{
      "department": "FAKULTAS SAINS DAN TEKNOLOGI",
      "subjects": [
        6.3
      ],
      "eprintid": 78782,
      "thesis_type": "masters",
      "date": "2026-08-11",
      "userid": 12460,
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            "eprintid": 78782,
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                  "filename": "24206051019_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf"
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            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1079725",
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            "pos": 1,
            "formatdesc": "OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING"
          },
          {
            "language": "id",
            "placement": 2,
            "eprintid": 78782,
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                  "filename": "24206051019_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf"
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            ],
            "content": "published",
            "rev_number": 3,
            "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/document\/1079726",
            "main": "24206051019_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf",
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            "pos": 2,
            "formatdesc": "OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING"
          }
      ],
      "rev_number": 10,
      "creators": [
        {
          "name": {
            "lineage": null,
            "given": "NIM.: 24206051019",
            "honourific": null,
            "family": "Haerul Anam"
          }
        }
      ],
      "dir": "disk0\/00\/07\/87\/82",
      "keywords": "Dense Angle Encoding, Hybrid QCNN, Karakter Arab, Quantum Machine Learning, Variational Quantum Circuit",
      "lastmod": "2026-10-07 02:12:31",
      "ispublished": "pub",
      "metadata_visibility": "show",
      "date_type": "published",
      "eprint_status": "archive",
      "status_changed": "2026-10-07 02:12:31",
      "datestamp": "2026-10-07 02:12:31",
      "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/eprint\/78782",
      "thesis_name": "other",
      "note": "Prof. Dr. Ir. Shofwatul 'Uyun, S.T., M.Kom., IPM., ASEAN Eng.",
      "full_text_status": "restricted",
      "contact_email": "muh.khabib@uin-suka.ac.id",
      "divisions": [
        "S2_inf"
      ],
      "abstract": "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",
      "type": "thesis",
      "title": "OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING",
      "institution": "UIN SUNAN KALIJAGA YOGYAKARTA",
      "pages": 118
    }