eprintid: 49801 rev_number: 17 eprint_status: archive userid: 14704 dir: disk0/00/04/98/01 datestamp: 2025-06-23 02:40:13 lastmod: 2025-06-23 02:40:13 status_changed: 2025-06-23 02:40:13 type: thesis metadata_visibility: show contact_email: 3332200056@untirta.ac.id creators_name: Nur Aviatna, Irfansyah creators_id: 3332200056 contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_name: Alimuddin, Alimuddin contributors_name: Fahrizal, Rian contributors_id: 197204172008121004 contributors_id: 197510262005011001 corp_creators: Fakultas Teknik Universitas Sultan Ageng Tirtayasa title: KLASIFIKASI KEMATANGAN BUAH KELAPA SAWIT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK ispublished: pub subjects: SB subjects: TK subjects: TR divisions: Elektro full_text_status: restricted keywords: Kematangan Buah Kelapa Sawit, Klasifikasi, CNN, EfficientNetV2-B0, Transfer Learning abstract: Kematangan buah kelapa sawit menjadi faktor penting yang memengaruhi kualitas dan kuantitas minyak yang dihasilkan. Penentuan tingkat kematangan buah secara manual sering kali bersifat subjektif, sehingga diperlukan metode otomatis yang lebih efisien dan akurat. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi kematangan buah kelapa sawit menggunakan metode Convolutional Neural Network (CNN) dengan menggunakan arsitektur EfficientNetV2-B0 sebagai transfer learning untuk meningkatkan performa model dalam klasifikasi gambar. Penelitian ini memanfaatkan data berupa citra buah kelapa sawit yang mencakup berbagai tingkat kematangan, yakni mentah, matang, dan terlalu matang. Dataset ini diproses melalui beberapa tahap seperti prapemrosesan dan augmentasi data untuk meningkatkan variasi data. Setelah itu model dilatih dengan alokasi data dan learning rate yang berbeda-beda. Hasil penelitian menunjukkan bahwa penggunaan alokasi data dan learning rate yang berbeda-beda memengaruhi performa model, serta terdapat empat model yang dilatih dengan alokasi data 90/10 dan variasi learning rate 0,0001, 0,0005, 0,00075, dan 0,0009 berhasil mencapai accuracy dan f1-score sebesar 100% dalam klasifikasi tingkat kematangan buah kelapa sawit. date: 2025-06-21 date_type: published pages: 89 institution: Fakultas Teknik Universitas Sultan Ageng Tirtayasa department: Teknik Elektro thesis_type: sarjana thesis_name: sarjana referencetext: [1] WWF, “Palm Oil.” [Daring]. Tersedia pada: https://wwf.panda.org/discover/our_focus/food_practice/sustainable_production/palm_oil/ [2] USDA, “Production - Palm Oil,” [Daring]. Tersedia pada: https://fas.usda.gov/data/production/commodity/4243000 [3] Himmah E. F., M. Widyaningsih, dan M. 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