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All Journal Jurnal IPTEK Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Green Intelligent Systems and Applications JEEE- Journal of Electrical And Electronics Engineering Makara Journal of Technology Jurnal Elektronika dan Telekomunikasi
Rospawan, Ali
President University
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : green intelligent systems and applications

 1 
Development of Hot Air Dryer Conveyor for Automotive Tampo Printing Parts Ali Rospawan; Joni Welman Simatupang; Irwan Purnama
Green Intelligent Systems and Applications Vol. 2 Iss. 1 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (665.233 KB) | DOI: 10.53623/gisa.v2i1.69

Abstract

This paper presents the development of a hot air dryer conveyor for the automotive industry in the tampo printing part process. The research started by designing and creating the actual device that is ready to use and be implemented in the industry. The method provides the detail of the drying chamber, hot air dryer, and its mathematical model. The chosen hot air dryer is Suiden SHD-9F II and is operated on the factory default setting of auto-tuning mode. The performance evaluation studies the indicator performance of hot air dryer for the chosen size of drying chamber, the robustness of the system against fluctuating environmental air change rate, the ducting capacity, and the damper opening value estimation performance. The result of this system is working well at specification requirement of air change rate from 15 to 21 at 80% of its maximum capacity for error compensation and its already being successfully implemented.
Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems Ali Rospawan; Yukai Yang; Po-Hsu Chen; Ching-Chih Tsai
Green Intelligent Systems and Applications Vol. 2 Iss. 2 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v2i2.106

Abstract

This paper presents a case study of the setpoint tracking performance of the proportional integral derivative (PID) controller on the Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) nonlinear digital plants under Gaussian white noise and constant load disturbance for the nonlinear time-delay dynamic system. With the objective of getting a better understanding of the nonlinear discrete-time PID controller, we proposed a case study using two SISO and two MIMO digital plants, and then do the numerical simulations along with the addition of Gaussian white noise and load disturbance to simulate the real environment. In this paper, we compare the results of the system working with and without noise and load disturbance. The study result of this paper shows that on the discrete-time digital nonlinear plant, the PID controller is working well to follow the nonlinear setpoint even under heavy noise and load disturbance. The study compared the performance indexes of the controllers in terms of the maximum error, the Root mean square error (RMSE), the Integral square error (ISE), the Integral absolute error (IAE), and the Integral of time-weighted absolute error (ITAE). 
A Benchmark Study of DeepLabV3+, U-Net++, and Attention U-Net for Blood Cell Segmentation Angelina, Clara Lavita; Rospawan, Ali
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.607

Abstract

Cell segmentation is a critical process in biomedical image analysis. This study evaluated the performance of three state-of-the-art deep learning models—DeepLabV3+, U-Net++, and Attention U-Net—using the Blood Cell Count and Detection (BCCD) dataset, which contains annotated images of blood cells. The models were rigorously analyzed through qualitative and quantitative evaluations, employing accuracy, precision, recall, and F1 score metrics. The results demonstrated that all three models achieved high segmentation performance, with U-Net++ excelling in accuracy (0.9740), precision (0.9511), and F1 score (0.9576), Attention U-Net achieving the highest recall (0.9692), and DeepLabV3+ providing a balanced performance across all metrics. Qualitative analyses revealed that U-Net++ delivered superior segmentation of complex and overlapping cell structures, while Attention U-Net exhibited exceptional sensitivity to dense cell clusters. Training and validation curves of the models confirmed their stability and generalizability, indicating efficient convergence without overfitting. By highlighting the unique strengths of each model, this study emphasized the importance of selecting architectures tailored to specific tasks. Future research will expand the application of these models to diverse biomedical datasets to further advance automated image analysis and its impact on healthcare outcomes.
Co-Authors Baihaqi, Muhammad Yeza Ching-Chih Tsai Clara Lavita Angelina Halawa, Edmun Irwan Purnama Irwan Purnama Joni Welman Simatupang Munajat, Muhammad Po-Hsu Chen Samsuri, Faisal Setiyadi, Surawan Vincent Vincent, Vincent Yukai Yang