Image Classification of Pandawa Figures Using Convolutional Neural Network on Raspberry Pi 4

Wisnudhanti, Kartika; Candra, Feri

doi:10.1088/1742-6596/1655/1/012103

Cited by 4 publications

(5 citation statements)

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“…The data-hungry issue on deep learning could be handled by using transfer learning schema [23], [24] even though on small dataset [25], [26], [27], [28]. Our contribution is that; we can showed better and efficient model and more labels classification with limited number of data rather than previous study [20], [21]. From this research, we are able to provide insight and comparison between models in terms of performance and efficiency on more label classification when facing data-hungry problems.…”

Section: Discussionmentioning

confidence: 95%

“…Several studies on classification in wayang aspect have been conducted like in their gamelan music pattern [18] and emotion recognition [19]. The similar studies on the classification of wayang characters have been performed using Convolution Neural Network [20] and MLP with GLCP Feature Extraction [21]. However, those studies contain limitations that only recognize five characters with average accuracy and require large data for each class.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification

Mustafid

Pamuji

Helmiyah

2020

IJID

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Deep Learning is an essential technique in the classification problem in machine learning based on artificial neural networks. The general issue in deep learning is data-hungry, which require a plethora of data to train some model. Wayang is a shadow puppet art theater from Indonesia, especially in the Javanese culture. It has several indistinguishable characters. In this paper, We tried proposing some steps and techniques on how to classify the characters and handle the issue on a small wayang dataset by using model selection, transfer learning, and fine-tuning to obtain efficient and precise accuracy on our classification problem. The research used 50 images for each class and a total of 24 wayang characters classes. We collected and implemented various architectures from the initial version of deep learning to the latest proposed model and their state-of-art. The transfer learning and fine-tuning method showed a significant increase in accuracy, validation accuracy. By using Transfer Learning, it was possible to design the deep learning model with good classifiers within a short number of times on a small dataset. It performed 100% on their training on both EfficientNetB0 and MobileNetV3-small. On validation accuracy, gave 98.33% and 98.75%, respectively.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification

Mustafid

Pamuji

Helmiyah

2020

IJID

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“…Pada penelitian yang dilakukan oleh Wisnudhanti & Candra pada tahun 2020 dengan judul: "Metode Convolutional Neural Network Dalam Klasifikasi Citra Tiga Tokoh Wayang Pandawa", diperoleh akurasi sebesar 96,67% menggunakan arsitektur yang telah dirancang sedemikian rupa [18].…”

Section: Tinjauan Pustakaunclassified

Convolutional Neural Network (Cnn) Untuk Menentukan Gagrak Wayang Kulit

2022

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Indonesia adalah negara yang kaya akan keragaman budaya, salah satu dari budaya Indonesia adalah Wayang Kulit. Wayang kulit di Indonesia memiliki beragam gagrak, mulai dari Cirebon, Solo, Jawa-Timuran, Yogyakarta dan lain sebagainya. Keberagaman dari gagrak wayang kulit membuat generasi muda kesulitan untuk mengetahui gagrak dari wayang kulit. Dari permasalahan tersebut, peneliti akan mengembangkan sebuah machine learning dengan menggunakan Convolutional Neural Network (CNN) untuk menentukan gagrak wayang kulit. Tujuan penelitian ini adalah menghasilkan machine learning yang bisa menentukan gagrak dari wayang kulit. Data yang digunakan pada penelitian ini terdiri dari 280 gambar wayang kulit yang diunduh satu persatu di platform Facebook dan Google penelusuran gambar, warna latar belakang gambar tersebut diubah menjadi putih dan diubah ukuran menjadi 640*480 pixel. Data tersebut disebarkan melalui google form dan dilakukan validasi menggunakan Inter-annotator Agreement sehingga dapat digunakan pada proses pelatihan dengan metode CNN di Google Colab. Setelah itu dilakukan pengujian untuk menentukan gagrak wayang kulit menggunakan 3 arsitektur CNN yaang sudah dibuktikan pada penelitian-penelitian sebelumnya. Hasil dari penelitian ini menunjukan bahwa arsitektur yang digunakan oleh Sudiatmika & Dewi merupakan model klasifikasi terbaik dari ketiga arsitektur, arsitektur tersebut mendapatkan akurasi sebesar 92,27%, presisi sebesar 92,22%, recall sebesar 96,85% dan f-measure sebesar 91,93%.

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“…The model exposure assessment phase is the result of the training phase using the new data as test data. The result of this phase is the level of accuracy/performance of the model when predicting unknown class data, especially test data (Antoko, et al, 2021;Hanafi et.al, 2019;Kartika Wisnudhanti, 2020).…”

Section: Introductionmentioning

confidence: 99%

Classification of Edelweiss Flowers Using Data Augmentation and Linear Discriminant Analysis Methods

Malau¹,

Mulyana²

2022

JAETS

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Edelweiss is a plant that grows at a height, and is known as a perennial flower because it has beautiful petals and does not wilt easily. Although edelweiss in Indonesia is still in the same family as Leontopodium Alpinum, it turns out that the type of edelweiss found in the mountains of Indonesia is different from edelweiss found abroad. Therefore, in this study, an image processing system was developed that can classify the types of edelweiss flowers based on their image using Linear Discriminant Analysis to classify data into several classes based on the boundary line (straight line) obtained from linear equations. In this study, the types of edelweiss flowers used in this study were Anaphalis Javanica and Leontopodium Alpinum, the two types of edelweiss flowers were distinguished based on their color characteristics using hue and saturation values. The images used are 1500 images for training data and 450 test data images with a training and test data ratio of 70:30, so that the accuracy produced in the testing process is 99.77% in the Linear Discriminant Analysis method.

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Image Classification of Pandawa Figures Using Convolutional Neural Network on Raspberry Pi 4

Cited by 4 publications

References 1 publication

A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification

A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification

Convolutional Neural Network (Cnn) Untuk Menentukan Gagrak Wayang Kulit

Classification of Edelweiss Flowers Using Data Augmentation and Linear Discriminant Analysis Methods

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