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All Journal ELKHA : Jurnal Teknik Elektro Sosaintek: Jurnal Ilmu Sosial Sains dan Teknologi
Khoiriyah, Ummatul
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Computer Aided Classification of X-ray Images from Pediatric Pneumonia Subjects Collected in Developing Countries Amrulloh, Yusuf Aziz; Prasetyo, Bayu Dwi; Khoiriyah, Ummatul; Gunarti, Hesti; Setyowireni, Dwikisworo; Triasih, Rina; Naning, Roni; Setyati, Amalia
ELKHA : Jurnal Teknik Elektro Vol. 15 No.2 October 2023
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v15i2.69981

Abstract

Pneumonia is a lower tract respiratory infection due to bacteria or viruses. It is a severe disease in the pediatric population. Pneumonia is the leading cause of mortality in children under five years worldwide. One of the problems with pneumonia is the diagnosis, as the symptoms of pneumonia may overlap with other diseases, such as asthma and bronchiolitis. In this work, we propose to develop a method for classifying pneumonia and non-pneumonia using X-ray images. We collected 60 X-ray images from Dr. Sardjito Hospital, Yogyakarta, Indonesia, and the dataset from Kaggle. We processed these images through pre-processing algorithms to enhance the image quality, segmentation, white pixel computation, and classification. The novelty of our method is using the ratio of the white pixels from edge detection using the Canny algorithm with the white pixels from segmentation for classifying pneumonia/non-pneumonia. In the Kaggle dataset, our proposed method achieved an accuracy of 86.7%, a sensitivity of 100%, and a specificity of 85%. The classification using the dataset from Dr. Sardjito Hospital yields sensitivity, specificity, and accuracy of 80%, 60%, and 66.7%, respectively. Despite the low performance in the results, we proved our novel feature, ratio of white pixels, can be used to classify pneumonia/non-pneumonia. We also identified that the local dataset is essential in the algorithm development as it has a different quality from the dataset from modern countries. Further, our simple method can be developed further to support pneumonia diagnosis in resource-limited settings where the advanced computing devices or cloud connection are not available.
AI dan Guru di Dunia Pendidikan: Bukan Kompetisi, tapi Kolaborasi Nuha, Muhammad Ullin; Atikoh, Nurul; Safitri, Maulida; Khoiriyah, Ummatul; Alhasan, Sudap
Sosaintek: Jurnal Ilmu Sosial Sains dan Teknologi Vol. 1 No. 4 (2024): Sosaintek: Jurnal Ilmu Sosial Sains dan Teknologi, Desember, 2024
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/sosaintek.v1i4.7315

Abstract

Advances in technology, particularly artificial intelligence (AI), have had a significant impact on education. However, instead of replacing teachers, AI should be seen as a partner that can enrich the teaching and learning process. This research aims to explore how AI can support teachers in creating a more personalised and effective learning experience. The research method used was a literature study and in-depth interviews with educators to understand the perceptions and challenges of AI integration in the classroom. The results show that AI has great potential in supporting learning through personalisation of materials, analysis of student learning data, and reduction of teachers' administrative burden. However, this collaboration will only be successful if there is adequate training for teachers as well as an ethical approach in the application of such technology.
Co-Authors Alhasan, Sudap Amalia Setyati Amrulloh, Yusuf Aziz Dwikisworo Setyowireni Gunarti, Hesti Nuha, Muhammad Ullin Nurul Atikoh, Nurul Prasetyo, Bayu Dwi Rina Triasih Roni Naning Safitri, Maulida