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All Journal Prosiding SNATIKA Vol 01 (2011) REKAYASA Jurnal Teknik Elektro INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Economic, Bussines and Accounting (COSTING) Indonesian Journal of Information System Journal of Information System,Graphics, Hospitality and Technology Jurnal Teknologi Informasi dan Multimedia Teknika Insyst : Journal of Intelligent System and Computation JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Joutica : Journal of Informatic Unisla Eduvest - Journal of Universal Studies
Edwin Pramana
Institut Sains Dan Teknologi Terpadu Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Neuroscience Physics Social Sciences Other

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Continuance Intention Pada Aplikasi Mobile Payment Dengan Menggunakan Extended Expectation Confirmation Model M. Yahya Ubaidillah; Edwin Pramana; Francisca Haryanti Chandra
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 2 (2023): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i2.359

Abstract

This study aims to identify the factors that influence the intention to continue using the mobile payment application during the new normal period after the COVID-19 pandemic, using the Extended Expectation Confirmation Model (EECM) approach. EECM combines aspects of the Expectation Confirmation Model (ECM) with other external factors, ECM is used to understand and explain decision-making related to the continued use of mobile payments. This research was conducted by analyzing data from respondents who have used mobile payment applications after the pandemic. The data was collected through an online survey and analyzed using Structural Equation Modeling (SEM) with the help of Analysis of Moment Structures (AMOS) software, 406 individuals were selected to serve as research participants. The results of the analysis show that factors such as satisfaction, and trust have a significant influence on the continuance intention of mobile payments. In addition, in the context of the new normal, factor such as social influence factors are known to have no significant influence on mobile payment continuance intention. As a result, this research contributes to understanding the factors that influence the intention to continue using mobile payment applications. The validity and reliability test results show that the survey instrument used has an adequate level of validity and reliability, supporting the quality and reliability of the analysis conducted.
A Hierarchical Multi-Label Classification Approach for the Automated Interpretation of Spinal MRI Series Cahyadi, David; Pramana, Edwin; Limantara, Rudi; Wiguna, I Gusti Lanang Ngurah Agung Artha; Deslivia, Maria Florencia; Liando, Ivan Alexander
Intelligent System and Computation Vol 7 No 2 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i2.438

Abstract

Manually selecting MRI slices is a significant bottleneck in clinical workflows. This issue is worsened by inconsistent naming conventions and variable acquisition protocols across institutions and radiologists, often leading to redundant efforts and potential oversights during medical image data preprocessing. This study introduces a fully automated, four-level hierarchical classification system specifically designed to intelligently filter and select clinically relevant spinal MRI slices directly from raw DICOM series. Our primary objective is to streamline the initial stages of radiological assessment, ensuring that only pertinent images are presented for subsequent analysis and review. We thoroughly evaluated the performance of modern, efficient deep learning architectures, including EfficientViT, MobileNetV4, and RepViT, benchmarking them against a robust ResNet-18 baseline. The proposed pipeline systematically refines its analysis through a structured hierarchy: it first broadly identifies the anatomical region, then precisely classifies the spine location and specific view (axial, sagittal, or coronal). Subsequently, it categorizes the imaging contrast, and finally, confirms the presence of the spinal cord. Our comprehensive experimental results reveal that the EfficientViT-based model achieved the highest end-to-end F1-score of 0.8357, demonstrating robust accuracy across all classification levels. Furthermore, its average inference speed of 9.17 ms per image highlights its computational efficiency. This automated pipeline offers an effective and computationally efficient solution for speeding up initial medical image preprocessing, ensuring subsequent analytical tasks are performed on accurately selected, clinically relevant data.
Co-Authors Adithya Marhaendra Kusuma Agung Adi Saptomo Alfa'izy, Erick Ahmad Fahmi Ari Trivianto Audrey Ayu Dianaris Ayyub Cahyadi, David DAVID KURNIAWAN Deslivia, Maria Florencia Elsen Ronando Endang Setyati Esther Irawati Setiawan Francisca Haryanti Chandra Gunawan Gunawan Gunawan Gunawan Habib, Ahmad Hartarto Junaedi Herman Budianto Judi Prajetno Sugiono Keraf, Boniface Boliona Badilangoe Liando, Ivan Alexander Limantara, Rudi Lukman Zaman M. Yahya Ubaidillah Misbahul Aziz Mochamad Alfan Rosid Monica Chandra Munsharif, Achmad Purnomohadi Sutedjo, Sing Tjoen Reddy Alexandro Harianto Retno Susanti Ridho Kurniawan RIDHO KURNIAWAN, RIDHO Safitri, Lia Sasono, Hikmah Agung Titania Dwi Andini Wiguna, I Gusti Lanang Ngurah Agung Artha Yosi Kristian