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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JAREE (Journal on Advanced Research in Electrical Engineering) Teknika Jurnal Informatika: Jurnal Pengembangan IT
Purnawan, I Ketut Adi
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Coloring Pekalongan Batik Using a Madura Dataset: A Comparative Study of GAN and Caffe-Based CNN Models Wahyudi, Muhamad Machrus Ali; Kurniawati, Arik; Damayanti, Fitri; Purnawan, I Ketut Adi
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1071

Abstract

Madura Batik, as one of Indonesia's valuable cultural heritages, is known for its unique characteristics involving the use of bright colors such as red, yellow, and green, as well as traditional motifs that often feature elements of nature like flowers, leaves, and animals. Each motif in Madura Batik reflects the rich philosophy, values, and stories of Madura culture. This batik is also famous for its production process, which is largely carried out manually using traditional dyeing techniques. However, with the advancement of technology, there is a growing need to integrate technological innovations into the batik dyeing process without losing its traditional essence. This research combines Generative Adversarial Networks (GAN) models and compares them with Caffe-based pretrained Convolutional Neural Networks (CNN) to create new color variations in Pekalongan batik images. The input for the models is grayscale batik images, which are then processed to generate colorful outputs. The dataset used consists of 519 Madura batik images, with a distribution of 80% for training, 20% for validation, and 10 images for testing. The preprocessing process includes resizing, normalization, and batching to accelerate model convergence. Performance evaluation is conducted using FID, MSE, PSNR, and SSIM metrics. The results show that the GAN model with 100 epochs produces better image quality compared to the Caffe-based pretrained CNN model, particularly in terms of visual and structural similarity. In conclusion, the GAN method offers great potential for innovation in batik coloring without compromising its traditional motifs.
Optimizing Diabetic Neuropathy Severity Classification Using Electromyography Signals Through Synthetic Oversampling Techniques Purnawan, I Ketut Adi; Wibawa, Adhi Dharma; Kurniawati, Arik; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.85675

Abstract

Electromyography signals are electrical signals generated by muscle activity and are very useful for analyzing the health conditions of muscles and nerves. Data imbalance is a prevalent issue in EMG signal data, especially when addressing patients with varied health conditions and restricted data availability. A major difficulty for machine learning models is class imbalance in datasets, which frequently leads to biased predictions favoring the dominant class and neglecting the minority classes. The data augmentation method employs the Synthetic Minority Over Sampling Technique (SMOTE) and Random Over Sampling (ROS) to address data imbalances and enhance the performance of classification models for underrepresented classes. This study employs an oversampling technique to enhance the efficacy of the XG Boost model. SMOTE exhibits better efficacy relative to competing methods; the application of appropriate oversampling techniques allows models to integrate patterns from both majority and often neglected minority data.
The Comparison of GAN and CNN Models in the Innovation of Coloring Madura and Bali Batik Permana, Yohan; Kurniawati, Arik; Damayanti, Fitri; Purnawan, I Ketut Adi
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 9, No 2 (2025): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v9i2.467

Abstract

This study aims to innovate automatic coloring of batik patterns using deep learning models. Specifically, it compares the performance of Generative Adversarial Network (GAN) with pretrained Caffe-based Convolutional Neural Networks (CNN) in coloring images of Madura and Bali batik. The dataset consists of 388 Madura batik images for training, 97 for validation, and 20 distinct images of both Bali and Madura batik for testing. This dataset was obtained through web scraping from batik posts on social media platforms like Instagram, Bing Image Search using specific keywords, and Kaggle, followed by a manual combination and cleaning process. The GAN model was trained with varying epochs (40, 80, 150), while the CNN utilized pretrained Caffe weights. Evaluation was conducted using Peak Signal-to-Noise Ratio (PSNR), Fréchet Inception Distance (FID), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). The results indicate that the GAN model with 150 epochs outperformed the CNN, achieving a PSNR of 29.702, an FID of 84.016, an MSE of 511.8812, and an SSIM of 0.9925, demonstrating superior color creation and artistic detail in batik. Conversely, the CNN model exhibited lower performance, with a PSNR of 28.218, an FID of 200.271, and an SSIM of 0.7925, indicating its limitations in preserving the intricate patterns and colors of batik. This research demonstrates the applicability of GAN in automatic batik coloring, potentially providing innovative solutions for the batik industry while maintaining the cultural and artistic integrity of traditional designs.
Comparison of IndoBERT and Bi-LSTM Models for Indonesian Law Violation Text Classification Pramana, Made Wahyu Adwitya; Putri, Desy Purnami Singgih; Purnawan, I Ketut Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8795

Abstract

Legal violations in Indonesia, particularly those under the Criminal Code (KUHP) and the Information and Electronic Transactions Law (UU ITE), are often difficult for the general public to interpret due to the complexity of legal language and article structures. This research aims to build a multilabel classification model that can automatically identify relevant legal articles from user-provided case descriptions. Two models were developed and compared: Bidirectional Long Short-Term Memory (Bi-LSTM) and IndoBERT. Using a manually labeled dataset, both models were evaluated through accuracy, F1-score, and Hamming Loss metrics, as well as 5-fold cross-validation. The results showed that IndoBERT outperformed Bi-LSTM with an average accuracy of 97% and a Hamming Loss of 0.027. However, t-test analysis revealed no statistically significant difference in F1-scores, indicating that both models have comparable effectiveness in capturing multiple labels. A confusion matrix analysis further identified patterns of misclassification in semantically similar articles. This study demonstrates the potential of NLP and deep learning to support legal awareness and provide the public with easier access to legal information.
Co-Authors Adhi Dharma Wibawa, Adhi Dharma Arik Kurniawati Fitri Damayanti Mauridhi Hery Purnomo Permana, Yohan Pramana, Made Wahyu Adwitya Putri, Desy Purnami Singgih Wahyudi, Muhamad Machrus Ali