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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
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A convolution neural network model for knee osteoporosis classification using X-ray images Omar Khalid M. Ali; Abeer K. Ibrahim; Bilal R. Altamer
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.v23i4.26778

Abstract

Bone structure deterioration along with low levels of bone density are the hallmarks of knee osteoporosis (KOP). The conventional approach for detecting osteoporosis is accomplished using a knee radiograph, but it requires specialized knowledge. Nevertheless, X-rays can be difficult to interpret due to their large volume and minor fluctuations. In the past few decades, deep learning algorithms have minimized misinterpretation and modified medical diagnosis. In particular, algorithms based on convolutional neural networks (CNNs) have been used to speed up the procedure of diagnosis because of their innate capacity to extract significant features that often are challenging to spot by hand. A robust CNN model was proposed in this paper for KOP classification which uses a train and test approach to recognize healthy, osteopenia-predicted, and osteoporosis knee cases using 1947 X-ray images. The proposed model was designed using Jupyter Notebook and is in Python. To verify the efficiency of the model, some factors were calculated such as accuracy, precision, recall, and f1-score. In comparison with other similar systems, the results obtained showed that the accuracy of the proposed system reached 90.25%.
Adaptive threshold for moving objects detection using gaussian mixture model Moch Arief Soeleman; Aris Nurhindarto; Muslih Muslih; Karis W.; Muljono Muljono; Farikh Al Zami; R. Anggi Pramunendar
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.v18i2.14878

Abstract

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
Optimization of support vector machine with cubic kernel function to detect cyberbullying in social networks Al-Khowarizmi Al-Khowarizmi; Indah Purnama Sari; Halim Maulana
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.v22i2.25437

Abstract

Social networking is a place where humans can interact using the internet network to be able to disseminate information, discuss, exchange ideas, pour out their hearts, and share activities. Many social networks are popularly used, one of which is Twitter. Information can be received quickly using Twitter. In addition, various government agencies also use Twitter to be able to interact directly with the community so that every government policy is disseminated through this social network. Every government policy neglects to reap the pros and cons of society, both collectively and individually. As a result of the pros and cons, a trial called cyberbullying was recorded. Cyberbullying in various studies has been carried out to change a person’s raw material so that with the application of information technology, identifying cyberbullying needs to be carried out further. The problem of cyberbullying is generally detected using the support vector machine (SVM) method. Cyberbullying detection is conducted in dealing with government policy data such as “cipta kerja” by using the SVM method which is optimized using the cubic kernel function. The accuracy value achieved in SVM uses a linear kernel function of 92.3% while using a cubic linear function of 90%.
Improved channel quality indicator estimation using extended Kalman filter in LTE networks under diverse mobility models Hilary U. Ezea; Mamilus A. Ahaneku; Vincent C. Chijindu; Obinna Ezeja; Udora N. Nwawelu
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.v23i5.27205

Abstract

Accurate channel quality indicator (CQI) estimation is crucial for optimizing resource allocation, improving link adaptation, and sustaining high performance in long term evolution (LTE) networks. In real-world scenarios, where channel conditions fluctuate rapidly due to user mobility, inaccurate CQI estimation can lead to suboptimal scheduling, degraded throughput, and reduced quality of service (QoS) for both users and network operators. Traditional Kalman filter (KF) approaches often struggle with the non-linear and time-varying nature of wireless channels, especially under unpredictable mobility patterns. This paper proposes an improved CQI estimation method based on the extended Kalman filter (EKF), which models non-linear system dynamics more effectively. The method is implemented in LTE-Sim, analyzed using MATLAB, and evaluated under random and Manhattan mobility models. Results show that across mobility regimes, KF outperforms EKF in the structured Manhattan model, while in the non-linear random-direction model, EKF yields markedly higher signal-to-interference-plus-noise ratio (SINR) stability and robustness to channel variation with SINR values above 10 dB between 300-450 s and a peak of approximately 60 dB. These results underscore the importance of mobility-aware estimation strategies in enhancing LTE network adaptability and throughput.
Model design to develop online web based questionnaire Julian Wesley; Fifilia Fifilia; Cynthia Catrine; Dennis Antony; Ferdian Rizky Estefan
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.v20i5.23768

Abstract

This research aims to create a web-based application for sharing questionnaires. The developed features are creating questionnaires, sharing questionnaire in the dashboard, filter the questionnaire as the requested criterias, exchange the rewarded coins for gifts, export questionnaire data to a document, set a limit for the questionnaire for each device. The development will be using data collecting using questionnaire and literature study. Then, software development life cycle (SDLC) waterfall research methodology will be used for the website system development. Result of this research will be a website application that will be used for questionnaire maker so that they can reach the respondent count target, have a suitable respondent (minimize respondent who does not meet the criteria), and also can collect more validated data.
Classification of P300 event-related potentials using SNN, CNN and LSTM deep learning models Ahlaam M. Saed; Ibtihal Fawzi Elshami; Ali I. Elgayar
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.v24i4.27659

Abstract

Accurate classification of P300 event-related potentials remains challenging due to the complex, non-stationary, and low signal-to-noise characteristics of electroencephalography (EEG) signals in brain-computer interface (BCI) systems. P300-based devices, such as the P300 speller, enable communication for patients with severe motor impairments, including those with locked-in syndrome; however, reliable brain signal classification is still a critical limitation. This study presents a comparative evaluation of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM) networks, and spiking neural networks (SNN), for P300 signal classification. SNNs represent a biologically inspired paradigm that models the discrete, time-dependent behavior of neural spiking activity and offers advantages in terms of energy efficiency and hardware implementability. Experimental results demonstrate that CNN achieved the highest average classification accuracy (81.04%), followed closely by SNN (80.94%) and LSTM (80.60%). Although CNN slightly outperformed the other models, SNNs showed comparable accuracy while requiring fewer training samples and offering potential benefits for low power and real-time BCI systems. These findings highlight the trade-offs between classification performance and computational efficiency and underline the promise of SNNs as an efficient alternative for P300-based BCI applications.
Swarm intelligence for intrusion detection systems in internet of things environments Apri Siswanto; Akmar Efendi; Jaroji Jaroji; Fajar Ratnawati
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.v23i1.25857

Abstract

The rise of the internet of things (IoT) technology has brought new security challenges, necessitating robust intrusion detection systems (IDS). This research applies swarm intelligence (SI) principles, specifically the pigeon inspired optimization (PIO) algorithm, to enhance IDS effectiveness in IoT environments. Drawing on the behavior of social species, SI fosters decentralized control and emergent behavior from simple rules. These principles guide the PIO algorithm, making it apt for optimizing IDS. We utilize two comprehensive IoT datasets – the Canadian Institute for Cybersecurity (CIC) IoT dataset 2023 and the IoT dataset for IDS, aiming to boost the IDS’s capability to detect illicit attacks. By adapting the PIO algorithm, our IDS learns from the environment, adapts to evolving threats, and mitigates false-positive rates. Preliminary tests show that our SI-based IDS outperforms traditional systems’ accuracy, speed, and adaptability. This research advances SI applications in IoT security, contributing to developing more resilient IDS and ultimately enhancing IoT network security against a range of cyber threats.
LSKA-ID: A lightweight security and key agreement protocol based on an identity for vehicular communication Murtadha A. Alazzawi; Mohammed Tali Almalchy; Ahmed Al-Shammari; Ahmed Salih Al-Khaleefa; Hayder M. Albehadili
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 4: August 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i4.24388

Abstract

Recently, a huge effort has been pushed to the wireless broadcasting nature in the open area. However, the vehicular ad hoc network (VANET) is disposed to various kinds of attacks. Hence, keeping the security in VANET is the most critical issue because of the VANET network related to human life. Thus, we propose a robust and lightweight security and key agreement-based identity protocol LSKA-ID for vehicular communication. Our protocol utilizes the elliptic curve cryptography, Chinese reminder theorem, and identity (ID)-based cryptosystem to resolve the issues found in the previously proposed schemes, in which our protocol can resolve the key escrow issues accompanied in most ID-based schemes. Also, it does not need batch verification operations, which cause some problems to the verifier in case the batch beacons have one or more illegal beacons. Moreover, the LSKA-ID protocol addresses the dependency on the trusted authority (TA) during the high frequent handover between the groups that may cause a bottleneck problem on the TA. The security analysis proves the correctness of the LSKA-ID protocol by using the random oracle model and has shown to be effective in a performance evaluation.
Modeling and characterization of optimal nano-scale channel dimensions for fin field effect transistor based on constituent semiconductor materials Waheb A. Jabbara; Ahmed Mahmood; Jamil Sultan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.21671

Abstract

This study aims to design an optimal nano-dimensional channel of fin field effect transistor (FinFET) on the basis of electrical characteristics and constituent semiconductor materials (Si, GaAs, Ge, and InAs) to overcome issues regarding the shrinking of dimensions and ensure the best performance of FinFETs. This objective has been achieved by proposing a new scaling factor, K, to simultaneously shrink the physical scaling limits of channel dimensions for various FinFETs without degrading their performance. A simulation-based comprehensive comparative study depending on four variable parameters (length, width, oxide thickness of the channel, and scaling factor) was carried out. The influence of changing channel dimensions on the performance of each type of FinFET was evaluated according to four electrical characteristics: i) ON-state/OFF-state current (ION/IOFF) ratio, ii) subthreshold swing (SS), iii) threshold voltage, and iv) drain-induced barrier lowering. The well-known multi-gate field-effect transistor (MuGFET) simulation tool for nanoscale MuGFET structure was utilized to conduct experimental simulations under the considered conditions. The obtained simulation results showed that the optimal channel dimensions for the best performance of all considered FinFET types were achieved at a minimal scaling factor K=0.125 with 5 nm length, 2.5 nm width, and 0.625 nm oxide thickness of the channel.
A practical approach to Candi Siwa 3D reconstruction with COLMAP and Nerfstudio Helena Widiarti; Rokhmat Febrianto; Agung Alfiansyah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27212

Abstract

We demonstrate a practical approach for large-scale object three-dimensional (3D) reconstruction with freely available frameworks, COLMAP and Nerfstudio. We performed the reconstruction of a temple named Candi Siwa, located at Prambanan Site, which is situated between Central Java and Yogyakarta Province, Indonesia. We utilized COLMAP and Nerfstudio as platforms for 3D reconstruction from images captured by an everyday smartphone. In the 3D model construction process, COLMAP generates a dense point cloud, whereas Nerfstudio generates a scene from source images. We selected 96 images of Candi Siwa to perform reconstruction using COLMAP. As a result, a 3D model for the temple with a clear structure and color was observed. A scene rendered in MP4 format was also generated using Nerfstudio. Additionally, we performed the 3D reconstruction from 150 images taken by the public and found them insufficient for constructing the object. This occurred despite the number of images being larger than those used in the previous reconstruction. The results indicate that the success of a crowdsourcing project for reconstructing a large-scale object should consider not only the number of images but also the variation in point of view and the completeness of the whole structure.

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