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All Journal Publikasi Pendidikan CommIT (Communication & Information Technology) Journal of ICT Research and Applications JTERA (Jurnal Teknologi Rekayasa) DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Indonesian Journal of Electrical Engineering and Computer Science REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat International Journal of Applied Technology Research Scientific Journal of Informatics
Nugroho, Hertog
Politeknik Negeri Bandung
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences

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Detection of Empty/Occupied States of Parking Slots in Multicamera system using Mask R-CNN Classifier Nugroho, Hertog; Adi, Ginanjar Suwasono; Afandi, Muhammad Khoer
Jurnal Internasional Penelitian Teknologi Terapan Vol 4 No 1 (2023): April 2023
Publisher : Bandung State Polytechnic (Politeknik Negeri Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/ijatr.v4i1.114

Abstract

A fast growth of vehicles in big cities has an impact of arising road loads and difficulty of finding empty parking spaces. One solution to cope with the problem is to develop a parking management system which can provide useful information of available parking spaces to the potential users. This paper discusses about a new multicamera arrangement and the function to evaluate the empty/occupied states of the parking slots, as an alternative solution to the existing single camera system, The system adopted Mask R-CNN for its classifier, because of its capability to provide the polygon outputs for its detected objects, compared with the existing bounding box outputs provided by other classifiers. The proposed function has optimized the available information from all cameras, by considering the relative position of each camera to the parking spaces, and also capable of overcoming occlusion problem occurs in some cameras, The experiment shows that the capability of overcoming the occlusion problem has been validated, and its performance to evaluate the empty/occupied states of the parking slots was better than the single camera system to a certain threshold.
Fusion algorithms on identifying vacant parking spots using vision-based approach Adi, Ginanjar Suwasono; Nugroho, Hertog; Rahmatullah, Griffani Megiyanto; Fadhlan, Muhammad Yusuf; Mutamaddin, Dinan
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1640-1654

Abstract

In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7’s advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems.
Development of Segmentation Method to Localize Epileptic Symptoms in EEG Signal Praja, Reval Bima; Nugroho, Hertog; Ginanjar, Teguh
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.40414

Abstract

Purpose: Epilepsy is a chronic neurological disorder that affects more than 50 million people worldwide, where early detection through EEG signal analysis is crucial for proper management. However, the quality of EEG signals is often affected by noise and artifacts, which can lead to diagnostic errors of up to 30% in the early stages. This study aims to develop an EEG signal preprocessing method to improve the classification performance of epileptic symptoms through preprocessing, segmentation, and seizure interval analysis approaches. Methods: The preprocessing stage involved applying a 50 Hz notch filter and a 0.5–60 Hz bandpass filter. The contribution of this work is in the development of  hybrid segmentation based on frequency and amplitude analysis, while seizure intervals were identified using distances criteria between consecutive spikes detected on signals. The method was tested using the CHB-MIT dataset consisting of 23 EEG channels. Result: The results showed that the system successfully identified seizure segments with an average accuracy of 62.09%, and 9 out of 23 channels achieved accuracies above 70%. Channels Ch08 (86.60%), Ch09 (86.36%), and Ch19 (80.51%) achieved the highest accuracies. The results also showed high specificity(99.85%) and low False Positive rate(0.15%) indicating the system’s effectiveness to reduce falase positive. Novelty: This method proved effective in detecting epileptiform activity and shows potential as an EEG-based early detection tool for epilepsy, although further optimization is needed to improve accuracy on channels with low signal-to-noise ratio (SNR).
Co-Authors Adi, Ginanjar Suwasono Afandi, Muhammad Khoer Andriani Budi Setiadi, Budi Enceng Sulaiman Fadhlan, Muhammad Yusuf Ferry Satria Ferry Satria Ginanjar, Teguh Griffani Megiyanto Rahmatullah Hanafi, Usman Hanifatunnisa, Rifa HANNY MADIAWATI, HANNY Jimmy Tirtawangsa Luthfiana Adam Maldini Maya Rahayu Maya Rahayu Maya Rahayu, Maya Mina Mina Naidah Gani Muhammad Yusuf Fadhlan Mutamaddin, Dinan Nila Nila Novita Sari Pov Vandeth Praja, Reval Bima Ridwan Solihin Rimba Whidiana Ciptasari Satria, Ferry Supriyadi, Tata Sutrisno Sutrisno Taufik Irfan Usman B Hanafi Usman Hanafi Utomo, TB Vitrasia Vitrasia ‪Usman Balugu Hanafi