http://digilib.uin-suka.ac.id/id/eprint/78782 2026-10-07T02:12:31Z info:eu-repo/semantics/descriptiveMetadata https://digilib.uin-suka.ac.id/id/eprint/78782/ OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING Haerul Anam, NIM.: 24206051019 006.3 Artificial Intelligence (Kecerdasan Buatan) 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 2026-08-11 Thesis NonPeerReviewed text id https://digilib.uin-suka.ac.id/id/eprint/78782/1/24206051019_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf text id https://digilib.uin-suka.ac.id/id/eprint/78782/2/24206051019_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf Haerul Anam, NIM.: 24206051019 (2026) OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING. Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA. info:eu-repo/semantics/objectFile info:eu-repo/semantics/objectFile info:eu-repo/semantics/humanStartPage