Article Per Year (5 Year)
p-Index From 2021 - 2026
1.756
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics International Journal of Artificial Intelligence Research Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ilmiah Keperawatan Sai Betik Aisyah Journal of Informatics and Electrical Engineering Wellness And Healthy Magazine Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Pengabdian Kepada Masyarakat Ungu ( Abdi Ke Ungu) Jurnal Ilmu Kesehatan Indonesia (JIKSI) Jurnal Maternitas Aisyah (JAMAN AISYAH)
Fitriana Fitriana
Universitas Aisyah Pringsewu
Computer Science & IT Education Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health Veterinary Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Application of Lung Diseases Detection based on CSLNet Panji Bintoro; Zulkifli Zulkifli; Fitriana Fitriana; Sukarni Sukarni; Abdullah Abdullah
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 3 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i3.68815

Abstract

Lung diseases caused by fungal or bacterial infections can lead to inflammation in lung and even death when not detected early. A standard method for diagnosing lung diseases is the use of chest X-ray, which require careful examination of X-ray images by a radiology expert. Therefore, this study proposes several new architecture models, namely CSLNet, to classify chest X-ray images for diagnosing whether patients suffer from COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, and normal. The experimental results show that the model has an 0.99 average Accuracy, 0.98 Precision, 0.98 Recall, and 0.98 f1-score. Meanwhile, the Receiver Operating Characteristic (ROC) for bacterial pneumonia, COVID-19, normal, tuberculosis, and viral pneumonia are 0.97, 0.99, 0.99, 0.94, and 0.97 respectively. This study is based on a deep learning with a new model, CSLNet, which can work well on the dataset of chest X-ray images used for diagnosing lung diseases.
Deep convolutional neural network for Lampung character recognition Bintoro, Panji; Zulkifli, Zulkifli; Fitriana, Fitriana; Sukarni, Sukarni
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6734

Abstract

Recognition of document based, and handwritten characters has recently emerged as highly relevant field of study in the field of digital image processing. The ability to read and write Lampung script is a crucial competency as it helps preserve the language, which is a part of Indonesian culture. This research utilizes data obtained from classified documents and handwritten samples, categorized into eight types. To recognize Lampung characters, deep convolutional neural network (DCNN) architecture is proposed. The novelty of this architecture lies in optimizing document-based and handwritten character recognition to achieve the best performance in terms of accuracy and execution time. The proposed architecture will be compared to principal component analysis (PCA) combined with support vector machine (SVM) to evaluate its results. Experimental results using the DCNN architecture show an average accuracy of 99.3% and an execution time of 283 seconds for all data, while PCA and SVM exhibit an average accuracy of 92.9%. Furthermore, the recognition results for all data from documents and handwritten samples yield satisfactory accuracy of 98.6%. These results make the DCNN architecture suitable for use in recognizing Lampung characters and are expected to make it easier for Lampung people to recognize Lampung character.
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.
Co-Authors Abdullah Abdullah Andini, Dwi Yana Ayu Ani Kristianingsih Aprilia Pratiwi Azri Afdhalluddin Beniqna Maharani Bertalina Bertalina Bintoro, Panji Desi Kumalasari Dewi Yunarti Fauziah, Nur Alfi Hellen Febriyanti Heni Pujiastuti Ika Fitria Elmeida Komalasari Komalasari Lia Puspita Maulia Isnaini Monica Dara Delia Suja Nopi Anggista Nopi Anggista Putri Nopi Anggista Putri, Nopi Anggista Nurul Badriyah Psiari Kusuma Wardani Saras Cipta Casmi Septika Yani Veronica Setiani Setiani Sfenrianto, Sfenrianto Soeprihatini, Retno Ariza Sri Lita Yani Suci Lanavia Sudarmi Sudarmi Sukarni Sukarni Sukarni Sukarni Sukarni Sulistiawati, Yuni Tahta Herdian Andika Wiyanti, Zulvi Yuliarti, Elis Yuni Sulistiawatia Yuni Sulistyawati Yuniastini Yuniastini Yunita Anggriani Zulkifli