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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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Throughput analysis of power beacon-aided multi-hop MIMO relaying networks employing NOMA and TAS/SC Pham Minh Nam; Thanh-Long Nguyen; Ha Duy Hung; Tran Trung Duy; Nguyen Thanh Binh; Nguyen Luong Nhat
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.v20i4.23769

Abstract

This paper measures end-to-end (e2e) throughput of power beacon-assisted multi-hop decode-and-forward (DF) relaying scheme adopting non-orthogonal multiple access (NOMA) and transmit antenna selection (TAS)/selection combining (SC). Particularly, TAS/SC and NOMA are adopted at each hop to relay different data of a source to multiple destinations. Moreover, the transmitters including source and relays have to harvest wireless energy from radio frequency (RF) signals from a power beacon. We also propose a simple and efficient power allocation method for the signals transmitted at each hop. For performance measurement and comparison, we provide closed-form formulas of the e2e throughput over Rayleigh fading channel. We then verify our derivations by computer simulations as well as compare the e2e throughput performance between our scheme and the corresponding one that does not use NOMA.
Low-cost environmental chamber for battery calendar aging under tropical conditions: design and validation Uvi Desi Fatmawati; Iwa Garniwa; Faiz Husnayain; Danang Lelono; Kuwat Triyana; Amelia Chandra Pratiwi
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.v24i3.27666

Abstract

This study presents a low-cost environmental chamber designed to replicate tropical temperature and humidity conditions for calendar-aging studies of LiFePO₄ lithium iron phosphate (LFP) cells. The system integrates passive insulation with an Arduino-based active control system for real-time monitoring. The design’s novelty lies in its cost-effective ability to maintain tropical-specific profiles, validated against Meteorology, Climatology, and Geophysics Agency (BMKG) meteorological data to ensure correlation with diurnal cycles. Experimental results demonstrate stable operation for 13 consecutive days, with an average temperature of 29.47 °C and a stability metric of ±0.61 °C, keeping 100% of data within the 25–35 °C target. Although relative humidity (RH) showed an average of 76.52%, its stability was quantified by a 96.98% success rate in maintaining the 70–80% target range. The chamber’s suitability for long-term degradation studies was confirmed via a one-month calendar-aging test on a 15 Ah LFP cell, where tha battery capacity decreased from 100% to 99.58%. These results demonstrate that the proposed chamber reliably maintains tropical environmental conditions and is suitable for long-term, low-cost battery aging studies.
Geographically weighted regression analysis of electricity consumption in Indonesian households: aligning with SDG 7 Tommy Novianto; Rezzy Eko Caraka; Prana Ugiana Gio; Rumanintya Lisaria Putri; Agung Sutoto; Rung Ching Chen; Maengseok Noh; Bens Pardamean
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.v22i6.26051

Abstract

The objective of this study is to establish a thorough comprehension of the interaction of population dynamics, poverty rates, minimum wage levels, and regional GDP in relation to household electricity consumption. The main objective is to improve the precision of electricity demand predictions and prevent planning mistakes, such as the considerable surplus of 6-7 GW in the Java Bali system between 2020 and 2023, resulting in major financial losses. We evaluate and compare the models by employing several approaches, such as ordinary least square (OLS) and geographically weighted regression (GWR) with fixed and adaptive bandwidths. We use modified R-squared and corrected Akaike Information Criterion (AICc) values for this assessment. The GWR with adaptive bandwidth is shown to be the most resilient method and is subsequently chosen for modeling. The results indicate that there is a strong correlation between the number of impoverished individuals and electricity use, with a coefficient range of 0.35-0.55. Furthermore, the correlation between poverty rates and power usage is defined by a coefficient that varies between -0.0010 and -0.0030. There is a direct relationship between regional GDP and power growth, as indicated by coefficients ranging from 1,000,000 to 5,000,000. Moreover, the impact of minimum wage levels differs among different locations.
Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification Ajib Susanto; Christy Atika Sari; Hidayah Rahmalan; Mohamed A. S. Doheir
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.v21i3.24928

Abstract

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.
Natural language processing and machine learning based cyberbullying detection for Bangla and Romanized Bangla texts Md. Tofael Ahmed; Maqsudur Rahman; Shafayet Nur; Abu Zafor Muhammad Touhidul Islam; Dipankar Das
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.v20i1.18630

Abstract

The popularity of social media has been increasing tremendously in recent times and thus cyberbullying towards people has also increased at an alarming rate. Many cyberbullying texts can be found in the comment sections of many well-known Bangladeshi social media personalities YouTube videos. It has the potential to cause severe emotional and psychological distress. Therefore, texts containing cyberbullying should be detected at the earliest stage and prevented from being displayed. In this study, we use natural language processing (NLP) techniques and various machine learning classifiers and presented model for cyberbullying detection in Bangla and Romanized Bangla texts obtained from YouTube video comments. We developed our own datasets using YouTube application programming interface (API) version 3.0. We collected 5000 Bangla comments, as well as 7000 Romanized Bangla comments from videos of different well-known social media personals. These two datasets, as well as a third dataset of 12000 texts which was the combination of the first two datasets were used to train the classifiers. These datasets were used to train machine learning classifiers after being preprocessed using NLP techniques. With an accuracy score of 76%, support vector machine (SVM) outperformed the other classifiers for the first dataset. The highest accuracy scores for the second and third datasets were 84% and 80%, respectively, which were both achieved by multinomial naive Bayes.
Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR Heru Priyanto; Rizqon Fajar; Yaaro Telaumbanua; Ariyanto Ariyanto; Mohammad Mukhlas Af; Sigit Tri Atmaja; Muhammad Samsul Maarif; Kurnia Fajar Adhi Sukra; Fauzi Dwi Setiawan
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.v23i6.27128

Abstract

In recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge.
Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets Maha A. Rajab; Firas A. Abdullatif; Tole Sutikno
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.v22i2.25840

Abstract

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classification by adapting VGG-16 net and VGG-19 net models and subsequently identifying the optimal performer between the two nets during the classification process. A publicly available dataset comprising 500 images categorized into 5 distinct classes (100 images per class), was utilized in this work. The obtained empirical outcomes demonstrate a remarkable accuracy rate of 99.6% for the VGG-16 net model, while VGG-19 net achieves a 100% accuracy rate. Based on these findings, it can be inferred that VGG-19 net exhibits superior performance in classifying images of grapevine leaves compared to the VGG-16 net.
Performance analysis and evaluation of distance vector and link state routing protocols over a large area networks Abdulhameed N. Hameed; Salah Abdulghani Alabady; Mohammad A. Thanoon
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.v20i6.22211

Abstract

Routing protocols are extremely important incredibly significant in data communication and computer networks. The high performance, reliability, stability, and security of the networks depend primarily on choosing the best type of dynamic routing protocol. In this paper, we evaluate and investigate the network performance for routing information protocol (RIP), enhanced interior gateway routing protocol (EIGRP), open shortest path first (OSPF), and intermediate system-to-intermediate system (IS-IS) routing protocols with three different scenarios of routes failure using the optimized network engineering tools (OPNET) simulator to determine which of the protocols is the most appropriate and effective in achieving high network performance. The results show that for large area networks, the EIGRP routing protocol gives the best network performance when all network routers are working with no failing, but when some network routers were failing to work and path failure is happening, the IS-IS link-state routing protocol works efficiently and gives the best performance. The obtained results for IS-IS protocol when failing seven routers is as: the hypertext transfer protocol (HTTP) page response time is (247.8 msec), voice delay variation is (4.19 µsec), video delay variation is (8.83 µsec) and ping request and response time is (115 msec).
Acoustic and vibration side channel analysis on post-quantum cryptography using image-based deep learning Abdul Haris Muhammad; Gamaria Mandar; Adelina Ibrahim
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.v24i4.27791

Abstract

Post-quantum cryptography (PQC) is designed to resist quantum-era attacks; however, practical implementations remain vulnerable to physical side channel leakage. This work proposes an image-based acoustic–vibration side-channel analysis framework to assess non-invasive leakage in PQC systems. Acoustic and vibration signals from secret-dependent executions are modeled and transformed into time–frequency spectrograms using short time fourier transform (STFT). The dataset comprises 1,545 samples (1,236 training and 309 testing), acquired at 16 kHz and segmented into 2.5-second windows. Leakage classification is performed using convolutional neural networks (CNNs) and vision transformers (ViTs) under single-modality and multimodal fusion settings. Results show that acoustic signals yield strong leakage, achieving up to 100% accuracy with CNN, while vibration signals reach up to 98.75%. Multimodal fusion improves training stability and overall performance, and ViT models demonstrate better generalization across modalities. These findings confirm that multimodal spectrogram based deep learning is effective for PQC side-channel analysis and underscore the need for rigorous physical security evaluation in real-world PQC implementations.
Visible light communication for rapid monitoring of environmental changes using thin film solar cells Soni Prayogi; Wahyu Kunto Wibowo
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.v23i1.26375

Abstract

This study investigates the use of visible light communication (VLC) for rapid environmental monitoring by leveraging thin film solar cells as signal receivers. VLC, which employs visible light for data transmission, presents an energy-efficient and eco-friendly approach for real-time monitoring. Thin-film solar cells, recognized for their efficiency and low-light performance, function both as environmental sensors and VLC signal receivers. We conducted experiments to evaluate the system's performance across various environmental conditions, such as light intensity and temperature changes. Our findings indicate that thin-film solar cells can swiftly and accurately detect environmental changes while maintaining a low bit error rate for VLC data. The system also shows high responsiveness to rapid light variations, making it well-suited for dynamic monitoring tasks like air quality, humidity, and forest fire detection. This research highlights VLC technology's significant potential for environmental monitoring applications requiring quick, real-time data transmission, and energy efficiency with thin-film solar cells. The integration of this technology promises to enhance environmental monitoring systems, contributing to climate change mitigation and improved environmental management, and sets the stage for developing advanced, sustainable solutions in wireless communication and ecological monitoring.

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