{
      "department": "FAKULTAS SAINS DAN TEKNOLOGI",
      "subjects": [
        "004."
      ],
      "eprintid": 78918,
      "thesis_type": "skripsi",
      "date": "2026-07-07",
      "userid": 12243,
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                  "filename": "22106050023_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\/1081329",
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            "pos": 1,
            "formatdesc": "KLASIFIKASI PENYAKIT PNEUMONIA DENGAN MODEL FUSI CITRA MULTIMODAL MENGGUNAKAN PENDEKATAN DEMPSTER-SHAFER THEORY"
          },
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            "placement": 2,
            "eprintid": 78918,
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            "rev_number": 3,
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            "formatdesc": "KLASIFIKASI PENYAKIT PNEUMONIA DENGAN MODEL FUSI CITRA MULTIMODAL MENGGUNAKAN PENDEKATAN DEMPSTER-SHAFER THEORY"
          }
      ],
      "rev_number": 10,
      "creators": [
        {
          "name": {
            "lineage": null,
            "given": "NIM.: 22106050023",
            "honourific": null,
            "family": "Hernadhif Rafif Wiryawan"
          }
        }
      ],
      "dir": "disk0\/00\/07\/89\/18",
      "keywords": "pneumonia; multimodal imaging; evidential neural network;  Demspter-Shafer Theory",
      "lastmod": "2026-10-09 02:46:32",
      "ispublished": "pub",
      "metadata_visibility": "show",
      "date_type": "published",
      "eprint_status": "archive",
      "status_changed": "2026-10-09 02:46:32",
      "datestamp": "2026-10-09 02:46:32",
      "uri": "http:\/\/digilib.uin-suka.ac.id\/id\/eprint\/78918",
      "thesis_name": "other",
      "note": "Dr. Siti Mutmainah, S.Kom, M.Cs.",
      "full_text_status": "public",
      "contact_email": "muchti.nurhidaya@uin-suka.ac.id",
      "divisions": [
        "Informatika(S1)"
      ],
      "abstract": "Pneumonia is a lung infection that ranks among the leading causes of death in children under five, with mortality rates continuing to rise as children get older. This study proposes a pneumonia classification model based on multimodal imaging that integrates an Evidential Neural Network (ENN) based on the Dempster-Shafer Theory (DST) framework. The model was built through five main stages: data collection, preprocessing, feature extraction using ResNet-34, followed by training the ENN model independently for each modality, and concluding with the combination of mass functions using Dempster’s rule of combination. Model evaluation was conducted using the",
      "type": "thesis",
      "title": "KLASIFIKASI PENYAKIT PNEUMONIA DENGAN MODEL FUSI CITRA MULTIMODAL MENGGUNAKAN PENDEKATAN DEMPSTER-SHAFER THEORY",
      "institution": "UIN SUNAN KALIJAGA YOGYAKARTA",
      "pages": 60
    }