Optimasi Extreme Gradient Boosting dengan Particle Swarm Optimization untuk Estimasi Software Effort: Optimized Extreme Gradient Boosting using Particle Swarm Optimization for Software Effort Estimation

Achmad Fahreza Alif Pahlevi; Mokhammad Amin Hariyadi; Agung Teguh Wibowo Almais

doi:10.57152/malcom.v5i3.2055

Authors

Achmad Fahreza Alif Pahlevi Universitas Islam Negeri Maulana Malik Ibrahim Malang
Mokhammad Amin Hariyadi Universitas Islam Negeri Maulana Malik Ibrahim Malang
Agung Teguh Wibowo Almais Universitas Islam Negeri Maulana Malik Ibrahim Malang

DOI:

https://doi.org/10.57152/malcom.v5i3.2055

Keywords:

Extreme Gradient Boosting (XGBoost), Optimasi, Particle Swarm Optimization (PSO), Prediksi, Software Effort Estimation (SEE)

Abstract

Estimasi upaya perangkat lunak (SEE) sangat penting dalam manajemen proyek, namun akurasi sering terganggu oleh kompleksitas proyek. Untuk mengatasinya, studi ini mengusulkan metode hibrida inovatif Particle Swarm Optimization (PSO) - Extreme Gradient Boosting (XGBoost) untuk SEE. Algoritma PSO mengoptimalkan hiperparameter XGBoost, meningkatkan kemampuannya memodelkan hubungan nonlinier dalam data proyek perangkat lunak, sehingga mengurangi kesalahan estimasi. Hasil eksperimen pada kumpulan data China dan Nasa93 menunjukkan bahwa PSO-XGBoost secara signifikan mengungguli metode tradisional dan model pembelajaran mesin mandiri. Metode yang diusulkan mencapai Root Mean Square Error (RMSE) yang lebih rendah sebesar 0,024 untuk China dan 0,0653 untuk Nasa93 menunjukkan efektivitasnya dalam memberikan estimasi upaya yang presisi. Meskipun memiliki kompleksitas komputasi dan bergantung pada data berkualitas, studi ini berkontribusi pada bidang SEE dengan menyajikan solusi praktis dan andal, membantu manajer perangkat lunak dalam perencanaan sumber daya dan pengambilan keputusan.

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