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All Journal Journal Of Artificial Intelligence And Software Engineering
Novitasari, Desy Candra
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Computer Science & IT

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Improving the Accuracy of COCOMO II in Software Projects Using Hybrid GWO-PSO Putri, Rahmi Rizkiana; Novitasari, Desy Candra
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7603

Abstract

Accurate business forecasting provides an important foundation for managing software projects effectively. If the business estimate is not accurate, it can have an impact on the quality of project management to become less efficient. It can be risky such as an excess budget, to failing to meet the set schedule. This research includes the hybrid Grey Wolf Optimization (GWO)-Particle Swarm Optimization (PSO) method to optimize the results of business estimation, thereby resulting in more valid and reliable business estimates of software projects. The implementation of the proposed method showed a Mean Magnitude Relative Error (MMRE) value of 321.16%, which is 1243.23% lower than the results of conventional COCOMO II. The results of the trial prove that the accuracy of business estimates has increased, thus making a significant contribution to improving the effectiveness of software project management. Thus, this study provides a more reliable COCOMO II business estimation framework that can be adopted by practitioners and researchers to improve the planning, control, and evaluation process of software projects.
Co-Authors Putri, Rahmi Rizkiana