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All Journal Medula International Journal of Artificial Intelligence Research Jurnal Ilmu Kedokteran dan Kesehatan Jurnal Aisyah : Jurnal Ilmu Kesehatan
Soeprihatini, Retno Ariza
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Electrical & Electronics Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Nursing Public Health Veterinary

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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 : Universitas 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.
Analysis of Respondent Characteristics Regarding The Severity of Community-acquired Pneumonia Patients at Dr. H. Abdul Moeloek Hospital, Lampung. Sanjaya, Rizki Putra; Herdato, M. Junus Didiek; Ajipurnomo, Adhari; Herliyana, Lina; Dilangga, Pad; Soeprihatini, Retno Ariza; Lyanda, Apri; Hendarto, Gatot Sudiro; Sinaga, Fransisca Tarida Yuniar; Kusumajati, Pusparini
Jurnal Aisyah : Jurnal Ilmu Kesehatan Vol 10, No 1 (2025): March
Publisher : Universitas Aisyah Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30604/jika.v10i1.3096

Abstract

Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide, particularly among the elderly and immunocompromised patients. Various factors, including age, gender, bacterial pattern, comorbidities, and Pneumonia Severity Index (PSI) score, influence CAP severity. Understanding these characteristics is crucial for optimizing treatment strategies and predicting clinical outcomes. This descriptive-analytical cross-sectional study was conducted at Dr. H. Abdul Moeloek Hospital, Lampung, from December 2022 to January 2023. A total of 102 CAP patients were analyzed based on their age, gender, bacterial pattern, comorbidities, and PSI score. Data were processed using IBM SPSS 21.0, with Pearson correlation applied to assess relationships between variables. A statistically significant correlation was found between age and PSI score (p = 0.018), with a low-strength positive correlation (R = 0.234). This suggests that as age increases, CAP severity also increases (p = 0.011). However, no significant correlations were found between PSI score and gender, bacterial pattern, or diabetes mellitus. This study confirms that age is a significant factor influencing CAP severity, emphasizing the need for early screening and intervention in elderly patients. Although gender, bacterial pattern, and diabetes mellitus were not significantly correlated with CAP severity. 
Byssinosis, An Occupational Lung Disease: Diagnosis And Management Mustofa, Syazili; Izzati, Marwah Aulia; Soeprihatini, Retno Ariza; Komala, Ramadhana; Silaen, David; Sinaga, Fransisca T.Y.
Jurnal Ilmu Kedokteran dan Kesehatan Vol 12, No 5 (2025): Volume 12 Nomor 5
Publisher : Prodi Kedokteran Fakultas Kedokteran Universitas Malahayati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jikk.v12i5.18864

Abstract

Byssinosis is an occupational lung disorder mainly caused by exposure of cotton dust during manufacturing. Over the years, it is well known as “Brown lung disease” and “cotton worker’s lung”. Byssinosis is characterized by respiratory symptoms such as cough, shortness of breath, chest tightness, and airway obstruction. The pathogenesis is still unclear. Despite the exposure to cotton dust, endotoxin release that resides in gram-negative in cotton dust also plays an important role in mediating byssinosis. Specify that supporting examination is not yet available. Thus, it is encouraging to relate the history of cotton dust exposure and the symptoms. In byssinosis, medication is given based on the symptoms that occur.
Paparan CO pada Lingkungan Kerja dan Dampak terhadap Kesehatan Izzati, Marwah Aulia; Soeprihatini, Retno Ariza; Komala, Ramadhana
Medula Vol 14 No 11 (2024): Medula
Publisher : CV. Jasa Sukses Abadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53089/medula.v14i11.1364

Abstract

Air pollution is waste produced from human activities in meeting their needs can become air pollution that has an impact on disorders and diseases due to air pollution, especially in the respiratory tract due to long-term exposure. Carbon monoxide is one of the toxic substances resulting from the process of imperfect combustion and driving fumes. Exposure to carbon monoxide in the respiratory system results in the formation of a bond of carbon monoxide (CO) with hemoglobin to become COHb. Carbon monoxide (CO) and O2 compete to occupy the same binding site in Hb, but the affinity of Hb for CO is 240 times greater than for O2. Other factors that cause the formation of COHb in the blood besides exposure to CO pollution such as smoking habits. chemicals contained in each cigarette, some of which are toxic, including CO gas. A person who smokes one pack of cigarettes/day can have a COHb level of 3% to 6% in the blood, with two packs/day the level can be 6% to 10% and as much as 20% with three packs/day. The easiest effort to avoid risk is the use of proper personal protective equipment. In this case, the use of masks can be useful because the exposure process is through inhalation. This study is a literature review that collects and evaluates research using Google Scholar with the keywords "Carbon Monoxide, COHb, Carbon Monoxide, workers, pollution". The results of the article review obtained several data on CO levels in the bodies of workers caused by exposure to CO gas that occurs due to their work environment. This concludes that exposure to CO gas can increase CO levels in the human body which has an effect on a person's health, especially in their breathing.
Co-Authors Ajipurnomo, Adhari Andini, Dwi Yana Ayu Apri Lyanda Bintoro, Panji Dilangga, Pad Fitriana Fitriana Gatot Sudiro Hendarto, Gatot Sudiro Heni Pujiastuti Herdato, M. Junus Didiek Herliyana, Lina Izzati, Marwah Aulia Kusumajati, Pusparini Nopi Anggista Putri Ramadhana Komala Rizki Putra Sanjaya Sfenrianto, Sfenrianto Silaen, David Sinaga, Fransisca T.Y. Sinaga, Fransisca Tarida Yuniar Sukarni Sukarni Syazili Mustofa Wiyanti, Zulvi