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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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5G-backed resilience and quality enhancement in internet of medical things infrastructure for resilient infrastructure Noora Jamal Ali; Noor Amer Hamzah; Ahmed Dheyaa Radhi; Yitong Niu; Poh Soon JosephNg; Jamal Fadhil Tawfeq
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.v22i2.24863

Abstract

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation.
Deep transfer learning based disease detection and classification of tomato leaves - a comparative analysis Munira Akter Lata; Marjia Sultana; Iffat Ara Badhan; Mastura Jahan Maria; Fariha Tasnim Nuha
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.v23i5.26887

Abstract

A wide variety of diseases have a significant impact on tomato plants. To avoid crop quality issues, a prompt and precise diagnosis is crucial. Classifying plant diseases is one of the numerous applications where deep transfer learning models have recently produced remarkable results. This study dealt with fine-tuning by contrasting the most advanced architectures, including Inception V3, ResNet-18, ResNet-50, VGG-16, VGG-19, GoogLeNet, and AlexNet. In the end, a comparison evaluation is conducted. Nine distinct tomato disease classes and one healthy class from PlantVillage make up the dataset used in this study. Precision, recall, F1-score, and accuracy were the basis for a multiclass statistical analysis that assessed the models. The ResNet-50 approach yielded significant results with precision: 82%, recall: 81%, F1-score: 81%, and accuracy: 85%. With this high success rate, it is reasonable to say that mobile applications or IoT-compatible gadgets implemented with the ResNet-50 model can assist farmers in identifying and safeguarding tomatoes against the aforementioned diseases.
High-speed multi-channel long-haul coherent optical transmission system Muthanna Ali Kadhim; Ali Yousif Fattah; Atheer Alaa Sabri
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.v20i5.23454

Abstract

In this work, high-speed transmission over the long-haul optical channel using orthogonal frequency division multiplexing (OFDM) was investigated. Furthermore, we recommend mixing polarization division multiplexing (PDM) with coherent OFDM (CO-OFDM) and quadrature amplitude modulation (16-QAM) to improve spectral efficiency (SE) while transmitting over a wavelength division multiplexing (WDM) system. An 800 Gb/s WDM PDM-CO-OFDM-16QAM transmission system with various channel spacing of 100 GHz, 50 GHz, and 25 GHz is examined utilizing the OptiSystem (2021) version 18.0 software package over ten spans of 60 km standard single-mode fiber (SSMF). Different channel spacing WDM systems have been compared in terms of performance and SE. The results reveal that the WDM system with 100 GHz channel spacing has a longer transmission range and needs minimal optical signal to noise ratio (OSNR) at the reception. The 25 GHz channel spacing WDM system exceeds the others in terms of SE. Further, the effect of ultra-low loss and large effective area fiber in lowering span loss and nonlinear effects for 25 GHz channel spacing WDM system is investigated. The findings show that the system performance with the new fiber outperforms the SSMF. The acceptable bit error rate (BER) for this study is 0.033 (20% concatenated forward error correction (FEC) threshold).
An improved harvested energy management mechanism for wireless sensor networks Abdelmalek Bengheni; Messaoud Hameurlaine
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.v24i4.27486

Abstract

Wireless sensor networks (WSN) play a vital role in monitoring and communication applications, but their performance is often constrained by limited battery power. Energy harvesting (EH) technologies have emerged as a promising solution to extend network lifetime by supplying supplementary energy from the environment. However, efficiently balancing harvested and consumed energy remains a significant challenge. This paper introduces an improved harvested energy management mechanism (IHE2M) that dynamically adjusts the duty cycle of sensor nodes based on residual energy availability. Unlike traditional approaches that rely on fixed duty cycles, IHE2M allows each node to determine its sleep and active periods adaptively, reducing collisions, idle listening, and unnecessary retransmissions. The mechanism was evaluated through OMNeT++/MiXiM simulations and compared with existing schemes such as EH2M and dynamic source routing (DSR). Results demonstrate that IHE2M achieves higher packet delivery ratios, lower latency, and better throughput while reducing average energy consumption per node. The findings confirm that IHE2M provides a more sustainable and efficient solution for energy harvesting WSN, improving reliability and extending network lifetime.
Real time Indian sign language recognition using transfer learning with VGG16 Sumit Kumar; Ruchi Rani; Sanjeev Kumar Pippal; Ulka Chaudhari
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.v22i6.26498

Abstract

Normal people’s interaction and communication are easier than those with disabilities such as hearing and speech, which are very complicated; hence, the use of sign language plays a crucial role in bridging this gap in communication. While previous attempts have been made to solve this problem using deep learning techniques, including convolutional neural networks (CNNs), support vector machine (SVM), and K-nearest neighbours (KNN), these have low accuracy or may not be employed in real time. This work addresses both issues: improving upon prior limitations and extending the challenge of classifying characters in Indian sign language (ISL). Our system, which can recognize 23 hand gestures of ISL through a purely camera-based approach, eliminates expensive hardware like hand gloves, thus making it economical. The system yields an accuracy of 97.5% on the training dataset, utilizing a pre-trained VGG16 CNN optimized by the Adam optimizer and cross-entropy loss function. These results clearly show how effective transfer learning is in classifying ISL and its possible real-world applications.
Experimental of vectorizer and classifier for scrapped social media data Setiawan Assegaff; Errissya Rasywir; Yovi Pratama
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.v21i4.24180

Abstract

In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided learning. Our tests use social media data. Based on the tests that have been carried out on classifier variations and vectorizer variations, it was found that the best classifier is a linear regression algorithm based on predictive adaptive compared to the random forest method based on decision trees, probability-based Bernoulli NB and SVC which work by clustering. Meanwhile, from the test results on the count vectorizer, term frequency-inverse document frequency (TFIDF), and hashing, the best accuracy is achieved on the TFIDF vectorizer. In this case, it means that the TFIDF vectorizer has a better value in presenting word feature dimensions.
Applying artificial intelligence for the application of bridges deterioration detection system Xuan-Kien Dang; Le Anh-Hoang Ho; Xuan-Phuong Nguyen; Ba-Linh Mai
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.v20i1.20783

Abstract

Recently, advances in sensor technologies, data communication paradigms, and data processing algorithms all affect the feasibilities of the bridges structural health monitoring and deterioration detection, and other implementations of monitoring operations. The paper proposes a method to design an irregularity detection and monitoring system for road bridges that combines internet of things (IoT) and artificial intelligence (AI) technologies. Raspberry Pi 4 embedded computer integrating IoT and AI technology with convolutional neural network (CNN) is employed to simultaneously monitor remote bridges on websites and apps via Google Firebase cloud database. The first step of successful testing in the laboratory showed that the system can work stably and coincide with the proposed goals.
Machine learning-based reconstruction of missing rainfall extremes: a comparative analysis with classical models Yanuar Henry Pribadi; Tania June; I Putu Santikayasa; Supari Supari; Ana Turyanti
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.v23i6.27404

Abstract

The limited availability of daily rainfall data remains a key challenge in rainfall data analysis. This study assesses the effectiveness of spatial interpolation and bias correction techniques using satellite-derived rainfall data to fill missing observations in the Banten and Jakarta regions. Three interpolation methods inverse distance weighting (IDW), kriging, and spline were compared. Nine statistical and machine learning-based bias correction methods were applied to climate hazards group infrared precipitation with station data (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and global precipitation measurement-integrated multi-satellite retrievals for GPM (GPM IMERG). Performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), bias, Pearson correlation (R), and Kling-Gupta efficiency (KGE) in the expert team on climate change detection and indices (ETCCDI) extreme index. The research findings indicate that CHIRPS with quantile mapping (QM) bias correction delivers the best performance, followed by random forest regression (RFR) as the most accurate machine learning method. In spatial interpolation, IDW stands out as the leading method. Testing the extreme index ETCCDI confirms that CHIRPS-QM consistently outperforms machine learning and interpolation methods. In general, CHIRPS-QM and IDW represent the most effective combination of techniques for reconstructing daily rainfall, particularly extreme events. This study uniquely integrates spatial interpolation and bias correction in a unified evaluation.
Metamaterial inspired circular antenna for Bluetooth band integration Ismail Moumen; Mourad Elhabchi; Mohamed Nabil Srifi; Raja Touahni
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.v22i3.25756

Abstract

Within the rapidly evolving landscape of wireless communication technologies, this scientific paper delves into an innovative approach by exploring the integration of the Bluetooth frequency band into a circular antenna design. Leveraging the capabilities of metamaterials. The Bluetooth frequency band a cornerstone for short-range communications, takes center stage in our study. Traditionally associated with wireless connectivity in diverse applications, its integration into a circular antenna structure opens up new avenues for optimizing performance and functionality. Our research aims to not only enhance the efficiency of short-range communication systems but also contribute to the broader discourse on antenna design evolution. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. By scrutinizing the symbiotic relationship between the Bluetooth frequency band and the circular antenna augmented with metamaterial, our study endeavors to push the boundaries of current design paradigms. We believe that this exploration will not only address the contemporary challenges associated with wireless communication but also pave the way for the development of more sophisticated, adaptable, and compact devices in the ever-expanding realm of telecommunications.
Surface coated red phosphors with silica nanoparticles and silica nanocomposites: performance modifications Phuc Dang Huu; Tri-Vien Vu
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.v20i6.24084

Abstract

Surface coverings using nanomaterials of silica nanoparticles and poly (methyl methacrylate) – abbreviated “PMMA-silica nanomaterials” – are introduced to the Y2O2S:Eu3+ phosphor through four distinct approaches. The objective is to enhance Y2O2S:Eu3+ phosphorescence and prolonged sustainability. Using immerse-daubing along with sol-gel approach (Stober way), it is possible to have Y2O2S:Eu3+ phosphors covered with relatively-monodisperse nano-silica particles (5 nm). To create the silica nano-crystals employed in the coating process of phosphors, we carry out concurrently hydrolysis and condensation procedures to develop the formation and basic polymerization utilizing poly (1-vinyl-2-pyrrolidone). The surface coating layer for Y2O2S:Eu3+ spheres, which comprises polymethyl methacrylate (PMMA)-silica nanocomposites, can be formed in two ways: by combining silica nanogranules with methyl methacrylate (MMA) monomer as well as through subjecting MMA to tetraethyl orthosilicate (Si(OC₂H₅)₄) chemical compound. The latter approach demonstrated is considered the highest augmentation in phosphorescence and prolonged sustainability for Y2O2S:Eu3+ spheres. Specifically, when using the second approach to get PMMA-silica phosphor coating, Y2O2S:Eu3+ exhibited a 5-percent increase in the phosphorescent intensity, compared to untreated phosphors. Contrary to a drop in cathode phosphorescence (CP) output and a rising bombardment duration for exposed phosphor, the latter technique produces a practically constant CP energy with PMMA-silica nanocomposite covered phosphors.

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