Estimasi Waktu Pengembangan Dengan Simple Function Point Menggunakan Metode Berbasis Ensemble
DOI:
https://doi.org/10.31598/sintechjournal.v7i3.1681Keywords:
Data Mining, Ensemble, Machine Learning, Scrum, Software EstimationAbstract
Pengembangan proyek perangkat lunak sangat bergantung pada perencanaan estimasi waktu dan biaya agar membantu pengembang mengurangi terlambatnya delivery software, meningkatkan kepuasan pelanggan, memungkinkan organisasi untuk mengalokasikan sumber daya secara efisien, mengurangi biaya dan mengoptimalkan proses pengembangan. Untuk mengatasi permasalahan tersebut diperlukan prediksi waktu pengembangan perangkat lunak yang cepat dan efisien serta memberikan hasil prediksi terbaik. Penelitian ini membandingkan metode machine learning Ada Boost Regressor, Support Vector Regression, Random Forest Regression dan Ensemble Method dengan data yang diambil dari proyek scrum pada salah satu instansi. Hasil penggunaan machine learning berbasis metode ensemble dengan penambahan fitur simple function point (SiFP) memberikan hasil terbaik dengan nilai RMSE dataset dengan SiFP 0.0714 dan tanpa SiFP 0.0819, nilai MSE dataset dengan SiFP 0.0051 dan tanpa SiFP 0.0067, nilai MAE dataset dengan SiFP 0.0589 dan tanpa SiFP 0.0674 sedangkan nilai R2 dataset dengan SiFP 0.7698 dan tanpa SiFP 0.6964. Dari hasil tersebut disimpulkan model machine learning ensemble methods dengan penambahan fitur SiFP meningkatkan estimasi waktu pengembangan pada proyek scrum.
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