Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network

Fayyadhila, Rizka; Junaidi, Apri; Prasetyo, Novian Adi

doi:10.20895/dinda.v1i2.366

Cited by 6 publications

(7 citation statements)

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“…In machine learning, there are techniques for using extraction features from training data and specialized learning algorithms for online community network classification and data representation. However, this method still has some drawbacks both in terms of speed and accuracy [11]. The application of deep neural networks can be seen in the existing machine learning algorithms so that now computers can learn with speed, accuracy, and on a large scale [12].…”

Section: Research Methods 21 Deep Learningmentioning

confidence: 99%

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Pratama,

Haiyudi

2024

ITJRD

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Analysis of social networks or online communities can be very difficult when working on large networks, as many measurements require expensive hardware. For example, identifying the community structure of a network is a very computationally expensive task. Embedded graph is a way to represent graphs with vectors, so that further analysis becomes easier. The purpose of this research is to analyze the knowledge graph from the wikipedia article data. This research aims to implement web scraping techniques on the wikipedia article search engine and display similar wikipedia pages and analyze them using a predetermined deep learning algorithm. Data collection in this research used scraping techniques to retrieve data from the unstructured wikipedia website and then processed it into structured data. The method used in this research is a standard cross-industry process for data mining by performing phases of data collection, data processing, proposed algorithms, testing and evaluation. The algorithm applied is deepwalk, kmeans, girvan newman. By doing this research, it is expected to provide knowledge about the deep learning approach for data representation of the wikipedia pages knowledge graph and can help users find similar wikipedia pages and enrich literacy on knowledge graph analysis.

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Section: Research Methods 21 Deep Learningmentioning

confidence: 99%

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Pratama,

Haiyudi

2024

ITJRD

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“…The feature extraction layer converts the image into a number which is then calculated for the matrix classification layer. At this stage, the results of feature learning will be used for the classification process based on predefined subclasses [6].…”

Section: Methodsmentioning

confidence: 99%

Identification of fish meal adulterated with rice bran by using an image analysis method

Agustin,

Albarki,

Martin

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Adulterated feed ingredients are one of the main problems in ensuring quality. This study aims to identify the level of fish meal forgery using rice bran based on image analysis with a convolutional neural network algorithm. The number of datasets in this study was 3200 images divided into 2400 images for training data and 800 images for test data. The forgery treatment consists of P0=100% fish meal, P1= 90% fish meal+10% rice bran, P2=80% fish meal+20% rice bran, P3= 70% fish meal+30% rice bran and P4=100% rice bran. The CNN algorithm processes the image through the input layer, feature extraction layer, and fully connected. The results of this study obtained a training data accuracy value of 100% and a validation data accuracy value of 100%. The results of 99% accuracy using 20 epochs were obtained in testing through the confusion matrix table. This study concluded that testing the counterfeiting of fish meal and rice bran using CNN can provide quite good and optimal results.

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“…In addition, (Saputra et al, 2022) ) also successfully used transfer learning with the MobileNetV2 model to classify traditional weapons in Central Java, achieving an accuracy of 98.64%. And in previous research, (Firmansyah, 2021) used CNN in transfer learning to classify flowers with an accuracy rate of 64%. All these research results show significant developments in the application of transfer learning in nut type classification and related fields.…”

Section: Literatur Reviewmentioning

confidence: 99%

Implementation of Transfer Learning in CNN for Classification of Nut Type

Fawwaz,

Sagala,

Sijabat

et al. 2023

SinkrOn

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Nut has a high nutritional value and is widely used as an ingredient in cooking and snacks. Nut is included in the group of grains and has many types. Each type of nut has different nutritional content. Some types of nuts can also cause allergies or negative reactions in certain people, so it is important to identify the type of nut to be consumed. There are many types of nut that are different from each other, but some of them are similar. This makes it difficult to distinguish between the types of nuts, so there is a need for technology that can accurately identify nut types. Transfer Learning method is used to utilize trained models and applied to nut type classification. The two CNN models used are Inception V3 and Xception. The dataset consists of 11 types of nuts consisting of 1,320 data. The data is divided into 60% for training data and 40% for validation data. Preprocessing is done to ensure the image size is consistent and clarify the focus on the data image to be tested. The training results show that the Xception model is superior to Inception V3, with an accuracy of 86.36% on the validation data, while Inception V3 only reached 74.05%. Xception is able to predict nut types more precisely.

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Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network

Cited by 6 publications

References 0 publications

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm

Identification of fish meal adulterated with rice bran by using an image analysis method

Implementation of Transfer Learning in CNN for Classification of Nut Type

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