Analisis Perbandingan ANN dan Hybrid QNN pada Klasifikasi Multikarakteristik Data
Abstract
The rapid advancement of machine learning has driven the exploration of various computational models to address classification problems across datasets with diverse characteristics. This study aimed to compare the performance of Artificial Neural Network (ANN) and Hybrid Quantum Neural Network (Hybrid QNN) under controlled experimental conditions. Three benchmark datasets, namely Red Wine Quality, Banknote Authentication, and Leukemia Gene Expression, were used with a training and test data split of 80:20. The evaluation was conducted using accuracy, precision, recall, F1-score, and computational time. The results showed that on the Gene Expression Leukemia dataset, Hybrid QNN achieved an accuracy of 0.9333, significantly outperforming ANN, which obtained 0.6111. Conversely, ANN demonstrated competitive performance and higher computational efficiency on low- and medium-dimensional datasets. These findings indicate that the advantages of Hybrid QNN are contextual and strongly dependent on data characteristics.
Keywords: Artificial Neural Network; Hybrid Quantum Neural Network; classification; machine learning
Abstrak
Perkembangan pesat pembelajaran mesin telah mendorong eksplorasi berbagai model komputasi untuk menyelesaikan permasalahan klasifikasi pada data dengan karakteristik yang beragam. Penelitian ini bertujuan untuk membandingkan kinerja Artificial Neural Network (ANN) dan Hybrid Quantum Neural Network (Hybrid QNN) dalam kondisi eksperimen yang terkontrol. Tiga dataset acuan digunakan, yaitu Red Wine Quality, Banknote Authentication, dan Gene Expression Leukemia, dengan pembagian data latih dan uji sebesar 80:20. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, F1-score, serta waktu komputasi. Hasil eksperimen menunjukkan bahwa pada dataset Gene Expression Leukemia, Hybrid QNN mencapai akurasi 0,9333, jauh lebih tinggi dibandingkan ANN sebesar 0,6111. Sebaliknya, ANN menunjukkan performa yang kompetitif dan lebih efisien pada dataset berdimensi rendah hingga menengah. Temuan ini menunjukkan bahwa keunggulan Hybrid QNN bersifat kontekstual dan bergantung pada karakteristik data.
Keywords
References
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