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22.11.5180.zip", "filesize": 185788, "mime_type": "application\/zip" } ], "eprintid": 33028, "main": "Sourcecode - 22.11.5180.zip", "formatdesc": "SOURCE CODE", "language": "id" }, { "formatdesc": "PUBLIKASI", "language": "id", "main": "Publikasi.pdf", "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/328936", "content": "published", "eprintid": 33028, "docid": 328936, "files": [ { "fileid": 1158884, "hash": "1d40309a9fb04dc634907cb13f75dcf3", "datasetid": "document", "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1158884", "mime_type": "application\/pdf", "hash_type": "MD5", "objectid": 328936, "filesize": 584399, "filename": "Publikasi.pdf", "mtime": "2026-09-29 02:21:05" } ], "placement": 9, "format": "text", "mime_type": "application\/pdf", "pos": 9, "rev_number": 2, "security": "staffonly" }, { "security": "public", "rev_number": 5, "placement": 1, "format": "text", "pos": 10, "mime_type": "application\/pdf", "main": "COVER.pdf", "content": "published", "eprintid": 33028, "docid": 328946, "files": [ { "filename": "COVER.pdf", "mtime": "2026-09-29 02:25:19", "filesize": 1033472, "hash_type": "MD5", "objectid": 328946, "mime_type": "application\/pdf", "uri": "http:\/\/eprints.amikom.ac.id\/id\/file\/1158913", "datasetid": "document", "hash": "8f49109a633cbbadf5220584295185a8", "fileid": 1158913 } ], "uri": "http:\/\/eprints.amikom.ac.id\/id\/document\/328946", "language": "id", "formatdesc": "COVER-ABSTRAK" } ], "rev_number": 23, "creators": [ { "nim": "22.11.5180", "name": { "honourific": null, "lineage": null, "family": "Restu", "given": "Arsenius Angga" } } ], "status_changed": "2026-09-29 02:29:19", "dir": "disk0\/00\/03\/30\/28", "eprint_status": "archive", "type": "thesis", "thesis_name": "skripsi", "title": "ANALISIS ALGORITMA HYBRID CNN-SVM UNTUK\r\nKLASIFIKASI CIRTA JENIS SAMPAH", "metadata_visibility": "show", "institution": "Universitas AMIKOM Yogyakarta", "thesis_type": "bachelor", "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/33028", "datestamp": "2026-09-29 02:29:19", "ispublished": "pub", "date": "2026-03-11", "full_text_status": "restricted", "abstract": "Sistem manajemen sampah modern membutuhklan klasifikasi material yang\r\ncepat dan akurat, namun metode konvesional berbasis inspeksi visual manual\r\nmenghadapi kendala subjektivitas dan kurangannya skalabilitas. Penerapan Deep\r\nLearning, khususnya Counvolutional Neural Network (CNN), telah menawarkan\r\nSolusi otomatisasi, tetapi penelitian menunjukan adanya masalah Generalization\r\nGap: model yang dilatih pada dataset citra sampah bersih (pristine) mencapai\r\nakurasi tinggi (>90%) di lingkungan yang terkontrol, tetapi anjlok drastis (<50%)\r\nKetika diterapkan pada real-life TPA yang kotor, tumpeng tindih, dan memiliki \r\npencahayaan buruk. Untuk mengatasi kesenjangan akurasi ini, penelitanini \r\nmengusulkan implementasi Algoritma Hybrid CNN-SVM, yang menggabungkan\r\nkemampuan ekstraksi fitur yang kuat dari arsitektur CNN (SVM). Penelitian ini\r\nbertujuan untuk menganalisis model Hybrid CNN-SVM mana (terbatas pada model\r\nCNN sebagai feature extractor) yang mampu meningkatkan metrik akurasi, presisi,\r\ndan recall dalam klasifikasi kategori jenis sampah (real-live), serta melakukan\r\nstudi komparatif terhadap model tunggal CNN dan SVM. Hasil dari penelitian ini\r\ndiharapkan dapat memberikan justifikasi empiris mengenai keunggulan pendekatan\r\nhybrid deep learning dalam menciptakan model yang robust terhadap dataset\r\nkompleks dan menjadi rekomendasi teknis yang solid untuk system memilihan\r\nsampah otomatis di masa depan.", "date_type": "published", "contributors": [ { "name": { "lineage": null, "honourific": null, "given": "Hartatik", "family": "-" } } ], "userid": 12, "corp_creators": [ "Universitas AMIKOM Yogyakarta" ], "subjects": [ "000.000.005" ], "lastmod": "2026-09-29 02:29:19", "eprintid": 33028, "department": "Fakultas Ilmu Komputer" }