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All Journal Teika SISFOTENIKA Jurnal Komputer Terapan CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) CSRID (Computer Science Research and Its Development Journal) SENSITEK JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Klabat Journal of Management SmartComp Servitium Smart Journal Jurnal Pengabdian Kepada Masyarakat Indonesia
Green Arther Sandag
Fakultas Ilmu Komputer - Universitas Klabat
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Nursing Physics Public Health Social Sciences

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Journal : CogITo Smart Journal

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Comparative Analysis of Lung Cancer Classification Models Using EfficientNet and ResNet on CT-Scan Lung Images Green Arther Sandag; Deo Timothy Kabo
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.706.680-691

Abstract

This study investigates the classification of lung cancer, a major global cause of mortality. The accurate diagnosis and classification of lung cancer through CT-Scan images demand significant expertise, precision, and time to ensure appropriate treatment for patients. Transfer learning has emerged as a beneficial technology to aid in this process by effectively classifying lung cancer-related patterns in CT-Scan images. In this research, a dataset of 1,000 lung CT-Scan images, divided into four categories—Adenocarcinoma, Large Cell, Squamous, and Normal—was employed. The study evaluated several transfer learning models, including ResNet50, ResNet101, EfficientNetB1, EfficientNetB3, EfficientNetB5, and EfficientNetB7. The findings revealed that the EfficientNetB3 model outperformed the others, achieving an accuracy of 97.78%, a precision of 97.34%, a recall of 98.33%, and an F1-Score of 97.78%. These results demonstrate that the EfficientNetB3 model enhances the accuracy of lung cancer classification in CT-Scan images more effectively than other transfer learning models. This research underscores the significant potential of EfficientNetB3 in facilitating early diagnosis, advancing the integration of machine learning in medical practices, and providing critical insights for the selection of transfer learning models in clinical applications. The implications of these findings suggest a substantial impact on improving diagnostic processes and outcomes in lung cancer management.
Sentiment Analysis and Topic Detection on Post-Pandemic Healthcare Challenges: A Comparative Study of Twitter Data in the US and Indonesia Tangka, George Morris William; Chrisanti, Ibrena Reghuella; Waworundeng, Jacquline; Maringka, Raissa Camilla; Sandag, Green Arther
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.819.561-579

Abstract

This study examines public sentiment and key topics in Twitter discussions regarding the COVID-19 vaccine and the Omicron variant in the US and Indonesia. The importance of this research lies in understanding people's changing views on vaccination, especially in light of new virus variants. Using sentiment analysis with VADER and topic modeling with Latent Dirichlet Allocation (LDA), this research analyzes 637,367 tweets from the US and 91,679 tweets from Indonesia collected over two months from January 21 to February 21, 2022. The results reveal that US discussions on vaccines are predominantly positive, while those on Omicron are mostly negative. In contrast, discussions in Indonesia are largely neutral, followed by positive sentiment. Additionally, five main topics were identified for each country, with the US showing a broader range of vaccine-related discussions. These findings suggest that while the vaccine is seen as a source of hope in both countries, factors such as literacy, socioeconomic status, and education contribute to negative sentiment and vaccine resistance.
MRI Image Analysis for Alzheimer’s Disease Detection Using Transfer Learning: VGGNet vs. EfficientNet Sandag, Green Arther; Djamal, Eleonora; Tangka, George Morris William; Taju, Semmy Wellem
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.836.580-592

Abstract

This study focuses on developing an effective Alzheimer's disease (AD) classification model using MRI images and transfer learning. This research targets individuals aged 65 and above who are affected by the predominant form of dementia and utilizes an Alzheimer's Disease MRI Image dataset from Kaggle. Model selection involved options like EfficientNetB1, B3, B5, B7, VGG16, and VGG19. Two scenarios with distinct batch sizes (10 and 20) were explored in the model creation process. Evaluation, using a confusion matrix, determined that the EfficientNetB5 model yielded the highest accuracy at 99.22%, surpassing other models such as EfficientNetB1, B3, B7, VGG16, and VGG19. Notably, this research highlights the superior performance of EfficientNet over VGGNet in transfer learning for analyzing Alzheimer's disease MRI images. The study concludes with the implementation of a simple web system for testing model outcomes. Overall, the investigation underscores the efficacy of Convolutional Neural Network (CNN) modeling in Alzheimer's disease analysis and identifies EfficientNetB5 as the optimal model for accurate classification.
Co-Authors Aldo Sabathos Mananta Andrew Tanny Liem Anggreiny Rolangon arlius lalong Aror, Megalita Axcel Weku Chrisanti, Ibrena Reghuella Deo Timothy Kabo Diaprilia Sondakh Djamal, Eleonora Djimesha, Enrico S. Edson Yahuda Putra Erienika Meiling Lompoliu Fini Keni Celsia George Morris William Tangka Godlife Davidson Rellely Grady Wantah Green Ferry Mandias, Green Ferry Haryanto Reza Musak Jacquline Waworundeng Jebriella Bororing Joe Yuan Mambu Jonathan Leopold Kaunang, Fergie Joanda Lidya Chitra Laoh Makapedua, Marlivye Mananta, Aldo Sabathos Marchel Thimoty Tombeng Marcviero Patresse Matauseya Maringka, Raissa Raymond Sambur Reynoldus Andrias Sahulata Semmy Wellem Taju stevanlee ngeloh Susi Susanti Tompunu, Alexander Valentine Lumingkewas Vinky Fransiscus Ong Wungkana, Cindy