Analisis Perbandingan Quantum Machine Learning Dalam Klasifikasi Berita Politik Fakta Dan Hoaks

Linda Kristiani Zebua (Universitas Kristen Immanuel)
Sunneng Sandino Berutu (Universitas Kristen Immanuel)
Aninda Astuti (Asia University Taichung City)

Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Vol 15, No 2 (2026): April 2026

This study analyzes the comparative performance of Quantum Machine Learning in classifying factual and hoax political news using three approaches, namely Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), and Hybrid Quantum Kernel with Classical SVM. News data is represented using TF-IDF and its dimensionality is reduced using Principal Component Analysis, then balanced using SMOTE. Feature transformation is carried out to the quantum domain through angle encoding, then applied to the QML model. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score. The experimental results show that QSVC has the best performance with an accuracy of 0.629 and an F1-Score of 0.735, followed by QNN and Hybrid Quantum Kernel Classical SVM. This study proves that the quantum kernel-based approach is effective in classifying medium-dimensional text, while also demonstrating the potential of Quantum Machine Learning as an alternative method for classifying factual and hoax political news.Keywords: Quantum Machine Learning; Quantum Neural Network; Quantum Support Vector Classifier; Hybrid Quantum Kernel; News Classification AbstrakPenelitian ini menganalisis perbandingan kinerja Quantum Machine Learning dalam klasifikasi berita politik fakta dan hoaks dengan menggunakan tiga pendekatan, yaitu Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), dan Hybrid Quantum Kernel dengan Classical SVM. Data berita direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan Principal Component Analysis, kemudian diseimbangkan menggunakan SMOTE. Transformasi fitur dilakukan ke domain kuantum melalui angle encoding, kemudian diterapkan pada model QML. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, dan F1-Score. Hasil eksperimen menunjukkan QSVC memiliki performa terbaik dengan accuracy 0,629 dan F1-Score 0,735, diikuti QNN dan Hybrid Quantum Kernel Classical SVM. Penelitian ini membuktikan bahwa pendekatan berbasis quantum kernel efektif dalam klasifikasi teks berdimensi sedang, sekaligus menunjukkan potensi Quantum Machine Learning sebagai alternatif metode klasifikasi berita politik fakta dan hoaks.