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All Journal Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Informatika (JUTIF)
Sanjaya, Karyna Budi
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Computer Science & IT

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DETECTION AND CLASSIFICATION OF GRAM-STAINED BACTERIA IN MICROSCOPIC IMAGES USING YOLOV8 WITH CBAM Sanjaya, Karyna Budi; Wonohadidjojo, Daniel Martomanggolo
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 3 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10891

Abstract

Bloodstream infection accounts for approximately 11 million deaths annually, and yet conventional blood culture methods require 40-48 hours to complete pathogen identification which delays definitive therapeutic decisions. Gram staining does provide preliminary bacterial classification within hours, but manual interpretation still remains a labor-intensive task and is prone to variability. This study develops an automated bacterial detection and classification system by integrating CBAM into the YOLOv8 architecture. The model was trained on Gram-stained microscopic images across four bacterial categories: Gram-positive cocci, Gram-negative cocci, Gram-positive bacilli, and Gram-negative bacilli. Dataset preprocessing involved quality selection, noise reduction, and targeted augmentation to address severe class imbalances. The inclusion of CBAM improved feature discrimination and localization performance, with an increase of 1.4% in mAP@0.5:0.95 (from 70.8% to 72.2%). The proposed model also reduced cross-class misclassifications, particularly among morphologically similar cocci. These findings demonstrate that integrating lightweight attention mechanisms can enhance bacterial detection reliability in microscopic imaging and support the development of automated systems for faster, more consistent preliminary bacterial identification.
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 Daniel Martomanggolo Wonohadidjojo Jolie, Jocelyn Naya, Rafi Abhista Satria Adi Nugraha