Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknik Informatika (JUTIF)
Bakar, Juhaida Abu
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm Toresa, Dafwen; Shahril, Mohamad Azrul Edzwan; Harun, Nor Hazlyna; Bakar, Juhaida Abu; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.664

Abstract

Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.
Complex Word Identification in Indonesian Children’s Texts: An IndoBERT Baseline and Error Analysis Lisnawita, Lisnawita; Bakar, Juhaida Abu; Rasli, Ruziana Mohamad; Costaner, Loneli; Guntoro, Guntoro
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5501

Abstract

Complex Word Identification (CWI) is a crucial step for building text simplification systems, especially for Indonesian children’s reading materials where unfamiliar vocabulary can hinder comprehension. This study formulates token-level CWI for Indonesian children’s texts and establishes two baselines:  an interpretable rule-based model using linguistic features e.g., length, syllable heuristics, and affix patterns, and an IndoBERT model fine-tuned for token classification. This study construct and annotate a children’s text corpus and evaluate both approaches using standard classification metrics. On the test set (22.584 tokens), IndoBERT achieves an F1-score of 0.9972 for the CWI class, substantially outperforming the rule-based baseline (F1 = 0.8607). The IndoBERT system makes only 39 errors (23 false positives and 16 false negatives), indicating near-perfect performance under the evaluated setting. Furthermore, this study provides an error analysis to highlight remaining failure patterns and borderline cases that are difficult even for contextual models. The resulting benchmark and findings contribute to Informatics/Computer Science by providing a strong baseline and analysis for educational NLP in a low-resource language setting, supporting the development of Indonesian child-oriented NLP resources and downstream text simplification tools.
Co-Authors Guntoro, Guntoro Harun, Nor Hazlyna Hidra Amnur Lisnawita Lisnawita Loneli Costaner Rasli, Ruziana Mohamad Shahril, Mohamad Azrul Edzwan Toresa, Dafwen