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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Masyarakat (Jubdimas)
Sari , Purwita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health

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Membangkitkan Motivasi Menjadi Pemimpin Dalam Mengelola Bisnis Bagi Para Siswa SMU Al Ittihadiyah Medan Sari, Tika Nirmala; Parhusip, Austin Alexander; Risal, Taufiq; Sari , Purwita; Siregar, Ratih Anggraini
JUBDIMAS ( Jurnal Pengabdian Masyarakat) Vol 1 No 1 (2022): Jurnal Pengabdian Masyarakat, Maret 2022
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jubdimas.v1i1.38

Abstract

The limited number of jobs is one of the causes of the high unemployment rate among the younger generation. Young people prefer to find work rather than create jobs that can absorb labor. Therefore, it is important to change this pattern of thinking, namely changing the desires of the young generation of productive age from being salary recipients (employees) to salary providers (business owners) or in other words, not only as job seekers but also able to create job opportunities for people. That's why it is important to instill the attitude and knowledge of an Leadership for early on to the young generation. The target audience of this service is the students of SMA Al Ittidaiyah Medan, which was attended by 25 students as participants. From the evaluation results, the results and benefits of this service activity include students motivated to be a leader on business t and they understanding the steps in determining the type of business to be carried out, the process also requirements to become a successful leader. Through Community Service activities that are packaged in the form of a seminar, it is hoped that it can generate desire and encourage students to start their new business
Combination Of Gamma Correction and Vision Transformer In Lung Infection Classification On CT-Scan Images Kesuma, Lucky Indra; Octavia , Pipin; Sari , Purwita; Batubara, Gracia Mianda Caroline; Karina, Karina
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.588

Abstract

Lung infection is an inflammatory condition of the lungs with a high mortality rate. Lung infections can be identified using CT-Scan images, where the affected areas are analyzed to determine the infection type. However, manual interpretation of CT-Scan results by medical specialists is often time-consuming, subjective, and requires a high level of accuracy. To address these challenges, this study proposes an automated classification method for lung infections using deep learning techniques. Convolutional Neural Networks (CNNs) are widely used for image classification tasks. However, CNN operates locally with limited receptive fields, making capturing global patterns in complex lung CT images challenging. CNN also struggles to model long-range pixel dependencies, which is crucial for analyzing visually similar regions in lung CT-Scans. This study uses a Vision Transformer (ViT) to overcome CNN limitations. ViT employs self-attention mechanisms to capture global dependencies across the entire image. The main contribution of this study is the implementation of ViT to enhance classification performance in lung CT-Scan images by capturing complex and global image patterns that CNN fails to model. However, ViT requires a large dataset to perform optimally. To overcome these challenges, augmentation techniques such as flipping, rotation, and gamma correction are applied to increase the amount of data without altering the important features. The dataset comprises lung CT-scan images sourced from Kaggle and is divided into Covid and Non-Covid classes. The proposed method demonstrated excellent classification performance, achieving accuracy, sensitivity, specificity, precision, and F1-Score above 90%. Additionally, the Cohen’s kappa coefficient reached 89%. These results show that the proposed method effectively classifies lung infections using CT-Scan images and has strong potential as a clinical decision-support tool, particularly in reducing diagnostic time and improving consistency in medical evaluations.
Co-Authors Batubara, Gracia Mianda Caroline Karina Karina Kesuma, Lucky Indra Octavia , Pipin Parhusip, Austin Alexander Risal, Taufiq Sari, Tika Nirmala Siregar, Ratih Anggraini