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All Journal International Journal of Technology and Modeling
Firdaus, Taufik Ramadhan
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : International Journal of Technology and Modeling

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Medical Image Reconstruction in MRI Using Interpolation Liya, Abel; Ningsih, Resti; Hidayat, Rafi; Firdaus, Taufik Ramadhan
International Journal of Technology and Modeling Vol. 3 No. 1 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i1.99

Abstract

Medical image reconstruction is a crucial element in magnetic resonance imaging (MRI) to produce high-quality images that support clinical diagnosis. This study aims to develop a medical image reconstruction method based on interpolation techniques that improves spatial accuracy and visual detail in MRI imaging results. The methodology used includes the implementation of bilinear and bicubic interpolation algorithms to process signal data obtained from MRI imaging. The dataset used in this study is brain MRI data from an open database that has been validated. The results show that the bilinear interpolation method provides higher computing speed, while bicubic interpolation produces better visual details on edges and small structures. Quantitative analysis using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics showed an improvement in the quality of the reconstruction images compared to conventional methods. In the brain dataset trial, bicubic interpolation recorded an average PSNR of 38.7 db and SSIM of 0.94, showing a significant improvement compared to the standard method. This research contributes to reducing artifacts and blurring in MRI reconstruction results, thus supporting more accurate medical decision-making. The implementation of this method also shows great potential to be applied in a variety of other clinical applications, such as soft tissue or internal organ imaging. This research is expected to be integrated with deep learning techniques to improve the efficiency and performance of medical image reconstruction in real time.
A Lightweight Interpolation Framework for Real-Time Travel Time Estimation with Incomplete Traffic Observations Syifani, Alya; Musyarofah, Musyarofah; Firdaus, Taufik Ramadhan
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.88

Abstract

Real-time travel time estimation is essential for intelligent transportation systems (ITS), yet operational traffic data streams are often incomplete due to sensor failures, communication delays, and limited coverage. This paper investigates the effectiveness of interpolation techniques for reconstructing temporally continuous travel-time profiles from real-time speed and density observations. Two approaches—linear interpolation and spline interpolation—are implemented and evaluated across varying traffic regimes (normal flow, dense traffic, and extreme congestion). Model performance is assessed using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) against reference travel-time measurements. The results show that interpolation-based methods consistently outperform a conventional baseline relying on average observed speeds, improving estimation accuracy by up to approximately 15%. Linear interpolation yields competitive performance under stable conditions, while spline interpolation achieves lower MAE and RMSE under congestion, indicating stronger robustness to nonlinear traffic dynamics. Additionally, interpolation improves service availability and estimated time of arrival (ETA) reliability with minimal computational overhead, supporting practical deployment in resource-constrained environments. These findings suggest that interpolation provides a lightweight and effective enhancement for real-time travel time estimation and can serve as a reliable preprocessing layer for advanced predictive models in future work.
Co-Authors Hidayat, Rafi Liya, Abel Musyarofah Musyarofah, Musyarofah Ningsih, Resti Syifani, Alya