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All Journal Telematika JUITA : Jurnal Informatika ICON-CSE Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Software Engineering and Computational Intelligence Jurnal Publikasi Teknik Informatika

Rudi Heriansyah

Universiti Kuala Lumpur

Author-ID : 2860574

Computer Science & IT Education Engineering

Published : 8 Documents Claim Missing Document

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Articles

Real-time Vehicle Surveillance System Based on Image Processing and Short Message Service Agustinus Deddy Arief Wibowo; Rudi Heriansyah
JUITA : Jurnal Informatika JUITA Vol. 9 No. 2, November 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

This paper proposes a real-time vehicle surveillance system based on image processing approach tailored with short message service. A background subtraction, color balancing, chain code based shape detection, and blob filtering are used to detect suspicious moving human around the parked vehicle. Once detected, the developed system will generate a warning notification to the owner by sending a short message to his mobile phone. The current frame of video image will also be stored and be sent to the owner e-mail for further checking and investigation. Last stored image will be displayed in a centralized monitoring website, where the status of the vehicle also can be monitored at the same time. When necessary, the stored images can be used during investigation process to assist the authority to take further legal actions.

Performance Evaluation of Digital Image Processing by Using Scilab Rudi Heriansyah; Wahyu Mulyo Utomo
JUITA : Jurnal Informatika JUITA Vol. 9 No. 2, November 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

Automated Vehicle Monitoring System Agustinus Deddy Arief Wibowo; Rudi Heriansyah
ICON-CSE Vol 1, No 1 (2014)
Publisher : ICON-CSE

Show Abstract | Download Original | Original Source | Check in Google Scholar

An automated vehicle monitoring system is proposed in this paper. The surveillance system is based on image processing techniques such as background subtraction, colour balancing, chain code based shape detection, and blob. The proposed system will detect any human’s head as appeared at the side mirrors. The detected head will be tracked and recorded for further action.

IMPLEMENTASI ALGORITMA GENETIKA DALAM PENJADWALAN MATA PELAJARAN SMP (STUDI KASUS SMPN 03 PENUKAL) Sintia Laiza; Rudi Heriansyah; Dwi Aksa Verano
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 19 No 1 (2025): Mei 2025
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v19i1.4014

Student course scheduling is a complex challenge in optimizing the utilization of time and educational resources. This research aims to develop a solution for scheduling student subjects using the genetic algorithm method, with a case study at SMPN 03 Penukal. Genetic algorithm is a computational approach that uses the concept of genetic evolution to handle scheduling problems. The study involved collecting data related to class schedules, constraints, and student and teacher preferences. With 29 teachers and 3 classes divided into 9 rooms, as well as 11 subjects covering 40 lesson hours per week, scheduling is very complex. The information gathered was used as input in designing the objective function and basic rules of the genetic algorithm. The genetic evolution process is carried out to find the optimal scheduling solution that meets all the constraints and preferences that have been set. The results showed that the genetic algorithm could produce a schedule with a fitness value of -24 after 230 iterations and 100 individuals, although there were still 24 components that did not fit. The limitation of computer specifications affected this result. This research suggests modification of the fitness function and comparison with other optimization algorithms to improve the efficiency and quality of scheduling.

Similarity Identification Model of Thesis Titles with Mahalanobis Distance Approach Muhammad Fajri Munawar; Rudi Heriansyah; Muhammad Hafiz Irfani; Muhammad Ikhwan Jambak; Dwi Asa Ferano
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5413

This study aims to identify the similarity of thesis titles by applying the Mahalanobis Distance method which is known to be effective in measuring the distance between vectors by considering data distribution and correlation between variables. In its implementation, each thesis title is represented in vector form using the TF-IDF scheme before calculating the level of similarity using Mahalanobis Distance. The test results show that this method is able to produce similarity values between titles, but its performance has not shown optimal effectiveness in the context of similarity classification. The highest precision value obtained of 1.0 indicates that this method is quite reliable in identifying pairs of titles that are truly similar. However, the low recall value of only 0.5 indicates that there are many pairs of similar titles that fail to be detected, resulting in an F1-score value of only 0.638. This shows an imbalance between the system's ability to detect similarity and its classification accuracy. Although the accuracy value is relatively high, ranging from 0.958 to 0.988, these results do not necessarily reflect the overall effectiveness of the method in handling minor classification errors. Testing of the threshold parameters also shows that a value of 0.1 provides the best performance compared to other threshold values because it is able to maintain a balance between precision, recall, F1-score, and accuracy.

DETEKSI PELANGGARAN LALU LINTAS DI JALAN TOL MENGGUNAKAN FRAMEWORK YOLO DAN KALMAN FILTER Fathan Qoriba Hanif; Rudi Heriansyah; Zaid Romegar Mair
Jurnal Publikasi Teknik Informatika Vol. 4 No. 2 (2025): Mei : Jurnal Publikasi Teknik Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v4i2.5204

Traffic violations, such as exceeding the speed limit and inappropriate lane usage, are among the main factors causing accidents and congestion on toll roads. To improve traffic safety and efficiency, an automated monitoring system capable of detecting and analyzing violations quickly and accurately is needed. This research aims to develop and evaluate a detection system for speed limit violations and lane misuse by heavy vehicles using deep learning-based object recognition and tracking technology. The method used is the YOLO (You Only Look Once) framework for object detection and the Kalman Filter to track vehicle movement between frames, thereby refining position and speed estimates. The research data was obtained from CCTV video recordings installed along the toll road. The developed system is capable of detecting vehicles, calculating speed based on the shift between frames, and analyzing vehicle position in relation to lane usage regulations. The model evaluation results demonstrated quite good performance with an accuracy of 83.97%, a precision of 0.702, a recall of 0.757, and an F1 score of 0.758. The combination of YOLO and the Kalman Filter proved effective in detecting and tracking vehicles in real time, with adequate accuracy and efficient processing speed. This study concludes that a deep learning-based system can be an innovative solution to support automated traffic monitoring on toll roads. Implementing such a system has the potential to help reduce traffic violations, prevent accidents, and improve driving safety. Furthermore, this study provides recommendations for further development for integration with intelligent transportation technology to support more adaptive and sustainable traffic management.

Pengenalan Gerakan Tangan untuk Kontrol Slide Presentasi Menggunakan Framework Mediapipe, OpenCV, dan Model LSTM Muhammad Rizaldi; Rudi Heriansyah; Nazori Suhandi
Jurnal Publikasi Teknik Informatika Vol. 4 No. 3 (2025): September : Jurnal Publikasi Teknik Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v4i3.5332

In presentation activities, the use of physical devices such as a mouse or remote often limits the presenter’s mobility and reduces the effectiveness of interaction with the audience. This study aims to implement a hand gesture recognition system as an alternative solution to control presentation slides in real-time without additional devices. The system was developed using the MediaPipe framework for hand landmark detection, OpenCV for video image processing, and a Long Short-Term Memory (LSTM) model for sequential gesture classification. Three main gestures were defined as commands, namely “Next,” “Previous,” and “Idle,” with input taken from live video streaming at a distance of 1–3 meters.The development process included manual labeling of gesture data from multiple users, training the LSTM model with sequential data, and testing the system in real-time integrated with Microsoft PowerPoint. Experimental results indicate that the system successfully recognized hand gestures with high accuracy across most scenarios, with optimal performance observed at distances of 1–2 meters. However, accuracy decreased under low-light conditions or when gestures were performed too quickly.These findings demonstrate that the combination of MediaPipe, OpenCV, and LSTM is effective in building a gesture-based presentation control system. Beyond enhancing flexibility and interactivity in presentations, this research also contributes to the development of more natural and practical human–computer interaction systems, while offering opportunities for broader applications in other domains

Robustness Evaluation of YOLOv8, YOLOv11, and YOLOv12 for Personal Protective Equipment Detection under Photometric Saturation Variations Zaid Romegar Mair; Rudi Heriansyah; Septa Cahyani; M. Ravensky Taro Danayaksa
Telematika Vol 19, No 2: August (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i2.3362

Computer vision-based Personal Protective Equipment (PPE) detection has become increasingly important for improving Occupational Safety and Health (OSH) compliance through automated, real-time monitoring of workers' safety practices. Although recent studies have reported promising performance for YOLO-based object detection models, most evaluations have been conducted under standard imaging conditions, providing limited evidence of model robustness against photometric variations. To address this gap, this study proposes a systematic robustness evaluation framework based on controlled photometric saturation variations to compare the performance stability of three state-of-the-art object detection models: YOLOv8, YOLOv11, and YOLOv12, for multi-class PPE detection. The experimental dataset comprised 3,116 images annotated into eight PPE-related classes: Helmet On, No Helmet, Vest On, No Vest, Gloves On, No Gloves, Boots On, and No Boots. To ensure a fair comparison, all models were trained using identical experimental settings and evaluated using Precision, Recall, mAP@50, and mAP@50–95. Model robustness was assessed under three saturation conditions (−30%, 0%, and +30%), representing realistic color variations commonly encountered in construction-site surveillance. The experimental results revealed that photometric saturation variations produced only marginal changes in detection performance across all evaluated models. Among the three architectures, YOLOv8 achieved the highest overall performance, attaining an mAP@50 of 55.1%, compared with 52.9% for YOLOv11 and 48.6% for YOLOv12, while maintaining the most stable performance under varying saturation levels. Although YOLOv12 demonstrated relatively better capability for detecting several small-object classes, its overall detection performance remained inferior to that of YOLOv8. These findings indicate that YOLOv8 provides the best trade-off between detection accuracy and robustness under moderate photometric saturation variations. This study contributes a systematic robustness evaluation of recent YOLO architectures under controlled photometric conditions and offers practical insights for selecting reliable object detection models for real-world PPE monitoring systems.

Co-Authors Agustinus Deddy Arief Wibowo Agustinus Deddy Arief Wibowo Dwi Aksa Verano Dwi Asa Ferano Fathan Qoriba Hanif M. Ravensky Taro Danayaksa Muhammad Fajri Munawar Muhammad Hafiz Irfani Muhammad Ikhwan Jambak Muhammad Rizaldi Nazori Suhandi Septa Cahyani Sintia Laiza Wahyu Mulyo Utomo

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