Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Ilmu Komputer dan Informatika
Batubara, Gracia Mianda Caroline
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Klasifikasi Jamur Beracun Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbors Batubara, Gracia Mianda Caroline; Desiani, Anita; Amran, Ali
Jurnal Ilmu Komputer dan Informatika Vol 3 No 1 (2023): JIKI - Juni 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.68

Abstract

Jamur adalah salah satu organisme eukariot heterotrof dengan jenis yang sangat banyak, sekitar 1.500.000 di dunia. Namun, pengenalan akan jamur masih sangat kurang, dimana jumlah jamur yang sudah dikenali hanya sebanyak 74.000 jenis. Beragamnya jenis jamur ini membuat pengenalan akan klasifikasi jamur menjadi sangat penting agar manusia tidak mengonsumsi jamur beracun yang akan memberikan dampak negatif. Penelitian ini bertujuan untuk menemukan algoritma terbaik dalam pengklasifikasian jamur beracun dan tidak beracun. Klasifikasi jamur berdasarkan ciri-cirinya dapat dilakukan melalui penerapan algoritma Naïve Bayes dan k-Nearest Neighbors (kNN) pada dataset jamur. Hasilnya, algoritma Naïve Bayes memberikan rata-rata akurasi sebesar 92%, lebih kecil dibanding k-Nearest Neighbors yang memberikan rata-rata akurasi sebesar 98%. Rata-rata presisi algoritma Naïve Bayes dan k-Nearest Neighbors sama, yaitu 92,5%. Rata-rata recall algoritma Naïve bayes sebesar 91,5% dan algoritma k-Nearest Neighbors sebesar 98%. Berdasarkan rata-rata akurasi, presisi, dan recall kedua algoritma tersebut, dapat disimpulkan bahwa algoritma k-Nearest Neighbors lebih baik dibanding algoritma Naïve Bayes dalam klasifikasi jamur beracun. Namun, rata-rata akurasi, presisi, dan recall dari algoritma Naïve Bayes masih tergolong sangat baik karena nilainya berada diatas 90%.
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 Ali Amran Anita Desiani Karina Karina Kesuma, Lucky Indra Octavia , Pipin Sari , Purwita