Prediksi Cacat Perangkat Lunak Untuk Evaluasi Kualitas Menggunakan Teknik Pembelajaran Ensemble Stacking

• MUHAMMAD ROMADHONA KUSUMA
• 14210128

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ABSTRAK

ABSTRAK

Nama : Muhamamd Romadhona Kusuma

NIM : 14210128

Program Studi : Ilmu Komputer

Fakultas : Teknologi Informasi

Jenjang : Strata Dua (S2)

Konsentrasi : Software Engineering

Judul : “Prediksi Cacat Perangkat Lunak Untuk Evaluasi Kualitas Menggunakan Teknik Pembelajaran Ensemble Stacking”

Penelitian ini bertujuan untuk meningkatkan kualitas perangkat lunak dan mendukung pengelolaan zakat yang lebih efektif oleh Badan Amil Zakat Nasional (BAZNAS) dengan membangun model prediksi kecacatan perangkat lunak (Software Defect Prediction Model - SDPM). Pada penelitian ini, digunakan teknik pembelajaran mesin dan pendekatan ensemble stacking pada dataset "Menara Masjid" yang terdiri dari 228 record dan 34 atribut. Proses preprocessing meliputi label encoding untuk mengubah data kategorikal ke numerikal, diikuti oleh fitur selection dengan pearson correlation dan select k-best untuk memilih atribut yang akan digunakan. Selanjutnya, diterapkan normalisasi standar dengan standar scaler dan teknik SMOTE untuk menangani ketidakseimbangan distribusi data. Proses hyperparameter tuning dengan grid search CV dilakukan pada algoritma Machine Learning seperti Ada Boost dan Gradient Boosting. Hasil penelitian menunjukkan bahwa pendekatan ensemble stacking dengan gabungan algoritma Gradient Boosting, Ada Boost, Decision Tree, dan Bayesian Ridge, serta meta learner LightGBM memberikan peningkatan akurasi dengan skor 0,97 R2 score, MAE 0,037, dan MSE 0,006. Hal ini menunjukkan bahwa pendekatan ensemble stacking mampu mengatasi permasalahan kecacatan perangkat lunak dengan hasil prediksi yang lebih akurat, dan dapat memberikan panduan dan kerangka kerja yang berguna dalam pengelolaan zakat dan aplikasi perangkat lunak lainnya. Hasil ini diharapkan dapat meningkatkan kualitas perangkat lunak dan mendukung Badan Amil Zakat Nasional (BAZNAS) dalam pengelolaan zakat dengan lebih efektif..

KATA KUNCI

Kecacatan perangkat lunak,Prediksi,Fitur Seleksi,SMOTE,Hyperparameter Tuning

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DAFTAR PUSTAKA

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