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All Journal Saintek Perikanan : Indonesian Journal of Fisheries Science and Technology Jurnal Harpodon Borneo Sains Akuakultur Tropis : Indonesian Journal of Tropical Aquaculture TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol MARLIN : Marine and Fisheries Science Technology Journal Juvenil: Jurnal Ilmiah Kelautan dan Perikanan DedikasiMU: Journal of Community Service Jurnal Bluefin Fisheries Mahseer : Jurnal Ilmu-ilmu Perairan dan Perikanan Jurnal Penelitian Chanos Chanos
Jayanti, Shara
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PARAMETER KUALITAS AIR PADA TAMBAK PEMBESARAN UDANG VANNAMEI (Litopenaeus vannamei) DI CV. SUKSES INDAH PRIMA, SITUBONDO, JAWA TIMUR jayanti, shara; Maharani, Chafsoh Attyra; Anna, Fauziah; M. E, Fauzi
MARLIN Vol 5, No 1 (2024): (FEBRUARI) 2024
Publisher : Politeknik Kelautan dan Perikanan Pangandaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/marlin.V5.I1.2024.59-78

Abstract

This study aims to determine the effect of air quality dynamics in ponds owned by CV. Sukse Indah Prima Situbondo, East Java on ADG and ABW and shrimp mortality. The air quality results for the C2 plot during the checking were optimal, that is, they met the standard, even though the results were different from the 2014 laboratory/SNI standards. The results of the checking on the C6 plot contained several optimal checking results. However, there are still a number of checks that exceed the standard and experience a drastic increase or decrease, such as ammonium and alkalinity, as well as TVC whose brightness fluctuates drastically. The survival of the C2 plot was 95%, the C6 plot was 60%, the mortality in the C2 plot was 5%, and the C6 plot was 40%, and the ABW results experienced an increase in weight and length for each sampling. As a comparison, ADG decreased in the C2 DOC 54 plot, while the C6 plot experienced an optimal increase. 
Sistem Deteksi Penyakit Ikan Koi Menggunakan Metode YOLOv5 Pratiwi, Citra Zaskia; Jayanti, Shara; Subiantoro, Raedy Anwar; Ziliwu, Boby Wisely; Mustono, Eddy
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 3 (2025): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v11n3.302-314

Abstract

Penyebab utama peningkatan kematian di budidaya perikanan adalah penyakit ikan. Salah satu ikan yang mudah terserang hama dan penyakit adalah ikan koi. Identifikasi penyakit ikan koi secara otomatis pada tahap awal merupakan langkah penting untuk mencegah penyebaran penyakit. Deteksi penyakit ikan koi dapat dilakukan melalui berbagai cara yaitu pemeriksaan visual, penggunaan sensor fisik, analisis genetik, teknologi citra dan pengolahan citra, biosensor dan biochips, teknologi saringan molekuler, jaringan saraf tiruan (artificial neural networks) dan pembelajaran mesin. Sistem deteksi penyakit ikan koi pada penelitian ini menggunakan YOLOv5. Hal ini dikarenakan YOLOv5 memiliki beberapa kelebihan antara lain memiliki tingkat akurasi yang tinggi, dapat mendeteksi secara real time, model ringan, sederhana dalam training, dan open source. Penelitian ini melalui beberapa tahap, yaitu pengumpulan dan penyiapan data, pelatihan model dengan algoritma YOLOv5, serta proses evaluasi terhadap performa model. Pada tahap ini, model dievaluasi berdasarkan nilai accuracy, recall, precision, dan mean Average Precision (mAP). Nilai accuracy sebesar 90% didapatkan sebagai hasil evaluasi model, nilai precision untuk ikan sehat (healthy-fish) sebesar 83,33% sedangkan untuk ikan sakit (sick-fish) sebesar 80%, recall sebesar 100%, dan mean Average Percision (mAP) sebesar 81,67%. Hal ini menunjukan bahwa model mampu mengklasifikasikan secara akurat ikan sehat dan ikan sakit. The main reason for higher mortality in aquaculture is fish-related diseases. One type of fish that is highly susceptible to pests and diseases is the koi fish. Early-stage automatic identification of koi fish diseases is an essential step in preventing the spread of infection. Koi fish disease detection can be conducted through various methods, including visual inspection, physical sensors, genetic analysis, image technology and image processing, biosensors and biochips, molecular screening technology, artificial neural networks, and machine learning. The disease detection system in this study uses YOLOv5, due to its several advantages: high accuracy, real-time detection capability, lightweight model, simplicity in training, and being open-source. This research comprises a series of steps, starting from data preparation and model training using YOLOv5, to the evaluation process which measures accuracy, precision, recall, and mean Average Precision (mAP). A 90% accuracy was achieved through the evaluation of the model, precision scores were 83.33% for healthy fish and 80% for sick fish. The model achieved a recall score was 100%, with mAP score was 81.67%. This model evaluation confirms the accurate detection of both healthy and sick fish.
Co-Authors Anna, Fauziah Asmarani, Anja Asmarany, Anja Budi Rianto Wahidi Fauzi, M E Fauzi, Muhammad Evan Fikriyah, Amiqatul Hamdani Hamdani Harun Harun INDAH PUSPITASARI Insivitawati, Era M. E, Fauzi Maharani, Chafsoh Attyra Maretha, Zayafika Muhammad, Ath-Thaariq Gusti Mustono, Eddy Mu’amar Abdan Nisa Hakimah Pratiwi, Citra Zaskia Primasari, Kartika Puspitasari, I Putri, Tasya Utami Raden Mohamad Herdian Bhakti Rumayasa, Yudana IGP Gede S, Dewi N Samsuli, Said Setyatuti, Tri Ari Subiantoro, Raedy Anwar Sugianti, Budi Sukamto, D Suseno, Dewi Nurmalita Syofriani, Syofriani W, Budi R Yudana, IGP Gede Rumayasa Ziliwu, Boby Wisely