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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 32 Documents
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Search results for , issue "vol 24, no 5: october 2026" : 32 Documents clear
NiteTron: a novel nighttime bunaken sea turtle species detection Muhamad Dwisnanto Putro; Jane Litouw; Abdul Haris Ontowirjo; Sherwin Reinaldo U. Sompie; Hebron Prasetya
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27678

Abstract

Automated monitoring is essential for sea turtle conservation, and vision-based algorithms offer a non-intrusive approach to observing turtles without disturb ing their natural habitat. However, nighttime conditions introduce significant challenges, as low illumination severely reduces the visibility of discriminative features. This limitation requires robust feature extraction to capture the lim ited important features. Recent advances in computer vision, such as you only look once (YOLO) 11-nano, enable real-time detection and allow optimization under challenging conditions. In this study, we propose NiteTron, a nighttime sea turtle species detector that uses a lightweight architecture with enhanced feature refinement. The network modifies YOLO11-nano to create a more ef ficient variant, YOLO11-pico. To improve detection in low-light scenarios, we introduce a strong feature modulation (SFM) module. This module enhances attention to relevant features by expanding the receptive field in the final back bone stage. We also incorporate a sparse separated convolution (S2C) module to extract key information efficiently while balancing accuracy and computational cost. The proposed model achieves high precision with fewer parameters and lower giga floating-point operations per second (GFLOPs) than YOLO11-nano. Experiments demonstrate superior performance, achieving 0.682 mean average precision (mAP)@0.5:0.95 for nighttime sea turtle detection. Inference tests also confirm efficient central processing unit (CPU) deployment, reaching 21.18 frames per second (FPS) on a desktop and 2.65 FPS on an edge device.
Engineering orchestration: adaptive and human-centred design for complexity, collaboration, and resilience Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.3777

Abstract

Engineering design is undergoing a profound transformation as electrical and computer engineering systems become increasingly interconnected, intelligent, and embedded within dynamic socio-technical environments. Building upon systems thinking, future engineering can no longer rely solely on deterministic optimization or fixed design assumptions but must instead embrace adaptive, resilient, and human-centred approaches capable of responding to uncertainty and continuous change. This editorial argues that these developments fundamentally redefine engineering design as the orchestration of adaptive socio-technical systems that continuously align technological intelligence, human expertise, resilience, and lifecycle evolution. Adaptive engineering extends traditional design principles through continuous learning, feedback, and system reconfiguration, while human centred engineering ensures that technological capabilities remain aligned with human judgement, values, and operational needs. Resilience further enables engineering systems to anticipate, absorb, recover from, and adapt to evolving disturbances across increasingly complex operating environments. Engineering excellence therefore depends not merely on optimizing technological performance but on orchestrating these complementary capabilities into coherent engineering ecosystems capable of generating enduring value. This evolving perspective establishes a conceptual foundation for the future of electrical and computer engineering, where engineering orchestration enables adaptive design, human collaboration, resilient innovation, and ultimately responsible and sustainable engineering practice.
Adaptive fuzzy proportional integral derivative control for azimuth rotational positioning system Mufid Ridlo Effendi; Rina Mardiati; Muhammad Bagja Nasrulloh; Aan Eko Setiawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27906

Abstract

This paper presents an adaptive fuzzy logic-based proportional integral derivative (PID) control strategy for an azimuth rotational positioning system characterized by nonlinear angular dynamics. Conventional fixed gain PID controllers often experience performance degradation when operating across varying error regions, resulting in slower responses and residual steady-state errors. To address this limitation, a region-based fuzzy gain scheduling mechanism is proposed to dynamically adjust the proportional (??), integral (??), and derivative (??) gains according to the instantaneous angular error. The proposed controller is implemented on a laboratory-scale embedded platform consisting of a stepper motor actuator, potentiometer-based angular feedback, and an Arduino Nano controller. Experimental evaluations were conducted at reference angles of 45°, 90°, 135°, and 180° and compared with a conventional PID controller. Results demonstrate that the adaptive fuzzy–PID controller completely eliminates steady-state error at all tested setpoints and reduces rise time by up to 56.7%, depending on the operating condition. The adaptive gain adjustment enables faster transient response for large angular deviations while maintaining stable convergence near the setpoint. These findings demonstrate that the proposed embedded region-based adaptive fuzzy–PID approach provides an effective and practical solution for improving positioning accuracy and dynamic performance in rotational positioning systems.
Development and assessment of grid-tied photovoltaic system of government facility in Bireuen Regency, Aceh, Indonesia, using system advisor model Nelly Safitri; Musbar Musbar; Busra Busra; Amri Amri; Yuli Anisah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27842

Abstract

This research examines the technical and economic viability of a 275 kWp hybrid photovoltaic (PV) system intended for a government facility in Bireuen, Aceh. It specifically addresses the regulatory changes in Indonesia set in 2024, which eliminate net metering credits. The research was conducted to determine if a hybrid system that combines solar panels with battery storage could maintain its financial viability and structural stability in light of new circumstances demanding independence from the electrical grid. The employed methodology combined structural engineering with techno economic modelling. An analysis of the building’s portal frame was conducted using the demand-capacity ratio (DCR) to ensure compliance with wind load requirements of up to 40 m/s. Performance simulations were executed using the system advisor model (SAM) to evaluate energy outputs against a 240 kVA load profile. Key findings suggest that the system is both structurally sound and economically viable. The findings indicate that the proposed 275kW grid-connected PV system can effectively support the building’s 240kVA load capacity, regardless of battery storage. Moreover, the financial assessment revealed a positive net present value (NPV) of approximately IDR 3.1 million, an internal rate of return (IRR) of 9.7%, which has a relatively short payback period of 9.3 years, suggesting that the project is financially sound.
Improving PaddleOCR optical character recognition for food composition labels using hybrid SymSpell and LayoutLM correction Ahmad Wahyu Rosyadi; Siti Ma’shumah; Muhammad Qomaruz Zaman
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27607

Abstract

A high level of text recognition accuracy is crucial for optical character recognition (OCR) applications, particularly those designed to extract information from food composition labels. However, packaging materials often introduce distortions, reflections, or irregular printing, which lead to recognition errors and word concatenation. To mitigate this issue, this study proposes a novel post-processing system that integrates PaddleOCR with the spelling correction (SymSpell) algorithm and layout language model (LayoutLM)-guided bigram-based word segmentation to enhance OCR accuracy. The implementation begins with capturing a composition label image using a smartphone camera, followed by initial text extraction performed by PaddleOCR. Consequently, SymSpell corrects misspelled tokens through a domain-specific food and beverage lexicon, while LayoutLM leverages spatial and contextual information to separate concatenated words. The performance was evaluated using the character error rate (CER) metric across four packaging surface categories: flat, curved, reflective, and textured. The results demonstrate consistent improvements across all categories, achieving an average CER of 0.0991, with the greatest reduction on flat surfaces (from 0.0885 to 0.0659). The experiments show that the system is robust across label conditions, lightweight for real-time use, and domain-aware. Its model-agnostic design adds flexibility, enabling effective post-OCR correction across different engines and improving readability for real-world food label applications.
Development of a T-instrument for soil resistivity measurement in agricultural applications Tedy Setiawan; Satria Bijaksana; Ulvienin Harlianti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27943

Abstract

Geoelectric measurements are widely applied in natural resource exploration and groundwater studies. In this work, a prototype resistivity meter, referred to as the T-instrument, was developed for soil resistivity measurements in agricultural applications. The system is designed with an emphasis on portability, ease of operation, and field applicability. Measurements were conducted using the Wenner electrode configuration, and system performance was evaluated through comparison with a commercial resistivity meter (OYO McOhm). The results show a strong correlation coefficient of 0.96 between the two instruments, indicating good agreement. The instrument performs reliably within typical agricultural soil resistivity ranges and targets shallow soil depths relevant to crop root zones. However, further refinement is required to improve performance under high-resistivity conditions and optimize power efficiency.
Hybrid neural network-augmented model predictive control for differential-drive robot tracking Taoufik Belkebir; Hicham Belkebir; Anass Mansouri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27870

Abstract

Nonlinear model predictive control (NMPC) is widely adopted for mobile robot trajectory tracking, yet the interaction between transcription methods and learning-based enhancements remains unstudied for differential-drive platforms. This paper presents a unified CasADi/Interior Point OPTimizer (IPOPT) evaluation of fourth-order Runge-Kutta (RK4) multiple shooting (MS) and Hermite-Simpson (HS) direct collocation on a TurtleBot3 Burger across ten scenarios spanning five trajectory families, model mismatch, and sensor noise. A ?-ambiguity in the standard ???2 (??) heading cost is identified and corrected, reducing figure-eight tracking error by approximately 95%. RK4 MS achieves superior baseline accuracy in 9 of 10 experiments (? < 0.01). Three hybrid neural approaches are evaluated: warm-starting reduces solve time by 16% with no accuracy loss; adaptive transcription selection outperforms the baseline in 3 of 10 experiments; and neural approximate control achieves sub-millisecond inference but degrades on unseen trajectories (51%-64% fallback rate). A generalization hierarchy emerges: solver-in-the-loop methods generalize without measurable degradation, whereas solver-replacement methods require trajectory-specific training. These findings suggest that solver involvement is a key factor influencing generalization in learning-augmented predictive control. All results are statistically validated across 500 simulations.
Predictive and fault-tolerant virtual machine migration for energy-efficient cloud data centers Rakshan G. K.; P. Vinothiyalakshmi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27706

Abstract

Virtual machine (VM) migration is a key mechanism for improving energy efficiency, service continuity and reliability in cloud data centers. However, conventional migration strategies are largely reactive and fail to account for workload fluctuations and potential failures, often resulting in inefficient resource utilization and increased service-level agreement (SLA) violations. This paper proposes the predictive VM migration manager (PVMM), a unified framework that integrates workload forecasting and failure prediction for proactive migration control. PVMM combines a gated recurrent unit (GRU)-based model for short-term resource prediction with a machine-learning-based failure predictor to identify high-risk VMs. An adaptive dynamic threshold based on energy consumption (ADT-EC) detects host overload conditions, while an improved energy-aware best-fit (IEABF) heuristic selects optimal migration targets. Experimental results demonstrate that PVMM reduces unnecessary migration, improves SLA compliance and enhances overall energy efficiency and system reliability. These findings highlight the effectiveness of predictive, multi-objective migration strategies for next-generation cloud data center management.
Rainfall detection on a tropical island using a communication satellite downlink signal and artificial intelligence models Raden Yudha Mardyansyah; Budhy Kurniawan; Santoso Soekirno; Danang Eko Nuryanto; Ferry Budhi Susetyo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.26012

Abstract

The development of rainfall estimates based on satellite links in several high latitude regions shows good results. However, its development for tropical regions is still very less promising, leaving the question of which method is worth using. In this work, we investigate artificial intelligence (AI) models by utilizing the Ku-band satellite downlink signal for rainfall detection on a tropical island. Long-term evaluation of all the models is performed using one year of rainfall intensity data. The findings of this study demonstrate that one-dimensional convolution neural network (1DCNN), long short-term memory (LSTM), and random forest (RF) models can effectively identify rainfall occurrences on tropical islands, with the area under the receiver operating characteristic curve (ROC) value of 0.84, 0.84, and 0.83, respectively. These three models consistently provide better accuracy in detecting the region’s diverse rainfall patterns throughout the year. The 1DCNN has better performance compared to the other models, achieving accuracy values of 0.76 and 0.82 during the periods of lowest and highest rainfall, respectively. Considering the presence of numerous potential mislabeled rain samples in the dataset, this result is excellent. In addition, the utilization of higher resolution rainfall intensity data can be a great potential for real-time rainfall monitoring in the tropical region.
Constrained finite-control-set predictive control of an H-bridge direct current motor drive Bouiri Abdesselam; Kendzi Mohammed; Boughazi Othmane; Drauoi Abdelghani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27909

Abstract

This paper presents a constrained finite-control-set model predictive control (FCS-MPC) scheme for an H-bridge converter-fed direct current (DC) motor drive, aimed at fast dynamic response and low steady-state armature-current ripple. By selecting the converter switching state directly at each sampling instant, the method removes the separate pulse-width modulation (PWM) stage used in conventional cascaded proportional-integral (PI) control and embeds the armature-current limit directly in the cost function. When the predicted armature current exceeds the predefined limit, a penalty term drives the optimizer to choose the sw itching state that returns the current within safe bounds while preserving speed tracking. The controller is evaluated in simulation under fixed-speed reference tracking, variable-speed reference tracking, and a step load-torque disturbance. The results show fast settling, low steady-state speed error, and bounded current during transients; under the idealized simulation conditions the steady-state armature-current total harmonic distortion (THD) is 0.21 %, indicating low current ripple for the studied operating points. The present study is limited to simulation; experimental validation and a quantitative comparison against conventional PI-PWM control are identified as future work.

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