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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Artificial Intelligence and Digital Business
Takyudin, Takyudin
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Catfish Fry Detection and Counting Using YOLO Algorithm Takyudin, Takyudin; Fitri, Iskandar; Yuhandri, Yuhandri
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6746

Abstract

The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss. The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfish puppy object that is given a bounding box and the detection label displays the class name and precision value. The dataset amounted to 321 images of catfish puppies from internet and photography sources that were trained to produce a new digital image model. The number of split training, validation and testing datasets is worth 831 annotation images, 83 validation images and 83 images for the testing process. The value of the training model mAP 50.39 %, Precision 61.17 % and Recall 58 % Detection test results based on the YOLO method obtained an accuracy rate of 65.7%. The avg loss value in the final model built with YOLO is 4.6%. Based on the results of tests carried out with the number of objects 50 to 500 tail size 2-8 cm using video, objects in the image are successfully recognized with an accuracy of 63% to 70%. Calculations using the YOLO algorithm show quite good results.
Metode Deteksi dan Estimasi Luas Lubang Jalan Menggunakan Deep Learning Berbasis YOLOv11 Takyudin, Takyudin; Rais , Muhammad Sandi; Putra , Jonni Adi; Hamsar , Ali
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1760

Abstract

Kondisi infrastruktur jalan yang rusak khususnya keberadaan lubang jalan dapat berdampak signifikan terhadap keselamatan berkendara. Efisiensi transportasi serta biaya distribusi barang menjadi komponen penting perekonomian. Deteksi kerusakan jalan secara manual dinilai kurang efisien dan rentan terhadap kesalahan manusia, terutama dalam skala besar dan waktu nyata. Penelitian ini bertujuan untuk mengembangkan sistem deteksi dan estimasi luas lubang jalan secara real-time menggunakan algoritma YOLOv11 berbasis deep learning. Model YOLOv11 dipilih karena kemampuannya dalam mendeteksi objek berukuran kecil dengan presisi tinggi serta kecepatan inferensi yang optimal. Sistem yang dibangun melibatkan proses pengumpulan data citra jalan berlubang, pelatihan model menggunakan dataset relevan, serta pengembangan aplikasi web berbasis Flask untuk mempermudah pengguna dalam mengunggah gambar dan melihat hasil deteksi. Berdasarkan hasil pelatihan, model menunjukkan performa yang stabil dan konvergen dengan nilai mAP@0.5 mencapai 0.8. Evaluasi model melalui confusion matrix menghasilkan nilai precision sebesar 75,1%, recall 76,6%, dan F1-score sebesar 75,8%, yang menunjukkan bahwa model memiliki kemampuan deteksi yang baik dan seimbang. Sistem ini diharapkan dapat menjadi solusi awal yang inovatif untuk mendukung pemeliharaan jalan yang lebih akurat, cepat, dan efisien, serta membantu instansi terkait dalam pengambilan keputusan perbaikan infrastruktur secara berkelanjutan.
Co-Authors Hamsar , Ali Iskandar Fitri, Iskandar Putra , Jonni Adi Rais , Muhammad Sandi Yuhandri Yuhandri, Yuhandri