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
 288   289   290   291   292 
Retinal Image Preprocessing: Background and Noise Segmentation Ibaa Jamal; M. Usman Akram; Anam Tariq
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i3.834

Abstract

Medical imaging is very popular research area these days and includes computer aided diagnosis of different diseases by taking digital images as input. Digital retinal images are used for the screening and diagnosis of diabetic retinopathy, an eye disease. An automated system for the diagnosis of diabetic retinopathy should highlight all signs of disease present in the image and in order to improve the accuracy of the system, the retinal image quality must be improved. In this article, we present a method to improve the quality of input retinal image and we consider this method as a preprocessing step in automated diagnosis of diabetic retinopathy. The preprocessing consists of background estimation and noise removal from retinal image by applying coarse and fine segmentation. We perform extensive results to check the validity of proposed preprocessing technique using standard fundus image database.
Big data cloud-based recommendation system using NLP techniques with machine and deep learning Hoger K. Omar; Mondher Frikha; Alaa Khalil Jumaa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24889

Abstract

Recommendation systems (RS) are crucial for social networking sites. Without it, finding precise products is harder. However, existing systems lack adequate efficiency, especially with big data. This paper presents a prototype cloud-based recommendation system for processing big data. The proposed work is implemented by utilizing the matrix factorization method with three approaches. In the first approach, singular value decomposition (SVD) is used, which is an old and traditional recommendation technique. The second recommendation approach is fine-tuned using the alternating least squares (ALS) algorithm with Apache Spark. Finally, the deep neural network (DNN) algorithm is utilized with TensorFlow. This study solves the challenge of handling large-scale datasets in the collaborative filtering (CF) technique after tuning the algorithms by adjusting the parameters in the second approach, which uses machine learning, as well as in the third approach, which uses deep learning. Furthermore, the results of these two approaches outperformed conventional techniques and achieved an acceptable computational time. The dataset size is about 1.5 GB and it is collected from the Goodreads website API. Moreover, the Hadoop distributed file system (HDFS) is used as cloud storage instead of the computer’s local disk for handling larger dataset sizes in the future.
Benefits of using TiO2 quantum dots in producing low-cost and high-quality white light-emitting diodes Phuc Dang Huu; Phung Ton That; Tran Thanh Trang; Phan Xuan Le; Nguyen Doan Quoc Anh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 3: June 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i3.15347

Abstract

Quantum dots (QDs) is considered as a potential material for the improvement of light-emitting diodes (LEDs). However, different from the traditional phosphor materials, they have unique scattering and absorption properties affected by their several nanometers sizes, which makes their application in the production of LED confront more challenges. In addition to this, their influences on QDs-converted LEDs (QCLEDs) are rarely investigated. So as to propose solutions for those problems, in this article, we experimentally and theoretically investigated the impacts of titanium dioxide (TiO2)QDs’ scattering and absorption on the light quality of QCLEDs by drawing a thorough comparison between their properties and the traditional yttrium aluminum garnet phosphors characteristics. The outcomes showed that QCLEDs have poor radiant efficacy and stability due to QDs’ strong characteristic of absorption (reabsorption) while their weak scattering property causes a low uniformity in correlated color temperature (CCT). For achieving high efficiency and stability white LEDs, we highly suggest using QDs with a low concentration to get reductions in the reabsorption and total internal reflection losses. With 0.05 concentration of TiO2 nanoparticles (TiO2 NPs), the white LEDs can simultaneously achieve a high CCT (approximately 7500 K) and a high color rendering index (around 85).
Hybrid intrusion detection in IoT devices: a deep learning approach using Kitsune and quantized autoencoder Md. Rifat E Noor; Md. Tofael Ahmed; Dulal Chakraborty; Pintu Chandra Paul; Sohana Nowar; Rejwan Ahmed; Tanjina Akter
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27316

Abstract

Internet of things (IoT) has been transforming the way to connect and communicate in smart homes, healthcare, and businesses so fast and rapidly around the world. But this growth has complicated security, because IoT devices are more likely to be hacked as they’re smaller, without even regular security practices, and under attack by more sophisticated threats. Traditional intrusion detection systems (IDS) are not functioning well in IoT environments as they are computationally expensive and struggle to accommodate the heterogeneous nature of IoT networks. This paper introduces a cross-domain intrusion detection based on adaptive adversarial training using Kitsune and quantized autoencoders (QAE) for anomaly detection and classification. The model is capable of capturing different attacking techniques, such as distributed denial of service (DDoS), Mirai botnet attacks, address resolution protocol (ARP) spoofing, and data exfiltration, by leveraging the reconstruction error generated by Kitsune autoencoders. The degree-based classification enables the system to dynamically categorize anomalies according to their severity, rendering the model exceptionally adaptive to various attacks. The anomalies are also classified into different types of attacks (normal, suspicious, and malicious) based on binarized error values. The approach achieves a high accuracy with an F1 score of 85.9% and supports real-time characterization to increase security in IoT scenarios.
Rumor detection based on deep learning techniques: a systematic review Lifan Zhang; Shafaf Ibrahim; Ahmad Firdaus Ahmad Fadzil
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25929

Abstract

The rise of social media platforms has led to an increase in the flow and dissemination of information, but it has also made generating and spreading rumors easier. Rumor detection requires understanding the context and semantics of text, dealing with the evolving nature of rumors, and processing vast amounts of data in real-time. Deep learning (DL)-based techniques exhibit a higher accuracy in detecting rumors on social media compared to many traditional machine learning approaches. This study presents a systematic review of DL approaches in rumor detection, analyzing datasets, pre-processing methods, feature taxonomy, and frequently used DL methods. In the context of feature selection, we categorize features into three areas: text-based, user-based, and propagation-based. Besides, we surveyed the trends in DL models for rumor detection and classified them into convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), and other methods based on the model structure. It offers insights into effective algorithms and strategies, aiming to guide researchers, developers, social media users, and governments in detecting and preventing the spread of false information. The study contributes to enhancing research in this field and identifies potential areas for future exploration.
Big data classification based on improved parallel k-nearest neighbor Ahmed Hussein Ali; Mostafa Abduhgafoor Mohammed; Raed Abdulkareem Hasan; Maan Nawaf Abbod; Mohammed Sh. Ahmed; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.24290

Abstract

In response to the rapid growth of many sorts of information, highway data has continued to evolve in the direction of big data in terms of scale, type, and structure, exhibiting characteristics of multi-source heterogeneous data. The k-nearest neighbor (KNN) join has received a lot of interest in recent years due to its wide range of applications. Processing KNN joins is time-consuming and inefficient due to the quadratic structure of the join method. As the number of applications dealing with vast amounts of data develops, KNN joins get more sophisticated. The authors seek to save money on computer resources by leveraging a large number of threads and multiprocessors. Six popular datasets are used to apply the method and evaluate the sequential and parallel performance of the KNN technique. These datasets are used to compare the sequential and parallel performance of the KNN method. When compared to a matching multi-core solution, the final implementation saves computing resources. It has been optimized to utilize as little RAM as possible, allowing it to manage high-resolution photo data without sacrificing efficiency. The authors will use the technique they presented using Spark Radoop. Our performance research validates the supplied method’s efficacy and scalability.
A 6G THz MIMO antenna with high gain and wide bandwidth for high-speed wireless communication Redwan Al Mahmud Bin Asad Ananta; Md. Sharif Ahammed; Md. Ashraful Haque; Md. Kawsar Ahmed; Narinderjit Singh Sawaran Singh; Jamal Hossain Nirob; Kamal Hossain Nahin; Liton Chandra Paul
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26526

Abstract

This study presents a comprehensive industrial and innovation design and thorough analysis of a terahertz (THz) multiple-input multiple-output (MIMO) antenna, addressing the increasing demand for high-performance multi-antenna systems in THz communication applications. The primary objective of this research is to develop a compact and efficient MIMO antenna that operates over a wide frequency range and provides high isolation, specifically within the 1–10 THz spectrum. The proposed antenna achieves an impressive total bandwidth of approximately 9 THz, featuring seven distinct resonance frequencies at 1.39 THz, 3.26 THz, 4.72 THz, 5.96 THz, 7.07 THz, 8.194 THz, and 9.426 THz. The design employs a polyimide substrate and a graphene patch. Key performance metrics include a maximum gain of 15 dB, efficiency of 99.8%, and isolation values that range from 28 dB to 63 dB. An resistor inductor capacitor (RLC) equivalent circuit using advanced design system (ADS) software. Additionally, the antenna displays remarkable diversity metrics, with an envelope correlation coefficient (ECC) of 0.000778 and a diversity gain of 9.99961 dB. With compact dimensions of (65×180) µm2 and outstanding performance characteristics, this design is confirmed to be suitable for THz applications, fulfilling the research goal of facilitating efficient and reliable communication in sophisticated multi-antenna systems.
An optimal control for complete synchronization of 4D Rabinovich hyperchaotic systems Shaymaa Y. Al-Hayali; Saad Fawzi Al-Azzawi
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.v18i2.14301

Abstract

This paper derives new results for the complete synchronization of 4D identical Rabinovich hyperchaotic systems by using two strategies: active and nonlinear control. Nonlinear control strategy is considered as one of the powerful tool for controlling the dynamical systems. The stabilization results of error dynamics systems are established based on Lyapunov second method. Control is designed via the relevant variables of drive and response systems. In comparison with previous strategies, the current controller (nonlinear control) focuses on convergence speed and the minimum limits of relevant variables. Better performance is to achieve full synchronization by designing the control with fewer terms. The proposed control has certain significance for reducing the time and complexity for strategy implementation.
Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm Dimas Aryo Anggoro; Assyati Amadjida Tamimi Marzuki; Wiwit Supriyanti
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.v22i1.24598

Abstract

The ideology of the Solo Batik pattern has not been conveyed to the public. In addition, a lot of people are unaware that batik contains particular patterns that are also used for particular activities. This study uses a convolutional neural network model to categorize 9 different Solo Batik patterns according to their use of elaborate geometric shapes, complicated symbols, patterns, dots, and natural designs. With 1 to 4 hidden layers, we aim to select the number of hidden layers that yields the highest accuracy. A 100×100 pixel image is used as the input. The feature extraction process then makes use of 3×3 feature maps from three convolution layers. The dropout regularization is then added, with settings ranging from 0.1 to 0.9. The Adam algorithm is also used in this model to perform optimization. The 3-layered convolutional neural networks (CNN) with a dropout value of 0.2, run in 20 epochs, produced accuracy results of 97.77%, which was the highest. Additionally, it can be inferred that applying a certain number of hidden layers and adding right dropout regularization values has an impact on raising the accuracy score.
Type-1 and type-2 fuzzy logic-based space vector modulation for two-level inverter fed induction motor Abhiram Tikkani; Polaki V. N. Prasad
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.v20i4.22454

Abstract

Two-level inverter control with type-1 and type-2 fuzzy logic-based space vector pulse-width modulation (PWM) method for induction motor drive (IMD) is presented in this paper. A new sampling time independent strategy with type-1 and type-2 fuzzy based methods are used in generating three phase duty ratios which are directly obtained without mathematical equations. The conventional method of space vector modulation (SVM) produces the duty ratios for the inverter which are sampling time dependent. However, in type-1 and type-2 fuzzy based space vector PWM algorithms, the three phases duty ratios generated are sampling time independent and with a new integrated dead-time insertion in SVM itself, can be implemented practically for any switching frequency. As the rule-base for Mamdani non-singleton interval type-1 and type-2 fuzzy inference systems are designed manually, with the expert knowledge of conventional space vector PWM, the duty ratios of the inverter are generated such that the performance of the IMD is improved. The simulation studies for aforementioned cases is performed in matrix laboratory (MATLAB) and experimental validation for the proposed space vector PWM algorithms is validated using real-time sacraetheologiae magister (STM) hardware with STM32F429I Cortex M4 processor for a 1 hp IMD.

Page 290 of 346 | Total Record : 3452

 288   289   290   291   292 

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