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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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Optimized human detection in NLOS scenarios using hybrid dimensionality reduction and SVM with UWB signals Enoch Adama Jiya; Ilesanmi Banjo Oluwafemi; Emmanuel Sunday Akin Ajisegiri
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.v23i5.26738

Abstract

Trapped victim localization in search and rescue (SAR) operations is especially difficult in non-line-of-sight (NLOS) conditions, where traditional techniques fail due to debris and signal distortion. Ultra-wideband (UWB) NLOS signal datasets offer a promising alternative but are often high-dimensional and noisy. This study proposes an optimized dimensionality reduction framework combining an adaptive human presence detector (AHPD) with genetic algorithms (GA) and independent component analysis (ICA), followed by support vector machine (SVM) classification. The approach is tested on a public NLOS dataset comprising 23,522 dynamic instances, each with 256 signal samples per attribute, simulating complex SAR scenarios including rubble and dynamic obstacles. The results indicate that the AHPD+GA+SVM model reached an accuracy of 85.78%, sensitivity of 80.00%, and specificity of 96.46%, which is better than the AHPD+ICA +SVM model that had an accuracy of 79.20%, sensitivity of 73.07%, and specificity of 81.05%. These findings demonstrate the framework’s robustness and scalability, making it a strong candidate for real-time human detection in disaster recovery missions.
Research on intelligent river water quality management system using blockchain-internet of things Anitha Roy; Jubilant J Kizhakkethottam
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.v22i6.25937

Abstract

Each and every living thing needs fresh water to survive. Fresh water is becoming a precious resource as a result of the combined risks of rapid urbanization, pollution, and climatic changes. The quality of the water we drink every day has an impact on our lives, either directly or indirectly. Maintaining the sustainability and health of the environment requires constant attention to water quality. Modern technology makes it possible to gather and analyze data from water distribution networks in order to maximize resources and enhance decision-making for all parties. Despite the enormous global growth of the internet of things (IoT) and blockchain in recent years, their integration is still in its early stages. In this research contains a technological framework that combines blockchain and IoT called B-IoT and aims to reward and incentivize more sustainable water quality management with real-time monitoring and security. IoT is used to monitor water quality in water resources and find any violations. By using blockchain, it is possible to retain the accuracy, reliability, and transparency of the records of breaches. This system will be able to gauge the water’s quality in real-time and allow for the quick identification of any infractions necessary to commit the crime.
Driving cycle tracking device development and analysis on route-to-work for Kuala Terengganu city Arunkumar Subramaniam; Nurru Anida Ibrahim; Siti Norbakyah Jabar; Salisa Abdul Rahman
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.v21i3.24264

Abstract

Driving cycle is a series of speed versus time profile used to represent driving patterns of a vehicle. research in this field guides vehicle manufacturers and environmentalists to investigate air quality through emissions. Study on driving cycle also aids manufacturers to manage vehicle emissions and to save energy released through exhaust. Also, driving cycles can provide information on road condition and driving behaviour of an individual. For that, a proper data collection method is crucial as it is solely based on real world driving. This research is an initiative to construct a prototype of driving cycle tracking device (DC-TRAD) in which it was implemented with internet-of-things (IoT) to manage big number of collected data. U-Blox global positioning system (GPS) neo 7 M sensor was used to increase the accuracy of data capturing and it was used on route-to-work for Kuala Terengganu city (RTW DC for KT city) for analysis.
Elevating cultural understanding: interactive museum exploration using 3D AR and MDLC framework Edy Jogatama Purhita; Eko Sediyono; Ade Iriani
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.v23i5.26343

Abstract

Limited access to information and interaction with artifacts in museums often hinders visitors from gaining a deeper understanding of the culture and historical context presented. This study addresses this challenge by developing a three-dimensional (3D) augmented reality (AR)-based interactive museum that enhances the museum visitor experience through an intuitive user interface (UI) and enriched content related to the exhibited artifacts. This study explores the potential of 3D AR technology in enhancing visitor engagement and interaction with museum exhibits, providing a more immersive and informative experience. This study uses the multimedia development life cycle (MDLC) as a framework to develop a 3D AR-based interactive museum. By applying the MDLC approach, this study integrates advanced AR technology with comprehensive and detailed content, resulting in a structured and user-centered interactive platform. Key benefits of this approach include enhanced interactivity, enriched artifact information, and an intuitive interface that facilitates easier access to museum content. The findings indicate that the developed interactive museum successfully overcomes the barriers of limited accessibility of information and interaction with artifacts. Through the application of advanced AR technology, the museum visitor experience is significantly enhanced, making the museum more inclusive, interactive, and educative for visitors.
Combined ILC and PI regulator for wastewater treatment plants Lanh Van Nguyen; Nam Van Bach; Hai Trung Do; Minh Tuan Nguyen
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.v18i2.14895

Abstract

Due to high nonlinearity with features of large time constants, delays, and interaction among variables, control of the wastewater treatment plants (WWTPs) is a very challenging task. Modern control strategies such as model predictive controllers or artificial neural networks can be used to deal with the non-linearity. Another characteristic of this system should be considered is that it works repetitively. Iterative learning control (ILC) is a potential candidate for such a demanding task. This paper proposes a method using ILC for WWTPs to achieve new results. By exploiting data from the previous iterations, the learning control algorithm can improve gradually tracking control performance for the next runs, and hence outperforms conventional control approaches such as feedback controller and model predictive control (MPC). The benchmark simulation model No.1-BSM1 has been used as a standard for performance assessment and evaluation of the control strategy. Control of the dissolved oxygen in the aerated reactors has been performed using the PD-type ILC algorithms. The obtained results show the advantages of ILC over a classical PI control concerning the control quality indexes, IEA and ISE, of the system. Besides, the conventional feedback regulator is designed in a combination with the iterative learning control to deal with uncertainty. Simulation results demonstrate the potential benefits of the proposed method.
Optimal placement of distributed generations on distribution network for reducing power loss and improving feeder balance Huu Truong Trinh; Thuan Thanh Nguyen
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.v22i2.25683

Abstract

The suitable placement and power of distributed generation (DG) can bring technical benefits to the distribution network. This paper applies the Coot optimization algorithm to the DG placement problem with the goal of minimizing power loss and feeder balancing load (LBF). The weight method is used to combine the membership objectives. The evaluation results on the network of 70 nodes for different weight values of the objective function show that the optimal power and installation location of DGs significantly reduces power loss and improves LBF index. In this study, eleven cases were considered. As the weight of power loss part ?1 increases from 0 to 1, the power loss gradually decreases, the LBF index gradually increases, the maximum current gradually decreases, and the minimum voltage amplitude gradually improves. Comparing the results of Coot with particle swarm optimization (PSO) shows that the indicators are improved. In the three cases where ?1 is 0, 0.5, and 1, power loss gained by Coot compared to PSO is less than 47.2663 kW, 73.2725 kW, 30.8708 kW, respectively. LBF index of Coot compared to PSO is less than 98.2%, 88.2%, 81.9%, respectively. The maximum, minimum, average, standard deviation, and CPU time of Coot are smaller than those of PSO. So, Coot is one of the promising methods for this problem.
Automatic diagnosis of rice plant diseases using VGG-16 and computer vision Al-Bahra Al-Bahra; Henderi Henderi; Nur Azizah; Muhammad Hudzaifah Nasrullah; Didik Setiyadi
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.v23i6.26975

Abstract

Pathogens are organisms that cause disease in plants. In the case of rice, these pathogens can include fungi, bacteria, nematodes, protozoa, and viruses. This study aims to investigate rice plant diseases using a hybrid system that employs the visual geometry group-16 (VGG-16) architecture and computer vision techniques, alongside various optimization algorithms and hyperparameters. We utilize the convolutional neural network (CNN) architecture of VGG-16 for feature extraction, implementing a process known as transfer learning. Additionally, this research compares different optimization algorithms with the VGG-16 model to identify the most effective optimization for the CNN architecture applied to the tested dataset. The main contribution of this study is the development of a model for identifying rice plant diseases based on data collected using VGG-16 for feature extraction and neural networks for classification with specific parameters. Our findings indicate that the best optimization algorithm is stochastic gradient descent (SGD) with momentum, achieving training and validation loss results of 0.173 and 0.168, respectively. Furthermore, the training and validation accuracies were 0.95 and 0.957. The model’s performance metrics include an accuracy of 95.75, precision of 95.75, recall of 95.75, and an F1-score of 95.73.
E-CityFarm: sustainable small-scale food production integrated fish and crop cultivation R. Wahyu Tri Hartono; Sakinah Puspa Anggraeni; Fajri Habibie Suwanda; Eka Pratiwi; Regina Nur Shabrina; Vina Fitriana
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.v20i5.24095

Abstract

E-CityFarm is an electronic system that can control the parameters needed to grow fish and plants integratedly. It can control temperature, water acidity, and utilizing the neural network method able to count the number and length of fishes also their weight. The weight will be directly proportional to the need for feed. In E-CityFarm, various variables are processed by multitasking, therefore real time operating system (RTOS) is used. RTOS has several advantages in terms of: concurrency, pre-emption, capacity, flash size, synchronization tools, third party software, and convenience. RTOS is real time where in the execution process it will work in parallel for all existing processes according to the time specified. E-CityFarm implements RTOS to improve and maintain the quality of system measurement accuracy, which is expected to help users maintain product quality. In several experiments, the measurement results still have deviations compared to conventional measurements, deviations in measurements for: temperature 0.46%, light intensity 1.935%, 4.93% (3 levels) and weight control 1.995% (98.005% accuracy). Within 14 months the growth of fish and plants seemed to be very controlled, fish and plants grew well, thus E-CityFarm is a feasible system to be developed in areas that have limited land and water.
Design and construction of microcontroller-based exhaust emission measurement equipment for freight transportation A. Irmayani Pawelloi; Muh Huzaifah Ashaba; Hakzah Hakzah; Asrul Asrul; Alauddin Yunus; Muhammad Zainal; Wahyuddin Wahyuddin
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.v24i4.27739

Abstract

Exhaust emissions from motor vehicles, especially freight transportation, are one of the main causes of air pollution in cities. This research aims to develop a portable and low-cost microcontroller-based vehicle emission measurement system. The system uses Arduino Uno with MQ-7 and MQ-2 sensors to detect carbon monoxide (CO) and hydrocarbons (HC) concentrations in real-time, with the measurement results displayed on the liquid crystal displays (LCD) screen. Validation is carried out by comparing the measurement results of the tool with a calibrated gas analyzer as a standard tool. The test results showed an error rate of 0.29%–1.79% for CO and 1.85%–3.84% for HC, as well as sensor stability after about 360 seconds of heating. With its compact design, easy to operate, and low cost, this system has the potential to be an alternative vehicle emission monitoring tool for field inspection and testing activities at vehicle workshops.
Rainfall prediction using support vector regression in Udupi region Karnataka, India Krishnamurthy Nayak; Sumukha K Nayak; Supreetha Balavalikar Shivarama
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.v23i1.26170

Abstract

The hydromatereological processes are examined through analysis of temporal rainfall variability. India is an agricultural land and its economy is mainly dependent on timely rains to produce good harvest. The amount of rainfall varies with regional and temporal variation in distribution. The present research has been conducted to predict the temporal variations in rainfall in Udupi district, Karnataka, India using support vector regression (SVR) model and to validate the findings using actual rainfall records. The data has been collected from the statistical department, Udupi district, Government of Karnataka, India. The prediction accuracy of SVR based rainfall prediction model depends on tuning of algorithmic-based parameters. The parameter optimization is performed using grid search to select the optimal values of hyperparameters. The analysis was performed for the year 2018 based on the training dataset from 2000-2017. It is observed that there is a decreasing trend in total annual rainfall in 2018 and it is concluded that the average yearly rainfall has declined during the years 2018 and 2019. The rainfall predicted results were validated with actual records. The SVR based rainfall prediction model will predicts the rainfall accurately for application in agricultural sector.

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