cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nizirwan Anwar
Contact Email
nizirwan.anwar@esaunggul.ac.id
Phone
-
Journal Mail Official
telkomnika@ee.uad.ac.id
Editorial Address
Ahmad Yani st. (Southern Ring Road), Tamanan, Banguntapan, Bantul, Yogyakarta 55191, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 336   337   338   339   340 
Leveraging artificial intelligence for detection of denial-of service attacks in 5G network environments Baseel Al-Ali; Mina Malekzadeh
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.v24i3.27402

Abstract

This research introduces an evaluation methodology that addresses the data leakage problem for detecting denial-of-service attacks in fifth-generation (5G) network slicing environments, and applies it to perform a benchmark comparison among twelve machine learning (ML), deep learning (DL), and probabilistic models using a publicly available 5G network slicing dataset for DoS/DDoS attacks. This methodology strictly enforces the execution of all preprocessing steps exclusively on the training data, where feature selection is performed using the mutual information (MI) metric, values are standardised via the z-score method, and synthetic samples are produced through the synthetic minority oversampling technique (SMOTE) technique on the training set only, with MI recalculated independently within each cross-validation (CV) cycle. Nine features out of eighty-four were retained at the elbow point where MI reached 0.51 or above. On the held-out test set containing approximately eighty percent benign data and twenty percent attack data, the convolutional neural network (CNN) model achieved the highest F1 value of 0.983 with a false discovery rate of 0.027, while the random forest model reached an F1 value of 0.968 at a considerably lower computational cost. All results remain tied to this particular dataset, and their generalisability to real-world 5G network traffic has not yet been validated.
Leveraging of recurrent neural networks architectures and SMOTE for dyslexia prediction optimization in children Yuri Pamungkas; Muhammad Rifqi Nur Ramadani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26092

Abstract

Learning disorders in children (dyslexia) have become a severe problem that needs attention. If this is not immediately detected and treated early on, bad habits due to dyslexia will carry over into adulthood. Many studies have been carried out on developing analytical methods for detecting/predicting dyslexia, both conventionally and based on machine learning. However, many prediction systems that have been proposed previously still do not focus on solving the problem of inequality in the data classes of people with dyslexia and ordinary people who are used in the training and testing process. Therefore, we are trying to build a system using RNN architectures that can quickly and accurately predict the possibility of a child having dyslexia. To overcome the data imbalance between dyslexics and non-dyslexics, we also apply the SMOTE method to the dataset. SMOTE will synthesize dyslexic data to balance the numbers with non-dyslexic data. This study used a dataset of 3640 participants (392 dyslexic and 3248 non-dyslexics). For the process of predicting dyslexia, several algorithms such as Simple RNN, LSTM, and GRU are used. As a result, there is an increase in prediction accuracy when SMOTE is applied (compared to without SMOTE) in the dyslexia forecasting process using RNN (92.68% for training and 91.16% for testing), LSTM (94.81% for training and 93.16% for testing), and GRU (96.43 % for training and 92.24% for testing). Using SMOTE+RNN architecture in this research increased the accuracy of dyslexia prediction by up to 5% compared to without SMOTE.
Comparison analysis of Bangla news articles classification using support vector machine and logistic regression Md Gulzar Hussain; Babe Sultana; Mahmuda Rahman; Md Rashidul Hasan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.23416

Abstract

In the information age, Bangla news articles on the internet are fast-growing. For organizing, every news site has a particular structure and categorization. News article classification is a method to determine a document’s classification based on various predefined categories. This research discusses the classification of Bangla news articles on the online platform and tries to make constructive comparison using several classification algorithms. For Bangla news articles classification, term frequencyinverse document frequency (TF-IDF) weighting and count vectorizer have been used as a feature extraction process, and two common classifiers named support vector machine (SVM) and logistic regression (LR) employed for classifying the documents. It is clear that the accuracy of the experimental results by applying SVM is 84.0% and LR is 81.0% for twelve categories of news articles. In this research work, when we have made comparison two renowned classification algorithms applied on the Bangla news articles, LR was outperformed by SVM.
Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification Radi Radi; Joko Purwo Leksono Yuroto Putro; Muhammad Danu Adhityamurti; Barokah Barokah; Luthfi Fadillah Zamzami; Andi Setiawan
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.v20i5.22358

Abstract

Electronic nose (e-nose) has been developed and implemented in a wide area, included in food industries. This study was conducted to investigate the performance of an e-nose that utilizes a packed gas chromatography column and a gas sensor for classification of synthetic flavor products. There were six aroma variants of synthetic flavor evaluated, namely durian, jackfruit, ambonese banana, melon, orange and lemon. The e-nose was designed with four main parts, namely aroma provider, column and detector room, microcontroller, and data acquisition system. The device was operated automatically at a stable temperature of 60 °C. Collected data consisted of ten data of each sample was preprocessed by baseline equalization and normalization, extracted its distinctive feature and then were analyzed through pattern recognition analysis. There were two kinds of methods used to analyzed the patterns of the data, namely a fuzzy c-means clustering and an artificial neural network (ANN). With the fuzzy c-means clustering, the result was six data clusters with an unbalanced number of members, indicated that this analysis could not classify samples properly. Meanwhile, analysis with the ANN could classify properly the samples with the level of accuracy of 70%.
Machine learning and deep learning for ransomware detection via feature decontamination Sriyanto Sriyanto; Chairani Fauzi; Mohd Faizal Abdollah; Zuriati Zuriati
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.v24i3.27833

Abstract

The continuous escalation of ransomware attacks poses a severe risk to network infrastructure and data integrity, highlighting the urgent requirement for dependable detection systems. This paper presents a comparative analysis of deep learning (DL) and machine learning (ML) techniques for identifying ransomware traffic using the UNSW-NB15 dataset. A significant obstacle in many intrusion detection investigations is feature contamination, where specific attributes inadvertently leak label data or reflect post-incident statistics, resulting in inflated and overly optimistic performance evaluations. To mitigate this concern, a feature decontamination protocol is implemented to isolate 29 reliable attributes, followed by the application of the synthetic minority over-sampling technique (SMOTE) to address the issue of class imbalance. Empirical results demonstrate that the random forest (RF) model achieves superior performance, reaching an accuracy of 0.9027 and a recall of 0.9507. Among the DL candidates, the multi-layer perceptron (MLP) delivers the most competitive outcomes with an accuracy of 0.8859 and an F1-score of 0.8996. These results suggest that ensemble-based ML frameworks offer more effective and computationally efficient ransomware detection when applied to decontaminated tabular datasets.
Optimation of image encryption using fractal Tromino and polynomial Chebyshev based on chaotic matrix Elkaf Rahmawan Pramudya; Moch. Arief Soeleman; Cahaya Jatmoko; Eko Hari Rachmawanto; Aris Marjuni; Pulung Nurtantio Andono; Folasade Olubusola Isinkaye
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26080

Abstract

Image encryption is a critical process aimed at securing digital images, safeguarding them from unauthorized access, tampering, or viewing to ensure the confidentiality and integrity of sensitive visual information. In this research, we integrate polynomial Chebyshev, fractal Tromino, and substitution S-box methods into a comprehensive image encryption approach. Our evaluation focuses on standardized 256×256-pixel images of Lena, Peppers, and Baboon, assessing key performance metrics like mean squared error (MSE), peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), number of pixel changes rate (NPCR), and entropy. The results reveal varying encryption quality across images, with Lena exhibiting the highest MSE (4702) and the lowest PSNR (12.89 dB). However, UACI, NPCR, and entropy values remain consistent across all images, indicating the proposed method’s stability concerning changing intensity, pixel alterations, and entropy levels. These findings contribute valuable insights into the effectiveness of the proposed encryption method, providing a foundation for further exploration and optimization in the field of cryptographic research. For future research direction, it is recommended to explore the impact of varying image sizes and types on the proposed method’s performance. Additionally, by focusing on the area of cryptographic threats, further analysis of the algorithm’s resistance against advanced attacks and its computational efficiency would be beneficial.
Design and performance analysis of low phase noise LC-voltage controlled oscillator Ramchandra Gurjar; Deepak Kumar Mishra
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.v21i4.22341

Abstract

Voltage controlled oscillator (VCO) offers the radio frequency (RF) system designer a freedom to select the required frequency. Today’s wireless communication system imposes a very stringent requirement in terms of phase noise generated in VCO. This study presents an inductive source degeneration technique to improve the phase noise performance of the inductance-capacitance (LC)-VCO. Double cross-coupled topology has been chosen for the proposed VCO. The post layout simulations with the parasitic resistance, inductance, capacitance (RLC) extracted view is carried out with united microelectronics corporations (UMC) 0.18 µm process by spectre simulator of cadence tools. The proposed VCO provides a phase noise of -124.3 dBc/Hz @ 1 MHz. The tuning range obtained is 19.87% with a centre frequency of 2.46 GHz which makes it suitable for industrial, scientific, and medical (ISM) band applications. It consumes a power of 2.10 mW. Also, a good figure of merit of -189 is achieved. The total layout area occupied is 477×545 µm2.
Exploiting performance gap among two users in reconfigurable intelligent surfaces-aided wireless systems Dinh-Thuan Do; Chi-Bao Le
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.v20i1.19001

Abstract

In this work, we study the outage performance of a reconfigurable intelligent surfaces (RIS)-aided wireless systems in the presence of non-orthogonal multiple access (NOMA). In particular, different power factors are allocated to users which belong a dedicated group. We derive exact outage probability of two users in a group. Specifically, it is assumed that the RIS is placed between the source and the users and far user has better performance under assistance of RIS. We also provide comparison analysis to investigate the effect of the main parameters on the outage performance of our proposed system, such as the number of tunable elements of the RIS, power allocation factors, target rates and the average signal-to-noise ratio at the base station. If we set small tunable elements for RIS, we can obtain the best performance. By using MonteCarlo simulation, we verify our analytical results via simulations. Our main results reported in this paper show the positive effect once we deploy RISs for guaranteeing fairness among NOMA users in wireless systems.
Watermarking on spread multi-frame data video using discrete wavelet transform hybrid and frame ratio message variance Ilham Firman Ashari; Sarwono Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27211

Abstract

The exponential growth of video sharing demands secure and imperceptible watermarking methods. This study presents a video watermarking framework using discrete wavelet transform (DWT) with hybrid sub-band embedding and multi-frame allocation to balance imperceptibility, capacity, and robustness. Watermark bits are adaptively distributed across low-low (LL), low-high (LH), and high-low (HL) sub-bands of selected frames, with uncompressed audio video interleave (AVI) ensuring coefficient integrity. Experiments on 640×360 videos show LL-only embedding achieves high imperceptibility (peak signal-to-noise ratio (PSNR) > 38.6 dB, structural similarity index measure (SSIM) ≥ 0.9945, and bit error rate (BER) = 0), while LL-dominant hybrids increase capacity with slight robustness trade-offs. Embedding in LH and high-high (HH) sub-bands raises distortion vulnerability. Under cropping, BER rises from 0.005 to 0.205 (0–50%), and normalized correlation (NC) drops from 0.998 to 0.802, remaining acceptable for ≤30% cropping. The scheme resists joint photographic experts’ group (JPEG) compression quality factor 20–80 (Q20–Q80), resizing (≥70%), and mild Gaussian blur (3×3), maintaining efficient decoding under higher payloads. Future work may apply error-correction coding and redundancy-aware embedding for improved resilience. Overall, the proposed method offers a secure, adaptive, and efficient solution for video authentication and covert communication.
Design and implementation of a cryptographic algorithm based on the AES advanced encryption standard for UHF RFID systems Sanae Habibi; Zahra Sahel; Abdelhak Bendali; Abid Reda El Wardi; Samia Zarrik; Mouad El Kobbi; Nazha Cherkaoui; Abdelkader Hadjoudja
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25520

Abstract

In this paper, a proposal is made for a cryptographic algorithm designed for passive ultra-high-frequency (UHF) radio frequency identification systems. The algorithm relies on the advanced encryption standard (AES) as its fundamental encryption technique, augmented by two supplementary steps: the initial step involves generating a random key and the second is the randomization of data, this introduces an extra level of security to encryption process against attacks. The developed architecture has been optimized to minimize hardware resource consumption with faster execution speed. The algorithm has been simulated, synthesized and implemented in an xtreme digital signal processing (DSP) starter kit equipped with xilinx’s spartan-3A DSP 1800A edition and it serves the purpose of encrypting and decrypting user data on a radio frequency identification (RFID) passive tag. The main objective is to make it difficult to break the algorithm because of its multiple steps. The experimental results showed that the speed, functionality and cost of encryption and decryption make this a perfectly practical solution, providing a satisfactory level of security for today’s communications systems, or other electronic data transfer processes where security is required.

Page 338 of 346 | Total Record : 3452

 336   337   338   339   340 

Filter by Year

2004 2026


Reset
Filter By Issues
All Issue Vol 24, No 4: August 2026 Vol 24, No 3: June 2026 Vol 24, No 2: April 2026 Vol 24, No 1: February 2026 Vol 23, No 6: December 2025 Vol 23, No 5: October 2025 Vol 23, No 4: August 2025 Vol 23, No 3: June 2025 Vol 23, No 2: April 2025 Vol 23, No 1: February 2025 Vol 22, No 6: December 2024 Vol 22, No 5: October 2024 Vol 22, No 4: August 2024 Vol 22, No 3: June 2024 Vol 22, No 2: April 2024 Vol 22, No 1: February 2024 Vol 21, No 6: December 2023 Vol 21, No 5: October 2023 Vol 21, No 4: August 2023 Vol 21, No 3: June 2023 Vol 21, No 2: April 2023 Vol 21, No 1: February 2023 Vol 20, No 6: December 2022 Vol 20, No 5: October 2022 Vol 20, No 4: August 2022 Vol 20, No 3: June 2022 Vol 20, No 2: April 2022 Vol 20, No 1: February 2022 Vol 19, No 6: December 2021 Vol 19, No 5: October 2021 Vol 19, No 4: August 2021 Vol 19, No 3: June 2021 Vol 19, No 2: April 2021 Vol 19, No 1: February 2021 Vol 18, No 6: December 2020 Vol 18, No 5: October 2020 Vol 18, No 4: August 2020 Vol 18, No 3: June 2020 Vol 18, No 2: April 2020 Vol 18, No 1: February 2020 Vol 17, No 6: December 2019 Vol 17, No 5: October 2019 Vol 17, No 4: August 2019 Vol 17, No 3: June 2019 Vol 17, No 2: April 2019 Vol 17, No 1: February 2019 Vol 16, No 6: December 2018 Vol 16, No 5: October 2018 Vol 16, No 4: August 2018 Vol 16, No 3: June 2018 Vol 16, No 2: April 2018 Vol 16, No 1: February 2018 Vol 15, No 4: December 2017 Vol 15, No 3: September 2017 Vol 15, No 2: June 2017 Vol 15, No 1: March 2017 Vol 14, No 4: December 2016 Vol 14, No 3: September 2016 Vol 14, No 2: June 2016 Vol 14, No 1: March 2016 Vol 13, No 4: December 2015 Vol 13, No 3: September 2015 Vol 13, No 2: June 2015 Vol 13, No 1: March 2015 Vol 12, No 4: December 2014 Vol 12, No 3: September 2014 Vol 12, No 2: June 2014 Vol 12, No 1: March 2014 Vol 11, No 4: December 2013 Vol 11, No 3: September 2013 Vol 11, No 2: June 2013 Vol 11, No 1: March 2013 Vol 10, No 4: December 2012 Vol 10, No 3: September 2012 Vol 10, No 2: June 2012 Vol 10, No 1: March 2012 Vol 9, No 3: December 2011 Vol 9, No 2: August 2011 Vol 9, No 1: April 2011 Vol 8, No 3: December 2010 Vol 8, No 2: August 2010 Vol 8, No 1: April 2010 Vol 7, No 3: December 2009 Vol 7, No 2: August 2009 Vol 7, No 1: April 2009 Vol 6, No 3: December 2008 Vol 6, No 2: August 2008 Vol 6, No 1: April 2008 Vol 5, No 3: December 2007 Vol 5, No 2: August 2007 Vol 5, No 1: April 2007 Vol 4, No 3: December 2006 Vol 4, No 2: August 2006 Vol 4, No 1: April 2006 Vol 3, No 3: December 2005 Vol 3, No 2: August 2005 Vol 3, No 1: April 2005 Vol 2, No 1: April 2004 More Issue