Tesis

Tesis

• By Kurniawan, Oky;
• Tesis
• 4

Dengan berkembangnya perangkat lunak maka dapat dengan mudah mengakses dan memperoleh informasi. Kualitas sebuah perangkat lunak sebelum digunakan haruslah di uji kualitasnya. Maka dari itu perlulah dilakukan pengujian berupa pengujian perangkat lunak untuk melihat apakah perangkat lunak tersebut mengandung cacat atau tidak. Dalam penelitian ini, pendekatan yang menggabungkan teknik Ensemble Gradient Boosting, dengan teknik pengoptimalan menggunakan empat model Hyperparameter Grid Search, Random Search, Bayesian Optimization (Hyperopt), dan Optuna. Tujuan utama penelitian ini adalah untuk meningkatkan performa klasifikasi prediksi cacat pada software. Penelitian dengan menguji dataset metrik cacat software menggunakan Ensemble Gradient Boosting mendapatkan hasil akurasi 77.870% dengan nilai AUC 0,777. Grid Search dengan akurasi 68,786% dengan nilai AUC 0,688. Random search menghasilakan akurasi 67,795% dengan nilai AUC 0,678, Hyperopt dengan hasil akurasi 79,356% dengan nilai AUC 0,792. Optuna dengan hasil 68,538% dengan nilai AUC 0,685%. model dengan kinerja terbaik pada pengujian ini yaitu Hyperopt karena memengaruhi peningkatan akurasi dari model lain yang diujikan.

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