Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik

Bariyah, Taufiqotul; Rasyidi, Mohammad Arif; Ngatini, Ngatini

doi:10.33633/tc.v20i1.4224

Cited by 16 publications

(19 citation statements)

References 13 publications

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“…Training loss is commonly used to maximize the performance of machine learning algorithms and is based on how well the model is created in the training and model validation stages. Training loss can indicate how bad or how good a model is after iterations of model optimization [17]. The training loss values generated during the learning process for the number of epochs of 200, 300, and 400 can be seen in Table 2.…”

Section: Training Resultsmentioning

confidence: 99%

Identification of yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) using deep learning

Ayuningtias

Jaya

Iqbal

2021

IOP Conf. Ser.: Earth Environ. Sci.

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Yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) have important economic values for the capture fisheries in Indonesia. Activities of identifying these fish and other types of tuna have been done manually, which can lead to errors and ultimately affect statistics, stock estimates, or traceability. The aim of this research is to use deep learning methods in identifying three species of tuna, specifically yellowfin tuna, mackerel tuna, and skipjack tuna. YOLO’s newest model, YOLOv5, was used to identify the fish. The number of epochs that produces the optimum accuracy value for use in the YOLOv5 model is 400. The values for training loss, accuracy, precision, recall and F1-Score when the model is learning with a total of 400 epochs are 0.000253, 95%, 98.1%, 93.9%, and 96%. Based on these results, the three species of tuna can be identified with high accuracy.

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Section: Training Resultsmentioning

confidence: 99%

Identification of yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) using deep learning

Ayuningtias

Jaya

Iqbal

2021

IOP Conf. Ser.: Earth Environ. Sci.

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“…Penelitian lain dilakukan oleh Bariyah et. al yang menggunakan CNN untuk klasifikasi batik multilabel dalam satu citra [15] yang melakukan klasifikasi pada motif batik Banji, Buketan, Ceplok, Encim, Kawung, Lung-lungan, Madura, Mega Mendung, Parang Rusak, Parang, Sekar Jagad, Truntum, Kipas, Semen, dan Fauna. Oleh karena itu, pada penelitian ini dilakukan pengembangan sistem klasifikasi yang sudah dikembangkan dengan menambahkan dataset yang digunakan serta motif batik Banyuwangi yang diklasifikasikan dengan menggunakan metode CNN.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Motif Batik Banyuwangi Menggunakan Convolutional Neural Network

Hakim

Rahmanto²,

Kristanto³

et al. 2023

JTI

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Batik cloth is one of Indonesia's most valuable cultural heritages and has been recognized by UNESCO as one of the world heritages. Many Indonesian people do not know the batik motifs of each region. Banyuwangi itself has more than 10 batik motifs, among the most famous Banyuwangi batik motifs is the Gajah Oling motif. The Banyuwangi batik motif classification system is a system built using the Python library with Python programming language. This system can recognize 7 types of Banyuwangi batik motifs, including Gajah Oling, Gedegan, Coffee Pecah, Moto Pitik, Kutah Rice, Paras Earthy and Sisikan. This system uses the convolutional neural network method and for evaluation, the confusion matrix method is used to measure the accuracy value. The research uses a CNN model with an architecture named MyCustomModel. The data used in this study were 120 images for each batik motif and the prediction results get an accuracy value of 63%.

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“…mampu mencapai akurasi yang lebih tinggi dengan persentase optimal sebesar 98,96% dengan jumlah dataset 500 citra dan menggunakan 8 data augmentasi. Selain itu, Bariyah dkk juga mengimplementasikan algoritma Deep Learning yang spesifik pada penggunaan algoritma Convolutional Neural Network [4]. Mereka melakukan klasifikasi multi-label pada 15 motif batik diantaranya motif Banji, Semen, Kawung, Ceplok, Parang, Buketan, Encim, Sekar Jagad, Truntum, Lung-lungan, Madura, Parang Rusak, Mega Mendung, Kipas dan Fauna.…”

Section: Pendahuluanunclassified

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

Hakim

Kristanto

Yusuf

et al. 2022

JTI

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Batik is one of Indonesia's cultural heritages that is recognized around the world and has existed since the colonial era. Indonesia has a variety of different batik pattern in every Indonesia's region. It causes many ordinary people and tourists to become harder to identify and recognize the existing patterns. Banyuwangi regency itself has more than 10 batik patterns, including the Gajah Oling pattern which in the oldest batik pattern. For preserving the culture and supporting the growing tourism aspect in Banyuwangi, this study developed a system for recognizing Banyuwangi batik patterns based on digital image processing. This system is built using python language and is able to recognize three classes of Banyuwangi batik patterns, such as Gajah Oling, Kopi Pecah and other Banyuwangi batik patterns. This system proposes Gray Level Co-occurrence Matrix (GLCM) as feature extraction method and k-Nearest Neighbors (kNN) as classification method. Based on the experiments that have been carried out, the optimal accuracy is 87,5% with the K parameter of kNN is 9.

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Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik

Cited by 16 publications

References 13 publications

Identification of yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) using deep learning

Identification of yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) using deep learning

Klasifikasi Citra Motif Batik Banyuwangi Menggunakan Convolutional Neural Network

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

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