Consumable Fish Classification Using k-Nearest Neighbor

Winiarti, Sri; Indikawati, Fitri Indra; Oktaviana, A.; Yuliansyah, Herman

doi:10.1088/1757-899x/821/1/012039

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…The photographs of the prawns for the research and databases obtained using various brands and models of mobile devices were used. The findings in the RGB and HSV color spaces enable assessing the suggested model's effectiveness [5].…”

Section: Fig 1 Types Of Consumption Of Shrimp In Coastal Watersmentioning

confidence: 99%

Classification of coastal shrimp species using deep learning InceptionResNetV2 with data augmentation

Budi Dwi Satoto,

Bain Khusnul Khotimah,

Mohammad Yusuf

et al. 2023

Technium

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Coastal areas that are rich in shrimp natural resources usually have a strong fishing industry. Shrimp consumption can contribute to the local economy. Several types of shrimp have higher utilization potential in the fishing industry and human consumption. Classification helps sort out shrimp species in coastal waters. Deep learning technology helps identify and separate shrimp species through deep learning with the Convolutional neural network. The seven classes observed were Vaname Shrimp, Tiger Shrimp, Jerbung Shrimp, Giant Prawns, Red Shrimp, Ronggeng Shrimp, and Prawns. The data used is 50 per class, so 350 images are used. Due to dataset limitations, the proposed contribution is an initial Resnet architecture with augmentation. Resnet helps perform in-depth training to save training computation time, and improvements add variety to limited amounts of data. The result is that the average accuracy of the model is 99.4%, with an average training computation time of 7 and a half minutes. The MSE misclassification value was 0.0143, RMSE was 0.1195, and MAE was 0.0086. Testing tests and testing data on the model only takes 1-2 seconds. Prediction accuracy ranges from 90.01%-99.8%.

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Section: Fig 1 Types Of Consumption Of Shrimp In Coastal Watersmentioning

confidence: 99%

Classification of coastal shrimp species using deep learning InceptionResNetV2 with data augmentation

Budi Dwi Satoto,

Bain Khusnul Khotimah,

Mohammad Yusuf

et al. 2023

Technium

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“…K-Title of manuscript is short and clear, implies research results (First Author) Nearest Neighbor menunjukan hasi yang lebih baik. Winiarti [6] melakukan penelitian tentang klasifikasi kesegaran ikan nila dan tenggiri berdasarkan daging ikan yang layak dikonsumsi. Penelitian ini menggunakan metode ciri warna dan ciri tekstur untuk proses ekstraksi ciri, sedangkan klasifikasi menggunakan metode KNN.…”

Section: Pendahuluanunclassified

Identifikasi Kesegaran Ikan Berdasarkan Citra Insang dengan Metode Convolution Neural Network

Sholihin¹

2021

JATISI

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Penelitian ini bertujuan untuk membuat sistem identifikasi kesegaran ikan yang didasarkan pada citra insang ikan. Dengan adanya sistem ini diharapkan masyaratak akan semakin mudah dalam menentukan ikan segar. Metode yang digunakan pada penelitian ini adalah CNN. Data yang digunakan pada penelitian ini berjumlah 150 data citra insang ikan yang dikategorikan kedalam tigas kelas yaitu kelas ikan segar, tidak segar, dan busuk. Penelitian iini memberikan hasil akurasi 100% untuk proses training dan 97,7% untuk proses testing.

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“…Image classification is usually done using 2 image processing techniques, namely unsupervised learning and supervised learning techniques. In supervised learning techniques, several algorithms that are usually and commonly used by researchers are SVM (Support Vector Machine) [8]- [10], Neural Network (NN) [11]- [17], K-Nearest Neighbor (KNN) [18]- [20], and others. The use of mind processing using supervised learning algorithms is usually used when researchers already have clear training data and variables to classify data.…”

Section: Introductionmentioning

confidence: 99%

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

Sari,

Shelomita

et al. 2023

JAIS

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Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.

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Consumable Fish Classification Using k-Nearest Neighbor

Cited by 9 publications

References 15 publications

Classification of coastal shrimp species using deep learning InceptionResNetV2 with data augmentation

Classification of coastal shrimp species using deep learning InceptionResNetV2 with data augmentation

Identifikasi Kesegaran Ikan Berdasarkan Citra Insang dengan Metode Convolution Neural Network

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

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