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All Journal Sinergi Jurnal Listrik, Instrumentasi, dan Elektronika Terapan Journal of Embedded Systems, Security and Intelligent Systems
Rocky Alfanz, Rocky
Sultan Ageng Tirtayasa University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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A Picofarad Capacitance Meter Based on Phase-Sensitive Demodulation for Tomography Application Alfanz, Rocky; Hamzah, Hamzah; Firmansyah, Teguh; Saraswati, Irma; Ahendyarti, Ceri; Rohmadi, Rohmadi; Muttakin, Imamul
Jurnal Listrik, Instrumentasi, dan Elektronika Terapan Vol 4, No 2 (2023)
Publisher : Departemen Teknik Elektro dan Informatika Sekolah Vokasi UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/juliet.v4i2.89134

Abstract

Electrical capacitance volume tomography (ECVT) is an imaging technique based on the object’s capacitance value. To provide a representative image of the object under study, the ECVT system requires a method that can measure the capacitance value in the order of picofarads (pF). This level of resolution poses a difficulty for typical commercial capacitance measuring devices, hence raising the need for a specialized method with dedicated signal conditioning circuitry. The capacitance meter based on phase-sensitive demodulation (PSD) is made to solve the aforementioned issue and it is then compared with the characteristics of a capacitance meter-based commercial Arduino setup. The designed PSD-based capacitance measuring device has 97.894% accuracy, precision of 0.704 pF, sensitivity of 0.1197 V/pF, linearity with a coefficient of determination 0.9983, and stability of 0.028 pF/min. In comparison, the capacitance meter based on Arduino has 97.943% accuracy, precision of 0.027 pF, linearity with a coefficient of determination 0.9999, and stability of 0.04 pF/min. Testing is done on an 8-electrode ECVT sensor using dielectric materials of air and water. The nearest electrode pair on the condition of air as the dielectric medium has a capacitance value of 2.62218 pF for PSD-based measuring devices and 3.4027 pF for Arduino-based measuring devices, while the pair of electrodes on the condition of water as a dielectric medium has a capacitance value 9.8229 pF for measuring device based on PSD and 9.1069 pF for Arduino-based measuring devices. The opposite and farthest electrode pair on the condition of air as a dielectric medium has a capacitance value of 0 pF for PSD-based measuring devices and 0,0798pF for Arduino-based measuring devices, while the pair of electrodes on the condition of water as a dielectric medium has a capacitance value of 4.652 pF for PSD-based measuring devices and 0.1224 pF for Arduino-based measuring devices.
Implementation of wavelet method and backpropagation neural network on road crack detection based on image processing Alfanz, Rocky; Fahrizal, Rian; Utomo, Tegar Priyo; Firmansyah, Teguh; Muhammad, Fadil; Muztahidul, Islam Md
SINERGI Vol 28, No 3 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.3.005

Abstract

Road crack detection is critical to road infrastructure maintenance, requiring sophisticated and accurate approaches. This research explores the utilization of a combination of Wavelet and Convolutional Neural Network (CNN) methods to improve efficiency and accuracy in detecting cracks in road images. The wavelet method was chosen for its capability to capture information at different scales, enabling improved feature extraction. Meanwhile, CNN was utilized to comprehend the spatial context and tackle image complexity. The research involves several stages, including data collection, pre-processing, decomposition using the Wavelet method, forming of the CNN architecture model, training, testing, and evaluating the result. The tested images involve three main types of cracks: alligator, linear, and images without cracks. The testing results show that the developed model is capable of classifying cracks with an F1-score of 0.96, recall of 0.96, and precision of 0.96. In real-time detection of road cracks, the testing obtained an F1-score of 0.84, recall of 0.92, and precision of 0.77. This research contributes to the advancement of road crack detection technology by leveraging the capabilities of Wavelet and CNN, enhancing the accuracy and efficiency of crack detection in road maintenance.
Vehicle Detection Counting using YOLO and DeepSORT on Edge Device Rafli; Wardoyo, Siswo; Alfanz, Rocky; Fahrizal, Rian; Muhammad, Fadil; Muttakin, Imamul
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9482

Abstract

Vehicle counting is a crucial method used in traffic management. Computer vision can be employed for efficient detection and classification techniques for vehicle objects. This paper reports on a simultaneous process of vehicle classification and counting implemented on NVIDIA Jetson Nano. The use of YOLOv5 overcomes computational load issues in edge computing deployments, whereas its combination with the DeepSORT tracker algorithm enhances the accuracy of vehicle detection and counting in various directions. A total of 18200 images are used to train the detectors that are designed to target local vehicles. The average accuracy of the model for detecting cars, motorcycles, buses, and trucks is 72.1%, 21.56%, 70%, and 25.63%, respectively. Real-time tests obtained an overall average vehicle counting accuracy of 49.95%.
Co-Authors Ahendyarti, Ceri Hamzah Hamzah Imamul Muttakin Irma Saraswati Muhammad, Fadil Muztahidul, Islam Md Rafli Rian Fahrizal Rohmadi Rohmadi Siswo Wardoyo Teguh Firmansyah Utomo, Tegar Priyo