%A NIM.: 24206051019 Haerul Anam
%O Prof. Dr. Ir. Shofwatul 'Uyun, S.T., M.Kom., IPM., ASEAN Eng.
%T OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING
%X Handwritten Arabic character recognition faces challenges due to variations in writing styles and morphological similarities, particularly among homoglyph characters that differ in the number or position of dots. In a Hybrid Quantum Convolutional Neural Network (HQCNN), these challenges are also related to limitations in feature representation when latent CNN features are mapped to a quantum circuit. Standard Angle Encoding maps one feature to one qubit, causing the required number of qubits to increase with the feature dimension. This study aims to develop and evaluate a Hybrid Quantum Convolutional Neural Network (HQCNN) with Dense Angle Encoding (DAE) for handwritten Arabic character recognition and to analyze the effect of
%K Dense Angle Encoding, Hybrid QCNN, Karakter Arab, Quantum Machine Learning, Variational Quantum Circuit
%D 2026
%I UIN SUNAN KALIJAGA YOGYAKARTA
%L digilib78782