Conditional Tabular Gan (CTGAN) Untuk Meningkatkan Akurasi Prediksi Kelangsungan Hidup Penderita Gagal Jantung

• AHMAD MAULID RIDWAN
• 14210252

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ABSTRAK

ABSTRAK

Nama              : Ahmad Maulid Ridwan

NIM                 : 14210252

Program Studi : Ilmu Komputer

Fakultas           : Teknologi Informasi

Jenjang            : Strata Dua (S2)

Konsentrasi     : Data Mining

Judul          : “Conditional Tabular Gan (CTGAN) Untuk Meningkatkan Akurasi Prediksi Kelangsungan Hidup Penderita Gagal Jantung”

Penyakit jantung, sebagai penyebab utama kematian global, memerlukan diagnosis dini yang efektif. Penelitian ini menerapkan Conditional Tabular GAN (CTGAN) pada dataset "heart_failure_clinical_records_dataset.csv" dari UCI. Menggunakan delapan model machine learning, termasuk AdaBoost Classifier, Random Forest dan Gradient Boosting Classifier, CTGAN digunakan untuk oversampling pada kelas minoritas. Hasilnya menunjukkan akurasi unggul dari AdaBoost Classifier (93%), Random Forest (93%) dan Gradient Boosting Classifier (92%), dengan skor F1 yang seimbang. Penggunaan CTGAN berhasil meningkatkan keseimbangan kelas melalui data sintetis yang mendekati distribusi asli. Evaluasi menyeluruh terhadap model memberikan wawasan mendalam tentang kemampuan prediksi kelangsungan hidup penderita gagal jantung. Penelitian ini berpotensi memajukan sistem pendukung keputusan untuk prognosis yang lebih baik, mengintegrasikan teknologi CTGAN dan machine learning.

KATA KUNCI

Conditional Tabular GAN (CTGAN),Gradient Boosting,Penyakit jantung

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DAFTAR PUSTAKA

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