Wayang Image Classification Using MLP Method and GLCM Feature Extraction

Santoso, M. Hamdani; Larasati, Diah Ayu; Muhathir, Muhathir

doi:10.30596/jcositte.v1i2.5131

Cited by 6 publications

(7 citation statements)

References 20 publications

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“…Based on these categories, the developed model is included in the good category. If it is related to previous research on the classification of the character image of Wayang Kulit characters, the results of this accuracy are higher than studies using the KNN method [8] which produces an accuracy of 77.5%, and research using the Multi-Layer Perceptron (MLP) method [12] which produces an accuracy of 73.4%. However, this research is not higher than research using the Support Vector Machine (SVM) approach [10] which produces an accuracy of 83.2%.…”

Section: Figure 8 Confusion Matrix Resultsmentioning

confidence: 79%

“…The SVM algorithm, which divides the best hyperplane into two classes, is effective in simple class situations, but less so in complex class cases [11]. Another study is the classification of Wayang imagery using the Multi-Layer Perceptron (MLP) method [12]. This study uses textural features in the feature extraction procedure, just like earlier studies.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Character Types of Wayang Kulit Using Extreme Learning Machine Algorithm

Fatmayati¹,

Nugraheni

Nuraini

et al. 2023

bits

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Wayang Kulit, which is an original Indonesian culture, is conditioned by the meaning of life in every performance. However, Wayang Kulit is currently less popular among young people due to a lack of understanding of the art of Wayang Kulit performance. To be able to provide knowledge to the younger generation about Wayang Kulit, one of which is by introducing the characters that exist in Wayang Kulit performances. This study aims to build an image classification system for Wayang Kulit characters by applying the neural network method using Extreme Learning Machine (ELM) and morphological feature extraction. Morphological feature extraction provides information about the shape characteristics of objects present in the image which are then used for input in the classification process. The Extreme Learning Machine (ELM) method may arbitrarily establish the weight value between the input neurons and the hidden layer during the classification step, resulting in a quicker learning pattern. Based on the test results using the confusion matrix, the accuracy value is calculated to get a value of 81%.

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Section: Figure 8 Confusion Matrix Resultsmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Classification of Character Types of Wayang Kulit Using Extreme Learning Machine Algorithm

Fatmayati¹,

Nugraheni

Nuraini

et al. 2023

bits

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“…The AI models developed using the deep learning approach with LeNet (Scenarios 1, 2, 3, and 4) exhibited better performance compared to AI models using other methods such as KNN + GLCM [5] and MLP + GLCM [36]. The LeNetbased AI models achieved higher accuracy and more balanced precision, recall, and F1-Score, as shown in Table VII.…”

Section: F Discussionmentioning

confidence: 90%

Preserving Cultural Heritage Through AI: Developing LeNet Architecture for Wayang Image Classification

Khairina,

Barus

et al. 2023

IJACSA

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Wayang, an ancient cultural tradition in Java, has been an integral part of Indonesian culture for 1500 years. Rooted in Hindu cultural influences, wayang has evolved into a highly esteemed and beloved performance art. In the form of wayang kulit, this tradition conveys profound philosophical messages and implicit meanings that resonate with Javanese society. This research aims to develop an artificial intelligence (AI) model using deep learning with the LeNet architecture to accurately classify wayang images. The model was tested with 2515 Punakawan wayang images, showing excellent performance with an accuracy of 80% to 85%. Although the model successfully recognizes and distinguishes wayang classes, it faces some challenges in classifying specific classes, particularly in scenarios 2 and 4. Nevertheless, this research has a positive impact on cultural preservation, as the developed AI model can be used for automatic wayang image recognition. These implications open opportunities to better understand and preserve this rich cultural heritage through AI technology. With further improvements, this model has the potential to become a valuable tool in the efforts to preserve and introduce wayang culture to future generations.

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“…The multilayer perceptron is chosen for its ease of implementation and good results in various cases. This capability has been demonstrated and proven in several studies [34] [35][36]. This test uses 5.2 hidden layers and 300 max_iter to achieve good accuracy values.…”

Section: Pseudocode 1: K-means Clusteringmentioning

confidence: 92%

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

Walid,

Sahbaniya,

Hozairi

et al. 2023

Knowl. Eng. Data Sci.

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The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited availability during the classification process, which involves dividing the transaction level attributes into three distinct groups: (0) low transactions, (1) medium transactions, and (2) high transactions. The clustering process encompasses three distinct features: (1) income, (2) spending, and (3) profit. These three traits will be utilized as input data throughout the categorization procedure. The classification procedure employing the Multilayer Perceptron technique involved processing a dataset including 1383 data points. The training data constituted 80% of the dataset, while the remaining 20% was allocated for testing. In order to evaluate the efficacy of the constructed model, the training error was assessed using K-Fold cross-validation, yielding an average accuracy score of 0.92. In the present study, the categorization technique yielded an accuracy of 0.96. This model aims to classify scenarios when the dataset lacks prior target data.

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Wayang Image Classification Using MLP Method and GLCM Feature Extraction

Cited by 6 publications

References 20 publications

Classification of Character Types of Wayang Kulit Using Extreme Learning Machine Algorithm

Classification of Character Types of Wayang Kulit Using Extreme Learning Machine Algorithm

Preserving Cultural Heritage Through AI: Developing LeNet Architecture for Wayang Image Classification

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

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