Klasifikasi Software Defect Prediction Portal Media Bisnis Syariah Menggunakan Teknologi Machine Learning

• FIKRI ISMAYA
• 14207089

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ABSTRAK

ABSTRAK

Nama             : Fikri Ismaya

NIM                 : 14207089

Program Studi: Ilmu Komputer

Fakultas          : Teknologi Informasi

Jenjang           : Strata Dua (S2)

Konsentrasi    : Software Engineering

Judul              : Klasifikasi Software Defect Prediction Portal Media Bisnis Syariah Menggunakan Teknologi Machine Learning

Penelitian ini bertujuan untuk mengembangkan prediksi cacat perangkat lunak dengan menggunakan dataset dari Bisnissyariah, sebuah situs forum yang berisi berita-berita terupdate terkait bisnis syariah. Pendekatan penelitian yang digunakan adalah rancangan penelitian yang simpel, dimaksudkan agar dapat dengan mudah dipahami oleh pembaca. Metode yang diterapkan dalam penelitian ini melibatkan model machine learning, termasuk Random Forest, Gradient Boosting, dan Support Vector Machine. Penggunaan model-model tersebut bertujuan untuk mengevaluasi dan membandingkan akurasi prediksi cacat perangkat lunak. Hasil penelitian menunjukkan bahwa model Random Forest memberikan hasil terbaik dengan tingkat akurasi sebesar 96,7%. Hasil ini menunjukkan bahwa model tersebut sangat efektif dalam memprediksi cacat perangkat lunak berdasarkan data yang berasal dari Bisnissyariah. Temuan ini memiliki implikasi penting dalam pengembangan perangkat lunak yang lebih andal dan berkualitas, khususnya dalam konteks bisnis syariah. Penelitian ini memberikan kontribusi berharga dalam meningkatkan pemahaman tentang prediksi cacat perangkat lunak dengan menggunakan data dari sumber seperti Bisnissyariah.

KATA KUNCI

Bisnissyariah.co.id,Defect Software,machine learning,Akurasi,Evaluasi Confusion Matrix

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

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