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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JOINTER : Journal of Informatics Engineering
Resti Ajeng Sutiani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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PENERAPAN ALGORITMA YOLO UNTUK MENDETEKSI KUALITAS TELUR AYAM BERDASARKAN WARNA CANGKANG Sri Ayu Ningsih; Resti Ajeng Sutiani; Ni Made Sri Ulandari; Rizal Adi Saputra
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3062

Abstract

In the poultry industry, chicken egg quality is a crucial factor influencing the price and market appeal of the product. Manual assessment of egg quality based on shell color requires significant time and labor and is prone to human error. Therefore, the implementation of automation technology through artificial intelligence (AI) is necessary to enhance the efficiency and accuracy of this process. The YOLO (You Only Look Once) algorithm is a fast and accurate object detection method that can be applied to classify chicken eggs based on shell color. This research aims to develop an automatic detection system using YOLO to identify and categorize the quality of chicken eggs based on shell color. Images of chicken eggs were collected and annotated to train the YOLO model. After training, the model was tested on a new dataset to evaluate its detection and classification performance. The results of the study indicate that the YOLO algorithm can detect and classify chicken eggs with high accuracy, reducing the need for manual labor and speeding up the quality assessment process. The implementation of this system is expected to improve operational efficiency in the poultry industry, ensure consistent product quality, and provide an innovative solution to the challenges in chicken egg quality assessment.
Klasifikasi Kesegaran Ikan Menggunakan Citra Mata dengan Convolutional Neural Network Arsitektur VGG-16 Ni Made Sri Ulandari; Resti Ajeng Sutiani; Rizal Adi Saputra
JOINTER : Journal of Informatics Engineering Vol 5 No 02 (2024): JOINTER : Journal of Informatics Engineering
Publisher : Program Studi Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/jointer.v5i02.350

Abstract

Sea fish are the most widely consumed type of fish by households in Indonesia, serving as an important source of protein for the body. According to Matondang's (2022) study titled "Comparison of Protein Content in Freshwater Fish and Sea Fish," the protein content in sea fish is higher than in freshwater fish, making high-quality fish highly beneficial for the body. The fishing industry plays a crucial role in food supply, especially in maritime countries like Indonesia. The freshness of sea fish, as the main protein source for many households, significantly determines its quality and safety for consumption. Freshness affects nutritional value, taste, and prevents health risks from consuming stale fish. This study employs the Convolutional Neural Network (CNN) method with the VGG-16 architecture to classify fish freshness based on eye images. The dataset used consists of 1,903 fish eye images, augmented to 4,560 images. Classification results indicate that the VGG-16 model can distinguish between fresh and stale fish eyes with an accuracy of 85.26%. This research is expected to assist the fishing industry in monitoring fish quality more effectively and efficiently, as well as enhancing the safety of fish consumption for the community.
PENERAPAN ALGORITMA YOLO UNTUK MENDETEKSI KUALITAS TELUR AYAM BERDASARKAN WARNA CANGKANG Sri Ayu Ningsih; Resti Ajeng Sutiani; Ni Made Sri Ulandari; Rizal Adi Saputra
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3062

Abstract

In the poultry industry, chicken egg quality is a crucial factor influencing the price and market appeal of the product. Manual assessment of egg quality based on shell color requires significant time and labor and is prone to human error. Therefore, the implementation of automation technology through artificial intelligence (AI) is necessary to enhance the efficiency and accuracy of this process. The YOLO (You Only Look Once) algorithm is a fast and accurate object detection method that can be applied to classify chicken eggs based on shell color. This research aims to develop an automatic detection system using YOLO to identify and categorize the quality of chicken eggs based on shell color. Images of chicken eggs were collected and annotated to train the YOLO model. After training, the model was tested on a new dataset to evaluate its detection and classification performance. The results of the study indicate that the YOLO algorithm can detect and classify chicken eggs with high accuracy, reducing the need for manual labor and speeding up the quality assessment process. The implementation of this system is expected to improve operational efficiency in the poultry industry, ensure consistent product quality, and provide an innovative solution to the challenges in chicken egg quality assessment.
Co-Authors Ni Made Sri Ulandari Rizal Adi Saputra Sri Ayu Ningsih