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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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Design and analysis of single layer quantum dot-cellular automata based 1- bit comparators Ziyad A. Altarawneh; Mutaz A.B Al-Tarawneh
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.v20i1.18434

Abstract

Quantum dot-cellular automata (QCA) technology has recently emerged as a potential candidate for the design of nanometer-scale computational circuits. In digital logic circuits, the comparator is the basic building block for comparing two binary values. This paper presents and implements two 1-bit QCA-based comparator designs. The proposed QCA implementations are compact, require only a single layer and are less complex compared to recently reported designs. The QCADesigner tool has been used to confirm the functional validity of the proposed QCA structures. The simulation results of the proposed comparators have shown considerable improvements compared to their existing counterparts in terms of the number of QCA cells and occupational area requirements in addition to cost and efficient complexity values. Furthermore, all of the proposed structures are dissipating extremely low energy values. Thus, the proposed QCA-based comparators can be viewed as viable options for low power digital applications.
Transforming the voting process integrating blockchain into e-voting for enhanced transparency and securiy Agus Tedyyana; Osman Ghazali; Tryo Asnafi; Onno W. Purbo; Nur Ziadah Harun; Faizal Riza
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.v22i2.25758

Abstract

This study introduces a novel e-voting system utilizing blockchain technology to address the challenges inherent to traditional voting methods. Traditional systems often suffer from inaccuracies, susceptibility to manipulation, and elevated costs. Conversely, while e-voting shows potential, issues related to transparency and security have curbed its full adoption. Our research overcomes these hurdles by integrating a system developed through the Kanban methodology, with the blockchain serving as the central repository for all election data. This approach boosts transparency and security, using public-private key pairs for each transaction, and simplifying blockchain access. Organizers initiate elections and define eligible voters; this data is then securely moved to the Ethereum blockchain. Voters can effortlessly use the system, casting votes and accessing real-time, unalterable results. Various communication protocols ensure system stability, with simulated cyberattacks showcasing its security. After exhaustive testing and refinement, areas for further enhancement have been identified. This innovative system offers unmatched transparency and trust in the voting process, marking a considerable leap for trustworthy elections, especially in small to medium-sized settings.
Enhancing handover management in 5G networks with encoder-decoder LSTM for multistep forecasting Zineb Ziani; Mohammed Hicham Hachemi; Bouabdellah Rahmani; Mourad Hadjila
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.v23i6.27107

Abstract

The continuous evolution of wireless communication networks, fueled by advancements in 5G and the envisioned potential of 6G technologies, has introduced significant challenges in mobility management and handover (HO) optimization. The frequent HOs due to network densification, particularly at high frequencies like millimeter waves (mmWave) and terahertz (THz) bands, can lead to increased latency, and potential service disruptions. To address these issues, artificial intelligence (AI) driven approaches are emerging as promising alternatives. This paper explores the use of deep learning techniques for predictive HO management. An encoder-decoder long short-term memory (ED-LSTM) model is proposed to generate multistep predictions of future reference signal received power (RSRP) values. The model was trained and evaluated on two distinct real-world drive-test datasets. The results demonstrate that the proposed ED-LSTM model achieves lower prediction error, with a mean absolute error (MAE) of 2.07 for dataset 1 and 2.33 for dataset 2, and a mean absolute percentage error (MAPE) of 2.80% for dataset 1 and 2.96% for dataset 2. Overall, the ED-LSTM outperforms the bidirectional LSTM (BiLSTM) and standard LSTM (S-LSTM) model, achieving improvements of 33–38% on dataset 1 and 48-50% on dataset 2 in terms of MAE and MAPE, respectively.
Design and develop authentication in electronic payment systems based on IoT and biometric Ahmed Abdulkarim Talib; Aymen Dawood Salman
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.22157

Abstract

Biometrics is a highly reliable technology where it has become possible to use the characteristics of a person or user (biometrics) along with traditional passwords, and we can even say that it has become an indispensable complement in modern authentication systems today, especially with regard to bank accounts, banks, financial technology (FinTech) internet of things (IoT) devices and all a process related to money and privacy, and biometric methods are multiple and increasing day by day, the most famous of which is (iris, face chart, palm, fingerprint, and others). Biometric systems are immune and immune from modern electronic attacks, such as plagiarism or electronic theft, because the authentication here takes place when all conditions are met. Then the authentication is done and the process is completed, and the aim is to reach the highest levels of accuracy and security and to make the user more comfortable to deal with these modern systems that provide him with many advantages and high privacy.
Behavioral fingerprints: driver profiling using transformer models on next generation simulation trajectory data Mohamed Laamimach; Mghari Mohammed; Aziz Mabrouk
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.v24i4.27790

Abstract

Characterizing individual driver behavior is essential for advancing intelligent transportation systems (ITS) and autonomous vehicle safety. While deep learn ing models excel at macroscopic traffic prediction, individual driving styles are often aggregated away. This paper addresses this gap by proposing a novel, weakly supervised transformer framework for driver behavior profiling using high-resolution next generation simulation (NGSIM) US-101 trajectory data. We extract microscopic behavioral features including acceleration, lane change dynamics, and headway management from 30-second observation segments. A transformer encoder learns complex temporal dependencies to classify drivers into ’aggressive’ and ’normal’ profiles, achieving a 97% F1-score on proxy labeled segments. Crucially, these “proxy labels” are derived from heuristic statistics, meaning the model is trained to learn the mapping from sequences to these behavioral indicators rather than identifying objective aggression. Our methodology enables the creation of precise “behavioral fingerprints” that cap ture individual driving nuances. These insights are vital for developing adaptive ITS that anticipate traffic stability issues and enhance autonomous vehicle safety by predicting human intent.
Fire detection and surveillance system with cloud-based alert to enhance safety in commercials and home Siddharth Swami; Rajesh Singh; Anita Gehlot; Jasvinder Kaur; Deepti Sharma; Sameer Dev Sharma; Devendra Singh
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.v23i1.26373

Abstract

This study presents a comprehensive internet of things (IoT) solution for improving home automation and fire safety. It describes the design and construction of an all-inclusive house fire extinguishing system using an ESP8266 microcontroller to supply water, detect fires in real time, and monitor them remotely. The IoT fire safety system is currently under investigation for its potential to prevent fires. The system includes a servo motor for precise water distribution, an ESP8266 microcontroller for smooth performance and networking, a water pump for timely fire suppression, and a fire sensor for detecting heat and flames. The system architecture, software integration, and hardware parts are detailed. Field testing has shown that fire detection and suppression systems can effectively detect fires, reducing risks and damages associated with fires. The discussion section discusses the pros and cons of the recommended strategy, implications for home fire safety and automation, and areas for further research and development. The IoT-based domestic fire extinguishing system combines modern technologies with quick response time, real-time monitoring, and fast action capacity, addressing the urgent need for increased home fire safety measures.
Amazon products reviews classification based on machine learning, deep learning methods and BERT Saman Iftikhar; Bandar Alluhaybi; Mohammed Suliman; Ammar Saeed; Kiran Fatima
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.v21i5.24046

Abstract

In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Radio frequency receiver of long-term evolution system design by MATLAB Simulink Fatima Faydhe Al-Azzawi; Faeza Abbas Abid; Maham Kamil Naji
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.20936

Abstract

For wireless broadband communication long-term evolution (LTE) is a standard for also mobile devices and data terminals, by using different radio interface together with core network improvements LTE increases the capacity and speed of mobile network. In this paper radio frequency receiver of radio-frequency long-term evolution (RF-LTE) is design and simulated using MATLAB Simulink, where the design based on illustrating parameters of each stage in LTE RF receiver from generating LTE waveform to error vector magnitude (EVM) measurements, where simulation results with 8 MHz bandwidth the transmitted signal power -3200 dBm, the received signal power (-140 to -160) dBm, while the demodulated signal reaches to -60 dBm difference between main loop and side loop witch lead to high confident recovered signal, also complementary cumulative distribution function (CCDF) measurements applied on output signals so that computes the power of complementary for cumulative distribution CCDF function from signal in time domain. Where CCDF curve shows value of time that a signal stand still above the level of average power for the measured signal or the probability of signal power will be above the level of average power.
Performance enhancement of embedded object detection via neural hardware acceleration Alwin Hartono Limaran; Agung Wicaksono; Patah Herwanto
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.v24i1.27448

Abstract

This paper presents the first benchmarking of you only look once version 11 (YOLO11) on the Rockchip RK3566 neural processing unit (NPU) within the Orange Pi 3B platform. Performance was compared between the quad-core ARM Cortex-A55 CPU and the integrated NPU using the COCO2017 dataset, evaluating latency, energy, and accuracy. NPU acceleration achieved >80% latency reduction and ≈ 94% lower per-inference energy consumption, with speedup of up to 16.7× while maintaining accuracy within 0.03 mean average precision (mAP) of the baseline. Average power remained nearly constant (3.60 W central processing unit (CPU) vs. 3.59 W NPU), indicating that the efficiency gains stem from reduced inference time rather than lower wattage. Limitations included unstable INT8 quantization due to unsupported operators and calibration-range mismatch, as well as minor CPU-side overhead in preprocessing and non-maximum suppression. The findings confirm that the RK3566 NPU delivers substantial efficiency gains without accuracy loss, enabling compact and low-cost platforms to sustain modern object-detection workloads. This demonstrates that affordable NPUs can provide reliable, real time artificial intelligence (AI) inference for embedded vision, internet of things (IoT), and robotics applications.
Optimized decision tree classification method for diabetes prediction Elly Muningsih; Fabriyan Fandi Dwi Imaniawan; Aprih Widayanto; Eva Argarini Pratama; Sutrisno Sutrisno; Sri Kiswati
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.v22i4.25656

Abstract

Diabetes is one of the most deadly chronic diseases because most sufferers do not realize they have it. A more accurate prediction of diabetes disease must be made to reduce the risk of bad things happening to sufferers. This research will optimize the decision tree (DT) classification method for diabetes prediction. Optimization is done by splitting criteria, splitting data, particle swarm optimization (PSO), and parameter optimization to find the highest and most accurate forecast of diabetes. Splitting criteria is done by comparing the results of three criteria, namely gain ratio (GR), information gain (IG), and gini index (GI). Splitting data is done by dividing training data and testing data into three comparison groups, namely 70:30, 80:20, and 90:10. The application of PSO and parameter optimization is carried out to increase the accuracy value. The processed data is taken from the UCI machine learning repository with 520 records and 17 attributes (1 class/label attribute). From the experiments, the GI criterion with splitting data 90:10 obtained the greatest accuracy of 98.08%, and the combination with PSO resulted in an accuracy of 97.66%. Meanwhile, parameter optimization with splitting data 90:10 combined with GR criteria resulted in the highest accuracy of 97.90%.

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