eprintid: 51741 rev_number: 19 eprint_status: archive userid: 11265 dir: disk0/00/05/17/41 datestamp: 2025-07-16 03:22:14 lastmod: 2025-07-16 03:22:14 status_changed: 2025-07-16 03:22:14 type: thesis metadata_visibility: show contact_email: rifkialain08@gmail.com creators_name: Alain, Rifki creators_id: 3332180003 contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_name: Saraswati, Irma contributors_name: Rian, Fahrizal contributors_id: 197807242003122001 contributors_id: 197510262005011001 corp_creators: Fakultas Teknik Universitas Sultan Ageng Tirtayasa title: KLASIFIKASI KESEGARAN IKAN BANDENG DENGAN CITRA INSANG MENGGUNAKAN METODE JARINGAN SARAF TIRUAN LEARNING VECTOR QUANTIZATION ispublished: pub subjects: SH subjects: TK divisions: Elektro full_text_status: restricted keywords: Klasifikasi, Ikan bandeng, Insang, Citra Digital, LVQ, HSV. abstract: Ikan bandeng (chanos chanos) merupakan salah satu komoditas unggulan perikanan di Indonesia yang memiliki nilai gizi tinggi. Nilai gizi yang tinggi ini berbanding lurus dengan peningkatan angka konsumsi ikan di masyarakat. Namun, peningkatan konsumsi ini juga memperbesar risiko peredaran ikan tidak layak konsumsi akibat penurunan mutu selama proses distribusi. Penelitian ini bertujuan mengembangkan sistem klasifikasi kesegaran ikan bandeng secara objektif menggunakan teknologi pengolahan citra digital berbasis warna dengan metode Jaringan Saraf Tiruan (JST) Learning Vector Quantization (LVQ) yang diaplikasikan pada fitur warna (HSV) dari citra insang. Sebanyak 1500 citra insang ikan bandeng yang terbagi ke dalam kelas segar, sedang, dan busuk dikumpulkan secara seimbang, kemudian dipra-pemrosesan dan dibagi menjadi 80% data latih serta 20% data uji. Hasil pengujian menunjukkan bahwa performa model LVQ sangat dipengaruhi oleh jumlah prototype dan nilai learning rate, sedangkan jumlah epoch tidak memberikan dampak signifikan setelah konvergensi awal. Model LVQ mampu mengklasifikasikan tingkat kesegaran ikan bandeng secara efektif dan akurat, mencapai akurasi tertinggi sebesar 94% pada data uji dengan konfigurasi optimal: prototype 3, learning rate 0,001, dan epoch 300. date: 2025-06-02 date_type: published pages: 123 institution: Fakultas Teknik Universitas Sultan Ageng Tirtayasa department: Teknik Elektro thesis_type: sarjana thesis_name: sarjana referencetext: [1] Suprianto, D. S. Lestari, and O. H. Simung, “Aplikasi Penentuan Kesegaran Ikan Bandeng Menggunakan Metode Convolution Neural Network,” INSECT, vol. 8, no. 2, pp. 77–86, 2023, doi: 10.33506/insect.v8i2.2196. [2] G. D. K. Sandi, D. Syauqy, and R. Maulana, “Sistem Pendeteksi Kesegaran Ikan Bandeng Berdasarkan Bau Dan Warna Daging Berbasis Sensor MQ135 Dan TCS3200 Dengan Metode Naive Bayes,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 10, pp. 10110–101117, 2020. [3] Alimudin, Masjudin, V. Vanessha, C. A. Wicaksana, R. Arafiyah, and I. 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