worth of data that include cock and hen profiles, breeding profile, health and medication, sparring records, and most importantly fight records as main input. The aforementioned data were gathered, processed, and analyzed in order to come up with reliable forecasts and efficient management of gamefowl records. To realize its primary goal, Bayesian Algorithm was integrated in the system as an enabler for its data analysis component which will forecast the winning probability of game fowls. As for the system’s performance, the proponent used F-measure to evaluate the accuracy of the system’s forecasting capability against actual cock performance in sparring and real fight. Moreover, the system was evaluated based on the ISO 25010 International Quality Standards for computer software. The result revealed that the system highly conforms to international standards in terms of its functionality suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. During the accuracy test, the F-Measure value using the harmonic mean of precision and recall is 78.25% which implies that the accuracy of the forecasting capability of the system is Medium to High. Meanwhile, an over-all mean of 4.73 establishes that the system has “excellent” quality based on the ISO 25010 International Standards.
- Forecast, F-measure, Gamefowls, Performance, Decision Support System
APA 7th Edition:
Ymalay, L. (2019). Decision Support System for Gamefowl Performance Forecasting. Innovatus, 2(1), 137-142.
Ymalay, L., 2019. Decision Support System for Gamefowl Performance Forecasting. Innovatus, 2(1), pp.137-142.
 L. Ymalay, "Decision Support System for Gamefowl Performance Forecasting", Innovatus, vol. 2, no. 1, pp. 137-142, 2019.
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