Prediksi Hepatitis C dengan Autoencoder-Based Feature Learning

• YUTRI AMELIA
• 14210237

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ABSTRAK

ABSTRAK

Nama              : Yutri Amelia

NIM                 : 14210237

Program Studi : Ilmu Komputer

Fakultas           : Teknologi Informasi

Jenjang            : Strata Dua (S2)

Konsentrasi      : Data Mining

Judul              : Prediksi Hepatitis C dengan Autoencoder-Based Feature Learning

Hepatitis C merupakan salah satu penyakit yang diakibatkan oleh virus yang menyebabkan tingkat kematian yang lebih besar di seluruh dunia dari penyakit kronis lain yang diderita secara global. Deteksi dini infeksi Hepatitis C sangat penting untuk mencegah penyakit kronis berkembang ke tahap yang lebih serius. Penggabungan beberapa metode machine learning dapat meningkatkan akurasi dalam memprediksi penyakit. Penelitian ini bertujuan untuk mendeteksi dini penyakit Hepatitis C dengan menggunakan autoencoder dan pengklasifikasian menggunakan metode machine learning diantaranya Logistic Regression, Support Vector Machine, K-nearest Neighbours, Random Forest, Decision Tree, Extreme Gradient Boosting, Artifical Neural Network dan Light Gradient Boosting Machine. Sebagai feature learning autoencoder memberikan akurasi lebih baik dari algoritma machine learning tanpa menggunakan autoencoder. Hasilnya, algoritma ANN yang menggunakan autoencoder mengalami kenaikan accuracy tertinggi yaitu sebesar 97%.

KATA KUNCI

Hepatitis C,Autoencoder,machine learning,Deep Learning,Feature learning

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DAFTAR PUSTAKA

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