The Development of Javanese Language Teaching Materials Through Introduction of Java Scripts Using Artificial Neural Network

Wardoyo, Siswo; Kuntari, W; Pramudyo, Anggoro Suryo; Suhendar, Suhendar; Hidayat, Syarif

doi:10.12928/telkomnika.v16i4.8465

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The Carakan Script consist of 20 main script has no sandhangan or clothes, therefore it needs another script to make vowel sounds. The sandhangan script is used to fill the vowel while murdha scripts is to write something which are considered respected [12].…”

Section: A Design Processmentioning

confidence: 99%

Cross-Cultural Concepts in Cultural Product Design

Ardhianto¹,

Hsieh²,

Mahanaim³

et al. 2021

Proceedings of the 3rd International Conference on Arts and Design Education (ICADE 2020)

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Cultural product design with a cross-cultural approach is currently in high demand by the younger generation. This positive thing increases the respect for local culture. However, the lack of industry player's knowledge of cross-culture principles makes less cultural ideology conveyed. This study aims to determine the process of cross-culture in design using a case study of casual sneaker shoes with ancient Javanese script culture (Hanacaraka) in the design. This research uses a qualitative case study by studying the form, function, and symbolic meaning of the culture in the shoe product design process. The results of this study found that the use of culture in popular products needs to pay attention to the symbolic meaning that represents the local culture. The use of culture in the design of cultural products gives a different look to provide its own identity that results in strengthening branding and strengthening the economy for local brands. Difficult cultural preservation can represent cultural product design with the concept of crossculture. The implication of this research is to provide steps in the process of cross-cultural methods in industries, especially local brands that carry the cultural locality of their products. Besides, this study also has implications on the cross-culture method for the preservation of Hanacaraka culture.

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Section: A Design Processmentioning

confidence: 99%

Cross-Cultural Concepts in Cultural Product Design

Ardhianto¹,

Hsieh²,

Mahanaim³

et al. 2021

Proceedings of the 3rd International Conference on Arts and Design Education (ICADE 2020)

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“…Research on Javanese script recognition in digital images has been carried out. Some of the latest research are [1], [8]- [13], [15], [16], [23]. From some of these studies, several studies use machine learning (ML) such as [1], [8], [10]- [12], [15], [16], [23], and others use deep learning (DL) [9], [13], [22], [24].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the latest research are [1], [8]- [13], [15], [16], [23]. From some of these studies, several studies use machine learning (ML) such as [1], [8], [10]- [12], [15], [16], [23], and others use deep learning (DL) [9], [13], [22], [24]. ML has relatively lighter computations than DL, but the accuracy performance of DL methods is generally better than ML methods.…”

Section: Introductionmentioning

confidence: 99%

Improved Javanese script recognition using custom model of convolution neural network

Susanto,

Mulyono,

Sari

et al. 2023

IJECE

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<div align="center"><span lang="EN-US">Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computational costs. The main features of handwritten objects are textures, edges, lines, and shapes, so convolution layers are not designed in large numbers. This research maximizes optimization of other layers such as pooling, activation function, fully connected layer, optimizer, and parameter settings such as dropout and learning rate. There are eleven main layers used in the proposed custom convolutional neural network (CNN) model, namely four convolution layers+activation function, four pooling layers, two fully connected layers, and a softmax classifier. Based on the test results on the Javanese script handwritten image dataset with 120 classes consisting of 20 basic character classes and 100 compound character classes, the resulting accuracy is 97.29%.</span></div>

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“…need feature extraction, such as horizontal and vertical profile image [16], [19], zoning method [11], [18], [20], histogram of oriented gradient (HOG) feature [18], mesh and local line direction (LLD) [17], and fast fourier transform (FFT) [19]. Artificial neural network (ANN) is one powerful machine learning and helpful for classification, clustering, pattern recognition, and prediction [17].…”

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confidence: 99%

Image preprocessing analysis in handwritten Javanese character recognition

2023

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The handwriting produced by each person is unique, so each person has a different stroke, even though they write the same letter. Handwritten Javanese is an exciting topic to study, in addition to scientific purposes and preserving Indonesian culture. The Javanese character image dataset is aksara Jawa: aksara Jawa custom dataset from the Kaggle database consists of 2,154 train data and 480 evaluation data. This research proposed to analyze the impact of some preprocessing methods in recognizing handwritten Javanese characters. The preprocessing methods are dilation, skeletonization, and noise reduction. The first process is segmentation for region of interest (ROI) extraction, then various preprocessing is used, and finally, the recognition step neural network (NN) to measure the effectiveness of the preprocessing method. The experiment shows that all preprocessing methods (dilation, skeletonization, and noise reduction) give excellent results, especially on the black background color, reaching 98% accuracy. Other experimental findings show that in any preprocessing combination, the black background accuracy is better than the white one.

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The Development of Javanese Language Teaching Materials Through Introduction of Java Scripts Using Artificial Neural Network

Cited by 4 publications

References 3 publications

Cross-Cultural Concepts in Cultural Product Design

Cross-Cultural Concepts in Cultural Product Design

Improved Javanese script recognition using custom model of convolution neural network

Image preprocessing analysis in handwritten Javanese character recognition

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