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All Journal Bulletin of Electrical Engineering and Informatics ODONTO Dental Journal
Ade Ismail Abdul Kodir
Sultan Agung Islamic University
Electrical & Electronics Engineering Medicine & Pharmacology

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Hybrid XAI and deep learning architecture for trustworthy dental diagnostics Yusra Fadhillah; Muhammad Noor Hasan Siregar; Ade Ismail Abdul Kodir; Khairur Rizki
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11193

Abstract

Dental periodontal disease is a persistent an inflammatory disorder affecting tooth supporting tissues and stays a main motive of tooth loss. Although dental radiographs are essential for early diagnosis, their interpretation is often subjective and inconsistent due to reliance on clinician expertise. This study proposes an automated and interpretable diagnostic framework using a convolutional neural network (CNN) integrated with gradient-weighted class activation mapping (Grad-CAM). The CNN performs binary classification of periapical radiographs into periodontal and normal categories, while Grad-CAM provides visual explanations of the model’s decision-making process. Experimental results show that the proposed model achieves a classification accuracy of 94.17%, indicating reliable diagnostic performance. The generated heatmaps consistently highlight clinically relevant regions, particularly alveolar bone loss in periodontal cases, whereas normal images exhibit no pathological activation. These findings demonstrate that the proposed CNN–Grad-CAM framework enhances both diagnostic accuracy and interpretability. The study contributes a transparent and trustworthy artificial intelligence solution to support objective periodontal disease diagnosis in dental radiology.
Inhibitorty Activity of Lemongrass (Cymbopogon Nardus, (L.) Rendle) Essential Oils on The Growth of Bacterial Phorpyromonas Gingivalis and Aggregatibacter Actinomycetemcomitans In Vitro Kosno Suprianto; Emriadi Emriadi; Ade Ismail Abdul Kodir; Dian Lestari
Odonto : Dental Journal Vol. 11 No. 2 (2024): December 2024
Publisher : Faculty of Dentistry, Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/odj.11.2.359-365

Abstract

Background: Periodontal disease is one of oral and dental health problems that suffered by many people in the world and occurs in 50% of adult population. Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans are the main bacteria that cause periodontitis. One of the plants that is often used as an alternative antimicrobial agent is citronella (Cymbopogon nardus L.) which contains citronellal and geraniol compounds. This study aims to determine whether the essential oils of lemongrass leaves have inhibitory effect on the growth of Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans in vitro. Method: The production of lemongrass essential oils is carried out by the distillation method and 25%, 50%, and 75% concentration of essential oils are diluted using Tween 20 and distilled water. The inhibition test was carried out by the Kirby-Bauer disk diffusion method on Mueller Hinton Agar media. Result: The analysis showed that there was a significant difference between the inhibitory power produced by each concentration of essential oil of citronella leaf on the growth of Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans. Conclusion: The conclusion of this study is the essential oils of lemon grass with concentrations of 25%, 50%, 75%, and 100% have different inhibitory properties in inhibiting the growth of Porphyromonas gingivalis bacteria and Aggregatibacter actinomycetemcomitans.
Co-Authors Dian Lestari Emriadi - Khairur Rizki Kosno Suprianto Muhammad Noor Hasan Siregar Yusra Fadhillah