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All Journal Sebatik Information System Journal (INFOS) Equiva
Nugroho, Bowo
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Computer Science & IT Mathematics

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Journal : Sebatik

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KLASIFIKASI KUALITAS HASIL PRODUKSI TAHU PUTIH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Rahmadani, Irwan; Muqimuddin, Muqimuddin; Hertadi, Christopher Davito Prabandewa; Nugroho, Bowo
Sebatik Vol. 27 No. 2 (2023): Desember 2023
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v27i2.2401

Abstract

Penelitian ini mengembangkan sebuah model Convolutional Neural Network (CNN) untuk melakukan klasifikasi kategori mutu tahu berdasarkan citra digital dan menentukan grid produk tahu berdasarkan kategori mutunya. Tahapan penelitian ini meliputi pengambilan sampel data, pelabelan data, preprocessing citra, pembuatan model CNN, training model CNN, evaluasi model CNN dan visualisasi kategorisasi. Sampel data yang digunakan dalam penelitian ini terdiri dari 600 citra tahu yang terbagi menjadi tiga kategori mutu, yaitu mutu A, B, dan C. Peneliti menggunakan metode pembaharuan stokastik (Stochastic Gradient Descent) dengan learning rate 0.001, dan fungsi aktivasi ReLU. Hasil pengembangan menunjukkan bahwa model kedua keseluruhan bentuk memiliki tingkat performansi dan validasi akurasi yang lebih tinggi sebesar 100 % dibandingkan dengan model pertama tampak permukaan sebesar 77%. Model kedua  memiliki arsitektur yang lebih kompleks dan lebih sesuai dengan karakteristik deteksi keseluruhan bentuk pada tahu. Dengan pengembangan model CNN ini, diharapkan industri tahu dapat meningkatkan efisiensi dalam penentuan kualitas tahu dan harga jual yang sesuai. Implementasi teknologi ini memungkinkan kategorisasi mutu tahu yang akurat dan objektif berdasarkan citra digital, yang dapat mengurangi ketergantungan pada penilaian manual.
Implementation of Sparrow Pest Detection Using YOLOv8 Method on Raspberry Pi and Google Coral USB Accelerator Nugroho, Bowo; Azhar, Nur Fajri; Pratama , Boby Mugi; Syakbani, Ahmad Rusdianto Andarina; Wibowo, Darrell Rajendra; Syam, Andi Muhammad Agung Ramadhani
Sebatik Vol. 29 No. 1 (2025): June 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i1.2558

Abstract

Sparrows are one of the most costly pests for farmers, as they can reduce rice yields by 50-60%. Traditional control methods, such as the use of scarecrows, windmills, and pesticides, are often ineffective or cause negative impacts on the environment, such as damage to ecosystems and human health. To overcome this problem, YOLOv8-based object detection technology offers a modern solution to automatically detect bird pests with a high level of accuracy. This research aims to implement the YOLOv8 model on power-efficient embedded devices, such as Raspberry Pi 4 and Google Coral USB TPU Accelerator, to support real-time sparrow detection at an affordable cost. The research was conducted through three main stages, namely collecting bird image datasets to support model training, training the YOLOv8n model to produce reliable bird pest detection, and implementing the model on embedded devices with and without TPU accelerators to evaluate detection performance. The evaluation results show that the YOLOv8 model has high performance with precision 0.91, recall 0.86, mAP50 0.92, and mAP50-95 0.59 after being trained for 300 epochs. Implementation on Raspberry Pi 4 without accelerator only resulted in an inference speed of 0.39 Frame Per Second, while with Google Coral USB TPU, the speed increased significantly to 7 Frame Per Second.  This proves that TPU accelerators are highly effective in supporting real-time object detection. This technology is expected to help farmers protect crops efficiently, reduce losses due to pests, support sustainable agricultural productivity, and contribute to the overall improvement of food security.
Co-Authors Azhar, Nur Fajri Hertadi, Christopher Davito Prabandewa Muqimuddin Muqimuddin Paninggalih, Ramadhan Pratama , Boby Mugi Rahmadani, Irwan Shafardan, Wahyu Achmad Subekti, Putra Cendikia Syakbani, Ahmad Rusdianto Andarina Syam, Andi Muhammad Agung Ramadhani Takaendengan, Mahardika Inra Utomo, Muchammad Chandra Cahyo Wibowo, Darrell Rajendra