Ade Ismail Abdul Kodir, Ade Ismail
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Efektivitas nanoemulsi gel daun sirih hijau (Piper betle l.) terhadap penurunan ketebalan biofilm bakteri Staphylococcus aureus ATCC 6538: eksperimental laboratoris Maliky, Zaid; Abdul Kodir, Ade Ismail; Agusmawanti, Prima; Pratiwi, Rosa
Jurnal Kedokteran Gigi Universitas Padjadjaran Vol 36, No 3 (2024): Desember 2024
Publisher : Fakultas Kedokteran Gigi Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jkg.v36i3.57721

Abstract

Pendahuluan: Penyakit periodontal adalah infeksi pada rongga mulut yang diakibatkan oleh bakteri dengan persentase kasus 74,1%. Salah satu bakteri yang berperan adalah Staphylococcus aureus. Bakteri ini merupakan salah satu bakteri gram positif yang berperan pada kolonisasi awal. Sirih hijau merupakan tanaman herbal yang memiliki kandungan senyawa antibakteri seperti flavonoid, alkaloid, tanin dan saponin. Teknologi nanoemulsi gel memiliki keunggulan untuk mempermudah penghantaran obat karena ukuran partikelnya yang sangat kecil. Penelitian ini bertujuan untuk menganalisis efektivitas nanoemulsi gel daun sirih hijau terhadap penurunan ketebalan biofilm bakteri Staphylococcus aureus. Metode: Metode penelitian ini menggunakan eksperimental laboratorium in vitro yang berjumlah 25 sampel dibagi menjadi 5 kelompok yang terdiri dari nanoemulsi gel daun sirih hijau konsentrasi 30%, 40%, 50%, gel metronidazole dan aquadest. Pembacaan hasil uji ketebalan biofilm diukur dengan Optical Density menggunakan ELISA-reader. Hasil: Hasil rerata nilai optical density pada kelompok formulasi nanoemulsi gel daun sirih hijau, konsentrasi 50% memiliki nilai terendah (2,732) dibandingkan 30% (3,478) dan 40% (3,352) yang artinya konsentrasi 50% paling efektif dibandingkan konsentrasi 30% dan 40%. Hasil uji One Way Anova didapatkan hasil p<0,05 menunjukkan adanya pengaruh nanoemulsi gel daun sirih hijau terhadap penurunan ketebalan biofilm bakteri Staphylococcus aureus. Simpulan: Formulasi nanoemulsi gel daun sirih hijau efektif dalam menurunkan ketebalan biofilm bakteri Staphylococcus aureus.The effectiveness of green betel leaf (piper betle l.) nanoemulsion gel on reducing of staphylococcus aureus ATCC 6538 biofilm thickness: laboratory experimentalIntroduction: Periodontal disease is an infection in the oral cavity caused by bacteria, with a case prevalence of 74.1%. One of the bacteria involved is Staphylococcus aureus. This bacterium is a Gram-positive organism that plays a role in early colonization. Green betel (Piper betle) is a medicinal plant that contains antibacterial compounds such as flavonoids, alkaloids, tannins, and saponins. Nanoemulsion gel technology has the advantage of enhancing drug delivery due to its very small particle size. This study aims to analyze the effectiveness of green betel leaf nanoemulsion gel in reducing the biofilm thickness of Staphylococcus aureus bacteria. Methods. This research used an in vitro laboratory experimental method involving 25 samples divided into 5 groups, consisting of green betel leaf nanoemulsion gel at concentrations of 30%, 40%, 50%, metronidazole gel, and aquadest. The thickness of the biofilm was measured using an ELISA reader by reading the optical density. Results. The average optical density values in the green betel leaf nanoemulsion gel formulation group showed that the 50% concentration had the lowest value (2.732) compared to 30% (3.478) and 40% (3.352), indicating that the 50% concentration was the most effective among the three tested concentrations. The One-Way ANOVA test results showed p < 0.05, indicating that the green betel leaf nanoemulsion gel had a significant effect on reducing the biofilm thickness of Staphylococcus aureus bacteria. Conclusion. It can be concluded that the green betel leaf nanoemulsion gel formulation is effective in reducing the biofilm thickness of Staphylococcus aureus bacteria.
Hybrid XAI and deep learning architecture for trustworthy dental diagnostics Fadhillah, Yusra; Hasan Siregar, Muhammad Noor; Abdul Kodir, Ade Ismail; Rizki, Khairur
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.