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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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Sclera boundary localization using circular hough transform and a modified run-data based algorithm Tunde Taiwo Adeniyi; Oladele Tinuke Omolewa; Jide Kehinde Adeniyi
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.v22i3.24588

Abstract

Security challenges over the years has led to the need for an improvement in the traditional security approaches. This led to the advent of biometrics. Recently, among the biometric approaches, sclera has been an area of imense study. This is due to its accuracy; however, segmentation of the sclera has been a limiting factor to the application of this biometric trait. Several approaches have been proposed in literature but there is still the need to improve the segmentation accuracy. This study proposes the use of circular hough transform and a modified run-data based algorithm. The study also presented a sclera recognition system using the compound local binary pattern for features extraction and Manhattan distance for classification. The system produced a segmentation accuracy of 99.9% for sclera blood vessels, periocular and iris (SBVPI) sclera database and 100% for manually captured sclera database. The system produced an accuracy of 99.98 for SBVPI sclera database and 99.99% for manually captured sclera database.
Modified variable neighborhood search algorithm for maximum power point tracking in PV systems under partial shading conditions Hazem W. Marar; Rosana W. Marar
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.v20i6.22646

Abstract

Photovoltaic (PV) systems are being increasingly popular for power generation from solar radiation as the usage of renewable energy grows. To improve the efficiency, the system should operate at its the maximum power point. Hence, several algorithms have been developed for this purpose. Continuous tracking for the maximum power point often leads to heavy energy loss. Furthermore, partial shading can dramatically lower the performance of the system. A modified variable neighborhood search algorithm is proposed resulting zero oscillations around the operating point. A set of variables are used to improve the system’s configurability in various atmospheric and geographical conditions. Simulation result proves the operation of the proposed algorithm, its accuracy, and its fast response time.
Privacy-preserving messaging for medical device networks Rachmad Andri Atmoko; Salnan Ratih Asriningtias; Akas Bagus Setiawan; Devasis Pradhan; Ismail Rakip Karas
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.v24i4.27794

Abstract

Internet of medical things (IoMT) deployments rely on lightweight messaging, but the message queuing telemetry transport (MQTT) protocol still exposes sensitive metadata through plaintext topic names and stable client identifiers. In healthcare settings, this visibility can reveal patient identity, location, and monitored condition even when payloads are encrypted. This paper presents a gateway-based privacy architecture that replaces semantic MQTT topics and client identifier (ClientIDs) with rotating pseudonyms managed by a topic and ID privacy manager (TPM). The design uses hash-based message authentication code using secure hash algorithm 256-bit (HMAC-SHA256) for pseudonym generation, advanced encryption standard-galois/counter mode (AES-GCM) for payload protection, and a short overlap phase that preserves message delivery during rotation without modifying the broker. Experiments from 5 to 10,000 patients show a consistent 5.0x increase in topic diversity, 2.50 μs per message cryptographic overhead, 0.063 ms maximum latency overhead, and zero packet loss. These results indicate that practical IoMT deployments can improve metadata privacy while still meeting real-time clinical communication requirements.
Enhanced sentiment analysis and emotion detection in movie reviews using support vector machine algorithm Aditiya Hermawan; Rico Yusuf; Benny Daniawan; Junaedi Junaedi
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.v23i1.26377

Abstract

Films evoke diverse responses and reactions from audiences, captured through their reviews. These reviews serve as platforms for audiences to express opinions, evaluations, and emotions about films, reflecting the personal experiences and unique perceptions of the viewers. Given the vast volume of reviews and the distinctiveness of each perspective, automated analysis is essential for efficiently extracting valuable insights. This study employs the support vector machine (SVM) algorithm for classifying movie reviews into positive and negative categories. The dataset includes 50,000 IMDb movie reviews, split evenly between positive and negative sentiments. Each review is analyzed using the National Research Council Canada (NRC) emotion lexicon (NRCLex) to assign scores for emotions such as anger, disgust, fear, joy, sadness, and surprise. Subsequently, these reviews are further analyzed using term frequency-inverse document frequency (TF-IDF) for classification. The proposed algorithm achieves 90% accuracy, indicating its effectiveness in classifying sentiments in movie reviews. The study's findings confirm the potential of the SVM algorithm for broader applications in sentiment analysis and natural language processing. Additionally, integrating emotion detection enhances understanding of nuanced emotional content, providing a comprehensive approach to sentiment classification in large datasets.
Modeling of Maximum Power Point Tracking Controller for Solar Power System Aryuanto Soetedjo; Abraham Lomi; Yusuf Ismail Nakhoda; Awan Uji Krismanto
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.v10i3.819

Abstract

 In this paper, a Maximum Power Point Tracking (MPPT) controller for solar power system is modeled using MATLAB Simulink. The model consists of PV module, buck converter, and MPPT controller. The contribution of the work is in the modeling of buck converter using equation model approach rather than circuit model one.  The buck converter model is developed using equation model that allowing the input voltage of the converter, i.e. output voltage of PV is changed by varying the duty cycle, so that the maximum power point could be tracked when the environmental changes. From the experiment, the developed model comforms with the circuit model provided by MATLAB Simulink Power Simulation. Furher, the simulation results show that the developed model performs well in tracking the maximum power point (MPP) of the PV module using Perturb and Observe (P&O) Algorithm. 
Efficient combined fuzzy logic and LMS algorithm for smart antenna Mohammed Hussein Miry; Ali Hussien Mary
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.v21i5.24370

Abstract

The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
IgG-IgM antibodies based infection time detection of COVID-19 using machine learning models Saja Dheyaa Khudhur; Dhuha Dheyaa Khudhur
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.v20i2.21649

Abstract

Over the last two years, most scientists have been researching the solution to the pandemic coronavirus disease 2019 (COVID-19). So, the effective inspection and the rapid diagnosis of COVID-19 provide a mitigation ability to the burden on healthcare systems. These research works focus on detecting and knowing the history of infection in terms of time and developed symptoms. In infections detection, artificial intelligence (AI) technologies increase the accuracy and efficiency of the adopted detection methods. These methods will aid the medical staff in classifying patients, essentially when there is a healthcare resources shortage. This paper proposed machine learning-based models for detecting the time of COVID-19 infection in weeks using the laboratory factors of detected antibodies immunoglobulins G and immunoglobulins M (IgG-IgM). This test is common and helpful in diagnosing the suspected patients who held a negative result for the reverse transcription-polymerase chain reaction (RT-PCR) test. The proposed models consider two machine learning models adopting root mean square error (RMSE) and mean absolute error (MAE) factors. The results show acceptable efficiency of performance that ranges from 80% to 100% for pointing the patient in any week of infection, to reduce the likelihood of transmitting the infection from patients who have developed symptoms but with false-negative RT-PCR test.
Improved disturbance rejection of induction motor drives using PI–VGSTASM control and torque disturbance estimation Ngoc Thuy Pham; Duc Thuan Le; Thanh Tinh Pham
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.v24i1.27459

Abstract

Induction motor (IM) drives often suffer performance degradation under load variations and parameter uncertainties when using conventional proportional–integral (PI)- based field-oriented control (FOC). To address these issues, this study proposes a composite control framework combining a PI regulator in the speed loop with a Lyapunov-based variable-gain super twisting algorithm (VGSTA) for the inner current loops to enhance robustness against disturbances and parameter variations. In addition, a load torque observer is developed to estimate unknown disturbances in real time and generate an equivalent compensation current, thereby improving disturbance rejection. Unlike existing approaches, the proposed strategy achieves a balance between simplicity, robustness, and smooth control by integrating classical PI control with higher-order sliding mode techniques and adaptive observer dynamics. Furthermore, the controller and observer gains are optimized using particle swarm optimization (PSO) to improve convergence and reduce overshoot under uncertain conditions. Simulation results demonstrate accurate speed regulation, effective chattering reduction, and reliable operation under load and parameter variations. Due to its low computational complexity and high robustness, the proposed method is well suited for industrial drive systems and electric mobility applications.
For wireless applications, design and analysis of patch antenna at 2.45 GHz Md. Sohel Rana; Shake Zion Haider Ovi; A.S.M. Tanvir Ul Islam; Piyal Mistry; Sahriar Islam Shipon; Md. Jahidur Rahaman; Md. Shahriar Rakib; Omar Faruq
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.v22i4.25751

Abstract

This research designs, analyzes, and studies a 2.45 GHz rectangular microstrip patch antenna (RMPA). The antenna design uses Rogers RT5880 (lossy) substrate material with 2.2 dielectric permittivity, 1.5 mm thickness, and 0.0009 loss tangent. Additionally, the antenna was designed and simulated using computer simulation technology (CST) studio 2019 software. Plot designs were again created using Origin Pro Software. The simulation results showed that the return loss (S11), voltage standing wave ratio (VSWR), gain, directivity, bandwidth, efficiency, and surface current were -45.992 dB, 1.0101, 6.115 dBi, 6.534 dBi, 70.8 MHz, 93.59%, and 49.9 A/m, respectively. This paper aims to increase return loss to a typical VSWR value near 1. Besides boosting antenna gain, directivity, and efficiency, it can be used in future wireless applications, including mobile phones and wireless LANs. The proposed antenna design outperforms earlier experiments, demonstrating that the research has increased performance.
Review on cyber-security for optimized and smart irrigation systems Wid Badee Abdulaziz; Muayad Sadik Croock
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.v21i1.24234

Abstract

It is well known that the resources in agriculture are considered the most important factors for success. Therefore, numerous researchers are involved in the field of managing these resources, particularly water and consumed power. Moreover, the security side of these resources is considered, particularly the cyber-attacks. In this project, an optimized resource management method is proposed for allocating the available resources in a smart on-demand way. The proposed method is applied for dripped and sprinkler irrigation systems for managing the available water and generated power. In addition, an optimization method is utilized to obtain reliable solutions for managing the adopted resources. This method adopts a cyber security algorithm for preventing any possible attack. Wireless sensor network (WSN) is used as a reading source, in which the underlying area is covered well, since using sensors in irrigation systems is cost-effective that ensures on-demand irrigation process to save water and power resources. This network is supported by the fault tolerance method to increase availability.

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