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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal SPEKTRUM Jurnal Pendidikan Bahasa dan Sastra Indonesia Undiksha JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering) Teknika Jurnal Informatika: Jurnal Pengembangan IT
I Ketut Adi Purnawan
Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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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.
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.
Co-Authors A. A. Made Arta Wijaya A.A. Gede Brampramana Putra Adhi Dharma Wibawa, Adhi Dharma Aditya Bhaskara Gde Sang Altry David Purba Anak Agung Ketut Agung Cahyawan Wiranatha Anak Agung Kompiang Oka Sudana Arik Kurniawati Ayu Wirdiani Clarissa Anindita Desak Made Novita Desy Purnami Singgih Putri Dewa Putu Andre Sanjaya Dwi Rusjayanthi, Dwi Fitri Damayanti G M Arya Sasmita Gede Indra Raditya Martha Gembong Satria Prabowo I Dewa Nym. Nurweda P., I Gede Ferdika Jayusman I Gede Wira Yudha Lesmana I Gusti Ngurah Bagus Picessa Kresna Mandala I ketut Gede Darma Putra I Made Abiyoga Sanjaya I Made Ageng Suyasa I Made Agus Dwi Suarjaya I Made Dony Trisnanjaya I Made Jaya Swastika I Made Saputra Mahardika I Made Sukarsa I Nengah Martha I Nyoman Piarsa I Putu Agung Bayupati I Putu Agus Eka Pratama I Putu Arya Putra Wibawa I Putu Bayupati I Putu Mahendra Pramadhitya I Putu Yudha Ariatmaja I Wayan Ryon Waryanta I Wayan Wendra I Wayan Yoga Wirangga Ida Ayu Sri Diah Sukayanti Imelda Rizky Purba James Kawilarang Joysun Agape Sianturi Kadek Dede Hendra Kusuma Kadek Suar Wibawa Mauridhi Hery Purnomo Muhammad Febrian Rachmadhan Amri Ngurah Padang Adnyana Ni Kadek Rahayu Widya Utami Ni Luh Candra Darmayanti Ni Luh Putri Ari Wedayanti Ni Luh Putu Novi Ambariani Ni Made Ika Marini Mandenni Permana, Yohan Pramana, Made Wahyu Adwitya Putri, Desy Purnami Singgih Putu Ananta Dama Putra Putu Arismawan Jaya Kusuma Putu Wira Buana Putut Rendra Wismawan Riza Afriza Islami Sri Ambar Pratiwi Wahyudi, Muhamad Machrus Ali Wiliem Indy