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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika (JUTIF)
Naya, Rafi Abhista
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Computer Science & IT

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Comparative Analysis of 1D CNN Architectures for Guitar Chord Recognition from Static Hand Landmarks Naya, Rafi Abhista; Tanuwijaya, Evan
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11339

Abstract

Vision-based guitar chord recognition offers a promising alternative to traditional audio-driven methods, particularly for silent practice, classroom environments, and interactive learning applications. While existing research predominantly relies on full-frame image analysis using 2D convolutional networks, the use of structured hand landmarks remains underexplored despite their advantages in robustness and computational efficiency. This study presents a comprehensive comparative analysis of three one-dimensional convolutional neural network architectures—CNN-1D, ResNet-1D, and Inception-1D—for classifying seven guitar chord types using 63-dimensional static hand-landmark vectors extracted via MediaPipe Hands. The methodology encompasses extensive dataset preprocessing, targeted landmark augmentation, Bayesian hyperparameter optimization, and stratified 5-fold cross-validation. Results show that CNN-1D achieves the highest mean accuracy (97.61%), outperforming both ResNet-1D and Inception-1D, with statistical tests confirming significant improvements over ResNet-1D. Robustness experiments further demonstrate that CNN-1D maintains superior resilience under Gaussian noise, landmark occlusion, and geometric scaling. Additionally, CNN-1D provides the fastest inference and most stable computational performance, making it highly suitable for real-time or mobile deployment. These findings highlight that, for structured and low-dimensional landmark data, simpler convolutional architectures outperform deeper or multi-branch designs, offering an efficient and reliable solution for vision-based guitar chord recognition.
Comparative Analysis of IndoBERT and mBERT for Online Gambling Comment Detection in Indonesian Social Media Nugraha, Satria Adi; Lestari, Citra; Sanjaya, Karyna Budi; Naya, Rafi Abhista; Jolie, Jocelyn
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5677

Abstract

The rapid growth of illegal online gambling promotions in Indonesian social media comments requires automated detection systems capable of handling informal and noisy text. This study aims to evaluate the effectiveness of Transformer-based language models for detecting online gambling-related comments in Indonesian Twitter and YouTube data. Two pre-trained models, IndoBERT and mBERT, were fine-tuned and compared using a labeled dataset consisting of gambling and non-gambling comments. Model performance was evaluated using accuracy, precision, recall, and F1-score. Experimental results show that IndoBERT achieved 98% accuracy and F1-score, outperforming mBERT, which achieved 96% on the same dataset. Additionally, performance was compared against a recurrent neural network baseline to validate the effectiveness of Transformer-based architectures. The findings demonstrate that language-specific pre-training provides measurable advantages for detecting domain-specific content in Indonesian social media. This study contributes empirical evidence supporting the application of Transformer models for automated moderation of harmful online content in Indonesian digital platforms.
Co-Authors Citra Lestari Evan Tanuwijaya Jolie, Jocelyn Sanjaya, Karyna Budi Satria Adi Nugraha