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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Artificial Intelligence and Digital Business Journal of Sustainable Innovation Engineering
Takyudin, Takyudin
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing 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.
Penerapan Metode Weighted Product untuk Evaluasi Kinerja Dosen di Prodi Sistem Informasi UNPRI PSDKU Pekanbaru Sihombing, Aland Polma Naek; Takyudin, Takyudin; Selvanda, Alifia Restu
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5454

Abstract

Universitas Prima Indonesia PSDKU yang berlokasi di Kota Pekanbaru secara rutin melaksanakan Penilaian Kinerja Dosen (PKD) setiap tahun sebagai dasar evaluasi dan pemilihan dosen terbaik. Namun, proses penilaian yang berjalan saat ini masih dilakukan secara manual, sehingga menimbulkan berbagai permasalahan seperti ketidakefisienan waktu, potensi subjektivitas penilaian, serta kesulitan dalam mengelola dan mengolah data kinerja dosen berdasarkan banyak kriteria. Kriteria yang digunakan dalam penilaian meliputi evaluasi mahasiswa, kedisiplinan mengajar, alokasi waktu mengajar, kualifikasi akademik, jabatan fungsional, serta jumlah dan kualitas karya ilmiah yang dihasilkan oleh dosen. Untuk mengatasi permasalahan tersebut, penelitian ini mengembangkan Sistem Pendukung Keputusan (SPK) menggunakan metode Weighted Product (WP) guna membantu pihak institusi dalam melakukan seleksi dan pemeringkatan dosen secara objektif, sistematis, dan terukur. Metode Weighted Product dipilih karena mampu mengolah kriteria bertipe biaya (cost) dan manfaat (benefit) melalui proses pembobotan dan perkalian nilai kriteria, sehingga menghasilkan peringkat alternatif yang lebih akurat dan konsisten. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu memberikan rekomendasi dosen terbaik secara transparan serta mudah dipahami oleh pengambil keputusan. Berdasarkan perhitungan metode WP, dosen atas nama Jeri memperoleh nilai preferensi tertinggi dengan bobot kriteria biaya sebesar 0,2, manfaat pengabdian dan komunikasi sebesar 0,4, serta manfaat penelitian sebesar 0,5. Dengan demikian, penerapan SPK ini terbukti dapat meningkatkan efektivitas, transparansi, dan akurasi dalam proses penilaian kinerja dosen, serta mendukung pengambilan keputusan strategis di Universitas Prima Indonesia PSDKU.
Innovative Edge Detection Approaches for Arabic Character Recognition in Kitab Kuning Images: A Comparison of Sobel, Prewitt, and Canny takyudin, takyudin
Journal of Sustainable Innovation Engineering Vol. 1 No. 3 (2025): November
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/sustainable.v1i3.20

Abstract

The preservation of Kitab Kuning manuscripts as part of Islamic intellectual heritage requires digitalization and image enhancement to ensure readability and sustainability. One of the essential stages in digital manuscript processing is edge detection, which plays an important role in extracting the structural shape of Arabic characters. This study aims to compare the performance of three classical edge detection methods—Sobel, Prewitt, and Canny—in detecting the edges of Arabic characters in Kitab Kuning manuscript images. Each manuscript image was preprocessed through grayscale conversion and Gaussian filtering before applying the three edge detection algorithms. The resulting edge images were evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The experimental results show that the Canny method consistently produces lower MSE values, higher PSNR, and more stable SSIM compared to Sobel and Prewitt, resulting in clearer, smoother, and more accurate edge structures resembling the original Arabic characters. Prewitt demonstrates moderate performance, while Sobel tends to generate rougher edges and is more sensitive to noise. Based on these findings, the Canny method is recommended as the most effective approach for Arabic character edge detection in the digital preservation of Kitab Kuning manuscripts, supporting better quality enhancement and long-term usability of digital Islamic manuscripts
Co-Authors Aland Polma Naek Sihombing Hamsar , Ali Iskandar Fitri, Iskandar Putra , Jonni Adi Rais , Muhammad Sandi Selvanda, Alifia Restu Yuhandri Yuhandri, Yuhandri