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All Journal Sinergi VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro
Tresna Dewi
Department of Electrical Engineering, Politeknik Negeri Sriwijaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Application of fuzzy logic and social media on flood hazards early warning system Evelina Ginting; Nyanyu Latifah Husni; Tresna Dewi; Ade Silvia Handayani; Muhamad Rizki Harahap
VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Vol 3, No 1 (2018): April 2018
Publisher : Department of Electrical Engineering Education, Faculty of Teacher Training and Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (833.214 KB) | DOI: 10.30870/volt.v3i1.3294

Abstract

This research aims to reduce losses that arise because of the flood in a way create a notifier flooding as flood detection.  Sensors Ultrasonic sensors measure distance as can be applied as a water level detector, while the sensors used to detect flow velocity of the water flow. Research methods used in the form of literature reviews and making hardware and software using sensors SR04-HC. Applica-tion of fuzzy logic is used as a data processing of the ultrasonic sensor and flow sensor.  Fuzzy logic will result in a decision on environmental conditions.  With the decision made by fuzzy logic, early warning against the danger of flooding, in the form of an alarm and display the text that indicates a likelihood of flooding, as well as to social media news delivery can be carried out.
MOTION CONTROL ANALYSIS OF TWO COLLABORATIVE ARM ROBOTS IN FRUIT PACKAGING SYSTEM Tresna Dewi; Citra Anggraini; Pola Risma; Yurni Oktarina; Muslikhin Muslikhin
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.013

Abstract

As robots' use increases in every sector of human life, the demand for cheap and efficient robots has also enlarged. The use of two or more simple robot is preferable to the use of one sophisticated robot. The agriculture industry can benefit from installing a robot, from seeding to the packaging of the product. A precise analysis is required for the installation of two collaborative robots. This paper discusses the motion control analysis of two collaborative arms robots in the fruit packaging system. The study begins with the relative motion analysis between two robots, starting with kinematics modeling, image processing for object detection, and the Fuzzy Logic Controller's design to show the relationship between the robot inputs and outputs. The analysis is carried out using SCILAB, open-source software for numerical computing engineering. This paper is intended as the initial analysis of the feasibility of the real experimental system.
Benchmarking YOLOv8 and vision transformers for intelligent fish monitoring in aquaponics and controlled aquarium environments Tresna Dewi; Yurni Oktarina; Sri Rezki Artini; Gita Ayu Julianka; Jhoni Satria
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.022

Abstract

Sustainable aquaculture requires reliable and accurate fish monitoring systems capable of operating across heterogeneous environmental conditions. Conventional monitoring approaches are labor-intensive and prone to human error, while recent advances in deep learning have enabled vision-based automation for aquatic environments. Convolutional object detectors such as YOLO and emerging Vision Transformer (ViT) models have demonstrated promising performance; however, most existing studies remain limited to single-environment evaluations and rarely address energy-constrained, real-world deployment. To bridge this gap, this study presents a systematic benchmark of YOLOv8 and ViT across two complementary settings: a controlled aquarium environment and a solar-powered, off-grid aquaponics system. The proposed framework integrates 1080p CCTV video acquisition, dataset annotation and augmentation, and standardized training and evaluation using COCO metrics. Experimental results show that ViT consistently outperforms YOLOv8 in detection accuracy and prediction stability across both environments. ViT achieves 99.73% accuracy in the controlled aquarium and ≥99.6% accuracy performance (99.68–99.73%) in aquaponics, while YOLOv8 records 87.90% accuracy in the aquarium and 93.92–97.92% across aquaponics fish classes, exhibiting higher sensitivity to background clutter. Statistical validation using McNemar’s test (p < 0.001) confirms that these differences are statistically significant. Beyond accuracy, the results reveal a trade-off between robustness and computational efficiency. ViT provides superior resilience under occlusion and glare, whereas YOLOv8 offers faster inference suitable for real-time operation on resource-limited edge devices. End-to-end deployment on a solar-powered NVIDIA Jetson Xavier NX demonstrates the feasibility of continuous, off-grid aquaculture monitoring and provides practical guidance for context-aware model selection in intelligent aquaculture systems.
Co-Authors Ade Silvia Handayani Citra Anggraini Evelina, Evelina Gita Ayu Julianka Jhoni Satria Muhamad Rizki Harahap Muslikhin Nyanyu Latifah Husni Pola Risma Sri Rezki Artini Yurni Oktarina