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All Journal Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Edita Rosana Widasari
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : Journal of Information Technology and Computer Science

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Village Data Backup and Disaster Recovery: A Comparative Study of Cloud Solutions with Traditional Methods Prasetio, Barlian Henryranu; Edita Rosana Widasari; Adi Setiawan; Hanifa Maulani Ramadhan
Journal of Information Technology and Computer Science Vol. 9 No. 3: December 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.93681

Abstract

This study presents a comparative analysis of traditional disk-based and cloud-based Backup and Disaster Recovery (DR) approaches, focusing on the challenges inherent in existing solutions, such as high infrastructure costs, extended recovery times, and operational disruptions during software updates. Conventional disk-based systems often require periodic reboots and manual interventions, which can interrupt ongoing operations. In contrast, cloud-based solutions, particularly Asigra, offer a streamlined alternative by reducing infrastructure dependency, enhancing recovery metrics—specifically Recovery Point Objective (RPO) and Recovery Time Objective (RTO)—and minimizing maintenance downtime through agentless and incremental backups. Cloud backup provides comprehensive upgrades without the need for system reboots, thereby saving time and improving operational effectiveness. This study evaluates the proposed cloud-based approach in a village government organizational environment, analyzing its performance based on Total Cost of Ownership (TCO), RPO, and RTO. Cloud-based configurations are compared with traditional setups to assess improvements in disaster recovery procedures and data storage. Findings demonstrate that cloud-based strategies offer simpler and more efficient DR solutions, providing superior scalability, reliability, and administrative ease tailored to the unique needs of village government data management.
Enhancing Brain Tumor MRI Classification Performance Using EfficientNetV2-B3 with an Efficient Channel Attention Module Navira Rahma Salsabila; Lailil Muflikhah; Edita Rosana Widasari
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103846

Abstract

Early identification of brain tumors using magnetic resonance imaging helps doctors make quick and informed decisions about treatment. Although recent deep learning approaches achieve high accuracy, many rely on complex architectures that increase computational cost and limit interpretability. In order to overcome these constraints, this work proposes a system for four-class brain tumor classification utilizing a public MRI dataset of 3,264 images that is built on EfficientNetV2-B3 and an Efficient Channel Attention (ECA) module used after feature extraction and Grad-CAM. The ECA module enhances cross-channel feature representation with minimal computational overhead. Experimental results indicate consistent performance gains over the baseline model, with accuracy increasing from 97.58% to 99.09% and macro-averaged F1-score from 97.51% to 99.08%. Despite the strong baseline, these gains are achieved without increasing architectural complexity. Grad-CAM visualizations support model interpretability by highlighting tumor-relevant regions that contribute most to the classification decisions. Overall, the proposed framework provides a balanced trade-off between classification accuracy, computational efficiency, and interpretability within the evaluated setting.
Co-Authors 'Ulum, Muhammad Bahrul Adi Setiawan Annathifa, Azza Barlian Henryranu Prasetio Dahnial Syauqy Fuad, Alifa Azwadina Hanifa Maulani Ramadhan Hutagaol, Yohanes Ronaldo Juliantino Lailil Muflikhah Navira Rahma Salsabila Niswa, Khoirun Safrudin Bendang, Dehleezto Lawanangkara Salam, Muhammad Daffa Haekal