{ "status_changed": "2026-09-11 02:29:18", "corp_creators": [ "Universitas Bojonegoro" ], "thesis_name": "sarjana", "type": "thesis", "abstract": "Penyakit daun pada tanaman padi, cabai, dan terong merupakan salah satu faktor utama yang menurunkan produktivitas pertanian. Identifikasi manual memiliki keterbatasan berupa subjektivitas tinggi dan memakan waktu yang lama. Penelitian ini bertujuan merancang sistem deteksi penyakit daun tanaman multi jenis berbasis Convolutional Neural Network (CNN) dengan bantuan box akrilik hitam (20\u00d716\u00d718 cm) dan pencahayaan LED 5V untuk menjamin konsistensi akuisisi citra via smartphone. Dataset yang digunakan berjumlah 2.974 citra yang terbagi ke dalam tujuh kelas penyakit daun. Pelatihan model menggunakan arsitektur YOLOv8 berbasis CNN dilakukan melalui Google Collab dan diimplementasikan menggunakan platform Edge Impulse. Hasil pelatihan model menunjukkan performa yang sangat baik, precision sebesar 94.54%, recall sebesar 88,14 F1-Score sebesar 91,49%. Uji kinerja web detection secara riil di lapangan menggunakan smartphone dengan 210 sampel baru di dalam box menghasilkan nilai akurasi sebesar 80,48%, rata-rata presisi 90,80%, recall 87,50%, dan F1-score 89,08%. Evaluasi tingkat kesalahan prediksi menunjukkan nilai MAE sebesar 0,667, RMSE sebesar 0,86, dan MAPE sebesar 18,61% yang termasuk dalam kategori \"Baik\" (Good Prediction). Penggunaan kotak uji terbukti efektif menghasilkan citra yang konsisten sehingga menjaga stabilitas performa model. Sistem ini diharapkan dapat menjadi solusi digital yang cepat, objektif, dan akurat dalam membantu petani pemula meminimalkan risiko gagal panen.", "keywords": "penyakit daun tanaman, CNN, YOLOv8, kotak uji, Edge Impulse, petani pemula.", "datestamp": "2026-09-11 02:29:18", "ispublished": "pub", "rev_number": 18, "divisions": [ "ti" ], "thesis_type": "bachelor", "institution": "Sains dan Teknik", "metadata_visibility": "show", "userid": 1735, "documents": [ { "main": "ABSTRAK.pdf", "pos": 1, "placement": 1, "security": "staffonly", "rev_number": 2, "files": [ { "filesize": 760471, "uri": "https://repository.unigoro.ac.id/id/file/105836", "objectid": 31313, "mime_type": "application/pdf", "hash_type": "MD5", "fileid": 105836, "datasetid": "document", "mtime": "2026-09-10 06:56:37", "filename": "ABSTRAK.pdf", "hash": "ea5a52ba98a28d957d0bb51651bbce23" } ], "docid": 31313, "uri": "https://repository.unigoro.ac.id/id/document/31313", "mime_type": "application/pdf", "language": "en", 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"fileid": 105844, "datasetid": "document", "mtime": "2026-09-10 07:01:16", "filename": "BAB IV.pdf", "hash": "bfa9fb3091f566288fdc6da3979b73a7" } ], "docid": 31317, "uri": "https://repository.unigoro.ac.id/id/document/31317", "mime_type": "application/pdf", "language": "en", "format": "text", "eprintid": 6436 }, { "main": "BAB V.pdf", "pos": 6, "placement": 6, "security": "public", "rev_number": 1, "files": [ { "filesize": 232122, "uri": "https://repository.unigoro.ac.id/id/file/105846", "objectid": 31318, "mime_type": "application/pdf", "hash_type": "MD5", "fileid": 105846, "datasetid": "document", "mtime": "2026-09-10 07:02:27", "filename": "BAB V.pdf", "hash": "5427b539ab68ca1d0f56ad963b33800f" } ], "docid": 31318, "uri": "https://repository.unigoro.ac.id/id/document/31318", "mime_type": "application/pdf", "language": "en", "format": "text", "eprintid": 6436 }, { "main": "LAMPIRAN.pdf", "pos": 7, "placement": 7, "security": "validuser", "rev_number": 2, "files": [ { "filesize": 2125535, "uri": "https://repository.unigoro.ac.id/id/file/105848", "objectid": 31319, "mime_type": "application/pdf", "hash_type": "MD5", "fileid": 105848, "datasetid": "document", "mtime": "2026-09-10 07:03:27", "filename": "LAMPIRAN.pdf", "hash": "ca0f6f54c1cd91d67172285e71ed35b4" } ], "docid": 31319, "uri": "https://repository.unigoro.ac.id/id/document/31319", "mime_type": "application/pdf", "language": "en", "format": "text", "eprintid": 6436 } ], "department": "Teknik Industri", "full_text_status": "restricted", "date": "2026-07-29", "dir": "disk0/00/00/64/36", "eprint_status": "archive", "creators": [ { "name": { "lineage": null, "family": "Nanang Supriyadi", "honourific": null, "given": "Supriyadi" } } ], "title": "Desain Sistem Deteksi Penyakit Daun Untuk Tanaman Multi Jenis Berbasis Convolutional Neural Network (CNN) Untuk Membantu Petani Pemula", "subjects": [ "CN" ], "contributors": [ { "name": { "lineage": null, "family": "Heru Prastiyono", "honourific": null, "given": "Prastiyono" } }, { "name": { "lineage": null, "family": "Faisal Ashari", "honourific": null, "given": "Ashari" } } ], "uri": "https://repository.unigoro.ac.id/id/eprint/6436", "lastmod": "2026-09-11 02:29:18", "note": 22262011046, "date_type": "published", "eprintid": 6436 }