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Nizirwan Anwar
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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 3,452 Documents
 268   269   270   271   272 
Real-time vehicle counting using custom YOLOv8n and DeepSORT for resource-limited edge devices Abuelgasim Saadeldin; Muhammad Mahbubur Rashid; Amir Akramin Shafie; Tahsin Fuad Hasan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 1: February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i1.25096

Abstract

Recently, there has been a significant increase in the use of deep learning and low-computing edge devices for analysis of video-based systems, particularly in the field of intelligent transportation systems (ITS). One promising application of computer vision techniques in ITS is in the development of low-computing and accurate vehicle counting systems that can be used to eliminate dependence on external cloud computing resources. This paper proposes a compact, reliable and real-time vehicle counting solution which can be deployed on low-computational requirement edge computing devices. The system makes use of a custom-built vehicle detection algorithm based on the you only look once version 8 nano (YOLOv8n), combined with a deep association metric (DeepSORT) object tracking algorithm and an efficient vehicle counting method for accurate counting of vehicles in highway scenes. The system is trained to detect, track and count four distinct vehicle classeses, namely: car, motorcycle, bus, and truck. The proposed system was able to achieve an average vehicle detection mean average precision (mAP) score of 97.5%, a vehicle counting accuracy score of 96.8% and an average speed of 19.4 frames per second (FPS), all while being deployed on a compact Nvidia Jetson Nano edge-computing device. The proposed system outperforms other previously proposed tools in terms of both accuracy and speed.
Compare between PSO and artificial bee colony optimization algorithm in detecting DoS attacks from network traffic Maha A. A. Mohammad; Muna M. T. Jawhar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.23757

Abstract

Our world today relies heavily on informatics and the internet, as computers and communications networks have increased day by day. In fact, the increase is not limited to portable devices such as smartphones and tablets, but also to home appliances such as: televisions, refrigerators, and controllers. It has made them more vulnerable to electronic attacks. The denial of service (DoS) attack is one of the most common attacks that affect the provision of services and commercial sites over the internet. As a result, we decided in this paper to create a smart model that depends on the swarm algorithms to detect the attack of denial of service in internet networks, because the intelligence algorithms have flexibility, elegance and adaptation to different situations. The particle swarm algorithm and the bee colony algorithm were used to detect the packets that had been exposed to the DoS attack, and a comparison was made between the two algorithms to see which of them can accurately characterize the DoS attack.
Layered virtual-leader DMPC with SQP for scalable V formation tracking of omni-robots in cluttered maps Tuan Phu Duong; Vinh Quang Nguyen; Minh Tuan Nguyen
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27589

Abstract

This paper proposes a hierarchical virtual leader based distributed model predictive control (DMPC) framework for V-formation control of omnidirectional mobile robots in static obstacle environments. The physical leader follows a predesigned reference trajectory, while the followers maintain the desired formation through distributed optimization. A layered communication topology is established, where only a subset of robots receives the leader’s predicted states and acts as virtual leaders for downstream followers. Each robot independently optimizes its control sequence using local neighbor information, enabling fully distributed coordination without centralized synchronization. The unified cost function considers formation maintenance, leader or virtual leader tracking, obstacle avoidance, and control effort. Static obstacles are represented on a grid map to ensure collision-free motion. Simulation results demonstrate that the proposed framework achieves accurate formation keeping, smooth trajectory tracking, and effective obstacle avoidance. The hierarchical virtual-leader architecture enhances scalability, coordination efficiency, and robustness for multi-robot formation systems.
Modified electro-optical modulator based bipolar optical code division multiple access for free-space optics Eddy Wijanto; Kevin Sutanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 4: August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i4.26727

Abstract

This study introduces a modified electro-optical modulator (EOM) to enhance bipolar optical code division multiple access (OCDMA) in free-space optical (FSO) communication. The proposed system improves signal quality, spectral efficiency, and resilience to atmospheric turbulence. Unlike conventional dual EOM techniques, the modified EOM enables simultaneous transmission of ‘0’ and ‘1’ chip values, reducing multiple access interference (MAI) and enhancing system robustness. This approach optimizes bandwidth utilization and ensures stable performance in varying environmental conditions. Simulations were conducted in an additive white Gaussian noise (AWGN) channel using three spectral amplitude coding (SAC) schemes: modified M sequence, Walsh-Hadamard, and random diagonal (RD) codes. Results indicate that the modified EOM scheme significantly improves FSO system performance, achieving an average 47.1% lower minimum log of bit error rate (BER) in normal weather and 43.3% lower in extreme weather, ensuring superior noise suppression and signal integrity across varying environmental conditions. Additionally, the system maintains superior performance over longer distances, demonstrating its suitability for high-speed, long-range FSO applications. These findings highlight the potential of the modified EOM based bipolar OCDMA system in advancing next-generation optical wireless networks, offering a more efficient, interference-resistant, and high-capacity communication solution for future technologies such as 6G and beyond.
Controlling a knee CPM machine using PID and iterative learning control algorithm Dechrit Maneetham; Petrus Sutyasadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14876

Abstract

A conventional continuous passive motion (CPM) machine uses simple controller such as On/Off controller. Some better CPMs use PID controller. These kind of CPMs can not distinguish load different due to the different size of the patient leg. This may cause the CPM no longer follow the trajectory or the angle commands. Meanwhile, each patient may have different scenario of therapy from the others. When progress on the patient exists, the range of the flexion may be increased step by step. Therefore, the treatment can be different in term of the range of flexion from time to time. This paper proposes CPM with hybrid proportional integral derivative (PID) and iterative learning controller (ILC). The system has capability in learning the trajectory tracking. Therefore, the CPM will be able to follow any load or trajectory changes applied to it. The more accurate CPM machine can follow the trajectory command, the better its performance for the treatment. The experiment showed that the system was stable due to the PID controller. The tracking performance also improved with the ILC even there exist some disturbances.
Model predictive control combined reinforcement learning for automatic vehicles applied in intelligent transportation system Vo Thanh Ha; Chu Thi Thuy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25274

Abstract

This paper presents the design of model predictive control (MPC) combined reinforcement learning (RL) applied in an intelligent transportation system (ITS). The car is to follow the reference path by the MPC control, and its parks in the parking by RL has been trained. The MPC controller constantly moves the vehicle along the reference path while the MPC algorithm searches for an empty parking spot. Meanwhile, the reinforcement learning-proximal policy optimization (RL-PPO) control will perform parking on demand if the MPC finds a parking position. This hybrid controller can quickly implement programming on MATLAB software by writing code. Furthermore, this hybrid controller simultaneously performs precise detection and avoidance of obstacles in tight parking spaces. The correctness of the theory is demonstrated through MATLAB/Simulink.
A dual-band modified-rectangular patch with parasitic antenna for 2.4/5 GHz wireless local area network applications Suthasinee Lamultree; Sakolkorn Ungprasutr; Charinsak Saetiaw; Chuwong Phongcharoenpanich
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.27055

Abstract

This research presents the design and implementation of a dual-band patch antenna (DBPA) optimized for 2.4 GHz and 5 GHz wireless local area network (WLAN) applications. The antenna features a modified rectangular patch with a cut corner and two parasitic rectangular patches, enabling dual-band operation with enhanced gain. The DBPA is fed by a 50-Ohm coplanar waveguide and fabricated on a single-layer copper circuit board using a flame-retardant 4 substrate with a relative permittivity of 4.3 and a thickness of 1.6 mm. A prototype with compact dimensions of 0.040×0.040×0.0009 λ³ was constructed and experimentally evaluated. Measurements reveal a nearly omnidirectional radiation pattern, achieving peak gains of 2.92 dBi at 2.4 GHz and 4.25 dBi at 5 GHz. The antenna demonstrates a wide 10 dB return loss bandwidth of 67.7% (1.7–3.44 GHz) for the lower band and 56% (4.59–8.16 GHz) for the upper band. The strong agreement between simulated and measured results validates the design’s potential for practical and scalable implementation. This DBPA design offers a simpler, more compact, and wider-bandwidth alternative to conventional antennas, making it ideal for modern WLAN systems.
Wind turbine doubly-fed induction generator defects diagnosis under voltage dips Sara Kharoubi; Lamiaa El Menzhi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.23760

Abstract

Voltage dips are brief but frequent disturbances that can be defined as the most important problem affecting all electrical grids, it cannot be completely avoided and represent the main priority for the industry. This study remains part of monitoring the quality of electrical energy’s field. Its aim is to apply two approaches signals for improving the failure recognition in wind turbine conversion chain. One is based on the combination of spectral analysis of stator current by fast Fourier transform and Lissajous curves technique of grid currents. The second approach is based on the stator current analysis by discrete wavelet transform which is considered an ideal tool for this purpose due to its time-frequency localization. These defects diagnosis methods have shown their efficiency in detection, visualization and analysis of voltage dips in order to develop a control strategy for a wind turbine requiring continuity of its service despite the presence of voltage dips.
Mapping research trends on tropical cyclone–induced flood susceptibility: a bibliometric and systematic review method Soenardi Soenardi; Bambang Dwi Dasanto; Yonny Koesmaryono; I Putu Santikayasa; Giarno Giarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27614

Abstract

Climate change has intensified tropical cyclones (TC), increasing extreme rainfall and flood hazards in many regions. Flood susceptibility (FS) mapping is therefore essential for understanding flood risk. This study analyzes global research trends on TC-induced FS by integrating bibliometric analysis and a preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based systematic literature review (SLR) using Google Scholar (GS) publications from 2014 to 2024. A total of 993 journal articles were analyzed, yielding an h-index of 101 and a g-index of 168, indicating strong and growing research interest. The results reveal an increasing application of machine learning (ML), deep learning (DL), remote sensing (RS), and geographic information systems (GIS) for FS mapping. Several gaps remain, including limited use of high-resolution data, underrepresentation of data-scarce and equatorial regions, restricted integration of hybrid models, and a lack of long-term assessments considering climate change and socio-economic factors. The model’s performance is also highly dependent on data quality and regional characteristics, limiting its generalizability across different conditions. The main contribution of this study is the knowledge mapping and synthesis of TC-induced FS research, providing a structured foundation for future studies and supporting evidence-based flood risk management and climate adaptation.
Identification of working memory status in children from EEG signal features using discrete wavelet transform Muhammad Hilmi Khairul Azlan; Wahidah Mansor; Ahmad Ihsan Mohd Yassin; Nabila Ameera Zainal Abidin; Mirsa Nurfarhan Mohd Azhan; Aisyah Hartini Jahidin; Muhammad Fakharul Radzy Mohd Rozlan; Zulkifli Mahmoodin; Megat Syahirul Amin Megat Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25551

Abstract

The conventional method for assessing the working memory performance of children is time-consuming and potentially inaccurate, especially when dealing with many samples. Therefore, an automated system that can produce swift and accurate results is required. Electroencephalograms (EEG) can be used to analyse the working memory status of children by extracting specific features from the EEG signal, which can be incorporated into an automatic system to reduce manpower and processing time for analysis. This project used EEG recording to identify children’s working memory status while they were performing working memory tasks. EEG signals were acquired from both children and adults using an automated computer-based working memory assessment tool, processed, and analyzed. The discrete wavelet transform (DWT) was then employed to identify five distinct working memory statuses: distracted, confused, daydreaming, losing focus, and active. DWT was also used to extract features that demonstrate these various statuses. The results showed that DWT could accurately identify the working memory status of both children and adults from their EEGs. This work has thus provided a more efficient method for extracting features from EEG signals to identify working memory statuses in both children and adults.

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