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All Journal International Journal of Artificial Intelligence Research Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Wiyanti, Zulvi
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Computer Science & IT Electrical & Electronics Engineering Health Professions Public Health

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Expert System for Diagnosis of Lung Disease from X-Ray Using CNN and SVM Zulkifli, Zulkifli; Soeprihatini, Retno Ariza; Sfenrianto, Sfenrianto; Wiyanti, Zulvi; Bintoro, Panji; Fitriana, Fitriana; Sukarni, Sukarni; Putri, Nopi Anggista; Andini, Dwi Yana Ayu
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.870

Abstract

The lung disease diagnosis expert system utilizes human knowledge to diagnose various conditions affecting the lung. Diseases caused by fungal or bacterial infection in the organ can cause inflammation as well as death when it is not detected on time. A standard method to diagnose these conditions is the use of a chest X-ray (CXR), which requires careful examination of the image by an expert. In this study, several CNN and SVM architectural models were proposed to classify CXR images to diagnose whether a person has COVID-19, Viral Pneumonia, Bacterial Pneumonia, Tuberculosis (TB), and Normal. The experiment showed that InceptionV3 had the best results compared to other CNN architectures and SVM. Classification accuracy, precision, recall, and f1-score of CXR images for COVID-19, Viral Pneumonia, Bacterial Pneumonia, TB, and Normal were 0.86, 0.91, 0.91, and 0.91, respectively. This study was based on a deep learning system with different CNN and SVM architectures that can work well on the CXR images dataset for diagnosing lung disease.
Knowledge, Attitudes, and COVID-19 Prevention Practices of Healthcare Workers in Indonesia: A Mobile-based Cross-sectional Survey Besral, Besral; Wiyanti, Zulvi; Nurizin, Dion Zein; Herdayati, Milla; Sutiawan, R; Rahmaniati, Martya; Yuniar, Popy
Kesmas Vol. 17, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Knowledge of disease can affect attitudes and prevention practices, and wrong attitudes and practices can directly increase the risk of disease infection. This study aimed to describe the knowledge, attitudes, and COVID-19 prevention practice of healthcare workers in Indonesia and factors associated with prevention practices. A mobile-based cross-sectional survey was conducted in August 2020 with 254 healthcare workers in Indonesia. The self-administered questionnaire consisted of four parts: 1) sociodemographic information, 2) knowledge of COVID-19, 3) attitudes and anxiety toward COVID-19, and 4) COVID-19 prevention practices. The results indicated that healthcare workers in Indonesia had excellent knowledge and positive attitudes about COVID-19, but their preventionpractices were lacking. The multiple logistic regression analysis results revealed that the factors associated with the COVID-19 prevention practices of healthcare workers in Indonesia were knowledge, attitudes, anxiety, domicile island, age, income, and education. Healthcare workers who had excellent knowledge, positive attitudes, and high anxiety exhibited better COVID-19 prevention practices than others. Healthcare workers in Sumatra Island, aged 41–50 years, and an undergraduate education showed better COVID-19 prevention practices than others.
Co-Authors Andini, Dwi Yana Ayu Besral . Bintoro, Panji Fitriana Fitriana Heni Pujiastuti Martya Rahmaniati Milla Herdayati, Milla Nopi Anggista Putri Nurizin, Dion Zein Popy Yuniar Sfenrianto, Sfenrianto Soeprihatini, Retno Ariza Sukarni Sukarni Sutiawan, R