TY  - THES
N1  - Prof. Dr. Ir. Shofwatul 'Uyun, S.T., M.Kom., IPM., ASEAN Eng.
ID  - digilib78782
UR  - https://digilib.uin-suka.ac.id/id/eprint/78782/
A1  - Haerul Anam, NIM.: 24206051019
Y1  - 2026/08/11/
N2  - 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
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - Dense Angle Encoding
KW  -  Hybrid QCNN
KW  -  Karakter Arab
KW  -  Quantum Machine Learning
KW  -  Variational Quantum Circuit
M1  - masters
TI  - OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING
AV  - restricted
EP  - 118
ER  -