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Multiobjective framework for congestion management through coordinated scheduling of generation and demand

10.11591/ijece.v16i4.pp1688-1703
Jayesh Priolkar , Govind Kunkolienkar
The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
Volume: 16
Issue: 4
Page: 1688-1703
Publish at: 2026-08-01

Classification of P300 event-related potentials using SNN, CNN and LSTM deep learning models

10.12928/telkomnika.v24i4.27659
Ahlaam; Bright Star University M. Saed , Ibtihal; College of Electrical and Electronics Technology Fawzi Elshami , Ali; University of Benghazi I. Elgayar
Accurate classification of P300 event-related potentials remains challenging due to the complex, non-stationary, and low signal-to-noise characteristics of electroencephalography (EEG) signals in brain-computer interface (BCI) systems. P300-based devices, such as the P300 speller, enable communication for patients with severe motor impairments, including those with locked-in syndrome; however, reliable brain signal classification is still a critical limitation. This study presents a comparative evaluation of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM) networks, and spiking neural networks (SNN), for P300 signal classification. SNNs represent a biologically inspired paradigm that models the discrete, time-dependent behavior of neural spiking activity and offers advantages in terms of energy efficiency and hardware implementability. Experimental results demonstrate that CNN achieved the highest average classification accuracy (81.04%), followed closely by SNN (80.94%) and LSTM (80.60%). Although CNN slightly outperformed the other models, SNNs showed comparable accuracy while requiring fewer training samples and offering potential benefits for low power and real-time BCI systems. These findings highlight the trade-offs between classification performance and computational efficiency and underline the promise of SNNs as an efficient alternative for P300-based BCI applications.
Volume: 24
Issue: 4
Page: 1294-1306
Publish at: 2026-08-01

Decision-tree-based machine learning for detecting coffee agroforestry using SPOT-7

10.12928/telkomnika.v24i4.27747
I Made; IPB University Khrisna Yoga Devandra , I Nengah; IPB University Surati Jaya , Tatang; IPB University Tiryana
This study develops a decision-tree-based machine-learning (ML) approach to identify coffee agroforestry plants using SPOT-7 satellite imagery. The algorithm was developed by examining the combination of image indices derived from SPOT-7 and biophysical variables. Detection using spectral variables is often hampered by spectral similarity between vegetation cover classes. This study found that a ML method that combines spectral and biophysical variables can significantly improve overall accuracy, from 60.4% (using conventional spectral variables alone) to 94% (using integrated spectral-biophysical variables). For detecting and identifying agroforestry coffee classes typically found under tree canopies, the addition of the “land cover” variable published by the Ministry of Environment and Forestry contributes significantly to the classification of agroforestry coffee. Important variables identified in this model are normalized difference vegetation index (NDVI), visible difference vegetation index (VDVI), normalized red-green vegetation index (NRGI), elevation, and land cover.
Volume: 24
Issue: 4
Page: 1307-1319
Publish at: 2026-08-01

Integration of public electric vehicle charging stations into a single gateway platform for net zero emissions goals

10.12928/telkomnika.v24i4.27786
Fajar; STMIK AMIK Bandung Sidiq Arrizal , Shafira; STMIK AMIK Bandung Febriani
The rapid adoption of electric vehicles (EVs) in Indonesia necessitates robust charging infrastructure. However, a major challenge is the highly fragmented network of public EV charging stations (SPKLU). Various operators utilize disparate protocols, resulting in isolated data silos and user range anxiety. This study proposes a nationally scalable application programming interface (API)-based gateway for cross-operator SPKLU integration. The system uses an event-driven internet of things (IoT) push mechanism. A standardized JavaScript object notation (JSON) API ensures interoperability and normalizes heterogeneous SPKLU data into a unified national dashboard. Pilot testing across 20 SPKLU locations in 10 major Indonesian cities validated cross-regional network reliability. The results demonstrate successful real-time status synchronization with a low margin of error (MoE) of 4.65%. To mitigate minor discrepancies caused by temporary network latency, a user interface (UI)-level timestamp transparency feature is proposed. Furthermore, by utilizing dynamic, region specific grid emission factors, the system accurately recorded an aggregated electricity consumption of approximately 3.5 million kWh in June 2025. This translates to an estimated 2.7 million kgCO₂eq in carbon emissions. Ultimately, this centralized platform eliminates information asymmetry for users. It also lays a crucial foundation for future smart grid integration, artificial intelligence (AI)-based demand forecasting, and Indonesia’s net zero emission (NZE) targets.
Volume: 24
Issue: 4
Page: 1113-1120
Publish at: 2026-08-01

Remaining useful life estimation for predictive battery maintenance with improved recurrent singular spectrum analysis algorithm

10.11591/ijece.v16i4.pp1817-1831
Chutipongse Boonyakitmaitree , Suchada Sitjongsataporn
As the global electric vehicle (EV) battery market is projected to reach a valuation of over USD 100 billion by the end of 2026, the demand for sophisticated battery management systems (BMS) has become more critical than ever. Accurate remaining useful life (RUL) prediction is essential for ensuring vehicle safety, optimizing maintenance, and evaluating retired batteries for second-life applications. However, existing prognostic methods often struggle to balance computational efficiency with predictive accuracy, especially during the early stages of battery usage. This research proposes combined weighted similarity-based and recurrent singular spectrum analysis (CWS-RSSA), a hybrid forecasting framework that integrates RSSA with a similarity-based approach through a weighted logistic switching mechanism. The algorithm is designed to be computationally lightweight, making it suitable for resource-constrained BMS hardware. The proposed method was validated using NASA and a large-scale dataset from MIT-Stanford consisting of 124 lithium-ion cells. Experimental results demonstrate that CWS-RSSA is capable of early-stage prediction with a relative error of 19.8%, whereas existing methods are unable to provide predictions. In later stages, once sufficient data becomes available, the algorithm achieves near-perfect accuracy with a negligible relative error on the NASA dataset and an average relative error of only 0.14% across the 124 MIT-Stanford batteries. Furthermore, the algorithm demonstrates robust performance in handling capacity regeneration phenomena. These findings suggest that CWS-RSSA represents a scalable and practical advancement for battery health management, supporting the transition toward a sustainable circular energy economy and providing a reliable foundation for second-life battery certification.
Volume: 16
Issue: 4
Page: 1817-1831
Publish at: 2026-08-01

Teaching analysis of sub-synchronous resonance of thermal power plants using virtual laboratory

10.11591/ijece.v16i4.pp1677-1687
Sugiarto Kadiman , Ratna Kartikasari
The development of a MATLAB/Simulink-based virtual laboratory for studying sub-synchronous resonance (SSR) offers a robust educational platform for analyzing complex power system interactions. Based on the IEEE first benchmark model, this virtual environment provides a safe, efficient, and comprehensive tool for engineering students to study the dangerous interactions between series-compensated lines and turbo-generator shaft systems. The simulation features a 920 MVA, 60 Hz turbo-generator connected to an infinite bus through a series-compensated line. Students can analyze torsional interaction, which result from energy exchange between the electrical network and the mechanical shaft. Higher degrees of series compensation increase the risk of SSR, as the electrical resonant frequency matches the complement of one of the mechanical shaft torsional modes.
Volume: 16
Issue: 4
Page: 1677-1687
Publish at: 2026-08-01

Performance analysis of 5G NR network planning at 2300 MHz in Indonesia: A comparative study of LoS and NLoS scenarios

10.11591/ijece.v16i4.pp1944-1954
Putri Rahmawati , Lia Hafiza , Muhammad Adam Nugraha , Syifa Maliah Rachmawati
This study presents a systematic comparative analysis of 5G new radio (NR) network planning at 2300 MHz for Bandung City, Indonesia, evaluating line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios to determine infrastructure requirements and performance characteristics for urban deployment. Employing the 3GPP TR 38.901 Urban Macro propagation model, link-budget analysis, and Atoll-based simulation for a 167.31 km² urban area, the study evaluates coverage performance under projected 2025 deployment conditions. The results reveal significant differences between propagation scenarios. LoS conditions require 45 gNodeBs for uplink coverage, achieving a synchronization signal reference signal received power (SS-RSRP) of -94.63 dBm and a synchronization signal signal-to-interference-plus-noise ratio (SS-SINR) of 10.87 dB, both categorized as “Good.” In contrast, the NLoS scenario requires substantially denser deployment with 635 gNodeBs, resulting in improved SS-RSRP performance of -71.98 dBm (“Excellent”) and SS-SINR of 12.32 dB (“Good”), along with more uniform coverage distribution. The findings indicate that improved KPI performance and coverage uniformity in NLoS environments can be achieved through substantially increased infrastructure density, highlighting the trade-off between network quality, deployment complexity, and infrastructure cost in urban 5G NR planning.
Volume: 16
Issue: 4
Page: 1944-1954
Publish at: 2026-08-01

Lightweight face recognition based on PCA coupled with PSO-based feature optimization in resource-constrained environments

10.11591/ijece.v16i4.pp2134-2157
Chaimaa Khoudda , Zineb Gotti , Salma Azzouzi , Moulay El Hassan Charaf
We present a streamlined PCA-PSO structure of lightweight face recognition in this paper, which combines principal component analysis (PCA) to reduce dimensions with particle swarm optimization (PSO) to adaptively select features. In contrast to traditional PCA-based models, our model dynamically chooses the most informative subset of the 44 features and results in a small but informative representation. The approach has a high recognition accuracy of 99.99 on ORL and 98.45 on LFW, with low latency (approximately 3 seconds per epoch) and low memory footprint, and without the need to use a graphic card to run it. Extensive computational studies have shown that the proposed pipeline is much faster and consumes less memory than PCA-ACO and lightweight convolutional neural network (CNN) models like MobileNetV2 and Squeeze Net and is best suited to run in real-time in CPU-limited environments. The statistical tests prove the betterment of our method compared to the past methods, where the changes are statistically significant. This research offers an efficient, simple, yet scalable alternative to deep learning-based recognition systems, especially when embedded integration is a key requirement, by offering rapid convergence, adaptive feature selection, and compact representation.
Volume: 16
Issue: 4
Page: 2134-2157
Publish at: 2026-08-01

A new diagnostic method based on support vector machine for short circuit winding faults in induction motors

10.11591/ijece.v16i4.pp1735-1754
Hicham Zaimen , Tawfik Thelaidjia , Makhlouf Chouki , Abdurrahman Ünsal
Inter-turn short circuits (ITSCs) in induction motor (IM) windings are among the most critical and frequent faults in industrial environments, as they can rapidly evolve into severe damage, leading to unplanned downtime and costly maintenance. To enhance the reliability of IMs, this paper proposes a machine learning–based diagnosis method dedicated to ITSC failures. The developed diagnostic tool combines a support vector machine (SVM) classifier with Fisher’s ratio (FR)-based feature selection. The proposed framework uses experimentally acquired current signals under healthy conditions and five ITSC fault severity levels (1%–5%), evaluated across four load conditions (25%, 50%, 75%, and 100%). Each signal is segmented into 200 non-overlapping segments (500 samples each), from which nine time-domain features are extracted to capture fault-related characteristics. These features are then used for training and testing a SVM classifier capable of distinguishing between healthy states and levels of severity of ITSC faults. To optimize the classification process, the Fisher’s ratio (FR) algorithm is employed to select the most informative features while discarding those with low relevance. Our findings unveiled that the proposed hybrid FR-SVM-based diagnosis achieves high diagnostic accuracy ranging from 99.54% to 100%. Furthermore, the outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
Volume: 16
Issue: 4
Page: 1735-1754
Publish at: 2026-08-01

Bone strength analyzer and monitoring device for lower limb external fixation

10.11591/ijece.v16i4.pp1778-1791
Devin Babu , Waheb A. Jabbar , Muhammad Hisyam Rosle , Noorazliza Sulaiman , Mohd Amir Shahlan Mohd Aspar , Abdul Nasir
The procedure for external fixator removal in lower limb fractures is typically based on radiographic data, subjects’ patients to ionising radiation, and provides minimal real-time information about the healing process. This project suggests a sensor-based Internet-of-Things apparatus, which will measure bone strength during the recuperating process based on load cells, HX711 amplifiers, and a Wemos ESP8266 microcontroller. The system provides real-time feedback in the form of LED indicators and a buzzer, whereas remote monitoring is supported by the Blynk dashboard. Measurement accuracy of over 90% was carried out as per the experimental validation conducted under the simulation of various loads (3-9 kg) and with clear stage indicators of critical, partial, and full recovery. The device offers continuous monitoring and objective bone healing evaluation with no radiation in comparison to conventional imaging. The limitation of the study is the limited range of loads in prototype testing, which could affect accuracy in particular situations. However, the results represent the possible clinical relevance of introducing real-time biomechanical surveillance into the process of fracture treatment, hence contributing to safer rehabilitation and more reasonable decisions related to the fixator removal.
Volume: 16
Issue: 4
Page: 1778-1791
Publish at: 2026-08-01

Comparative performance analysis of lightweight face identification algorithm

10.11591/ijece.v16i4.pp2042-2060
Wuyun Wang , Suchada Sitjongsataporn
With the wide application of face recognition in resource-constrained scenarios like mobile and embedded devices, lightweight algorithms have become a research focus, but existing studies lack multi-dimensional, scenario-based performance comparisons. This paper studies the performance evaluation and application adaptation of lightweight face recognition algorithms, innovatively builds a scenario-based evaluation system, verifies the performance improvement of combining traditional algorithms with MobileNet, and constructs an efficient, stable and low-cost system. It elaborates on face recognition principles, including key links of face detection, feature extraction and matching, introduces traditional algorithms such as Eigenfaces, Fisherfaces and LBPH, and focuses on MobileNet’s characteristics: reducing computation and parameters via depthwise separable convolution, and adjustable width and resolution. Four comparative experiments verify the "traditional algorithms + MobileNet" hybrid strategy. Results show the combination achieves 98.1% accuracy, 4.3 percentage points higher than single MobileNet; LBPH + MobileNet balances performance and resource consumption best, with 110MB memory, 40% CPU usage and 315ms processing time. The hybrid strategy improves accuracy and efficiency in different scenarios, aiming to provide a scientific basis for the engineering application and subsequent optimization of lightweight face recognition algorithms, and supporting algorithm selection and performance improvement in resource-constrained scenarios.
Volume: 16
Issue: 4
Page: 2042-2060
Publish at: 2026-08-01

SS-ANFIS: a semi-supervised neuro-fuzzy model for offline signature verification

10.11591/ijece.v16i4.pp1985-1997
Sadly Syamsuddin , Jufri Jufri , Suci Rahma Dani Rachman , Suryani Suryani , Wilem Musu , Salmiati Salmiati , Yesycha Arun Mangopo
Signature verification remains a critical authentication mechanism in academic and administrative environments, yet manual verification is vulnerable to forgery and subjective judgment. This study proposes SS- ANFIS, a semi-supervised neuro-fuzzy model for offline signature verification under limited labeled data conditions. The proposed model integrates pseudo-label-based self-training into a Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system (ANFIS). Static image-based features were extracted from offline signature images and transformed using principal component analysis (PCA) before classification. Experiments were conducted on 800 offline signature samples collected from Dipa University Makassar, consisting of 400 genuine and 400 forged signatures. The proposed model achieved an accuracy of 90.5%, precision of 98.8%, recall of 82.0%, and F1-score of 90.0%. The high precision indicates that SS- ANFIS is effective in minimizing false positive predictions, which is important for academic document verification. The results show that the proposed model provides a practical, interpretable, and computationally efficient approach for offline signature verification, particularly in institutional settings with limited labeled data.
Volume: 16
Issue: 4
Page: 1985-1997
Publish at: 2026-08-01

The life-cost cycle-based sizing of complementary energy storage technologies in DC microgrids

10.11591/ijece.v16i4.pp1724-1734
Dunya Sh. Wais , Huda A. Abbood , Radhi Sehen Issa
Hybrid energy storage system (HESS) configurations have the potential to mitigate the detrimental effects of photovoltaic power generation oscillations on DC microgrid safety and reliability. The economic efficiency of HESS can be improved through the utilization of batteries and their complementary attributes through an energy management strategy. This will allow for the full utilization of the benefits of superconducting magnetic energy storage (SMES), such as high efficiency, lossless energy storage, elevated power density, and rapid response. The battery-SMES HESS is subject to a life cycle cost (LCC) model, along with its associated constraints. System expenditures can be drastically cut by optimizing the HESS capacity layout. The goal function is the lowest LCC, presuming that the power demands of the system are met. Particle swarm optimization takes acceleration into account when designing the capacity of the system. In order to prove that the suggested method of configuring capacity works, a microgrid model is created and tested using numerical data.
Volume: 16
Issue: 4
Page: 1724-1734
Publish at: 2026-08-01

ACLiMA: an IoT-based autonomous flood monitoring and mitigation system with database-driven threshold control

10.11591/ijece.v16i4.pp1867-1875
Hendi Santoso , Rizqan Khairan Munandar , Apriansyah Apriansyah , Andi Ihwan , Putri Yuli Utami
Urban flooding remains a critical challenge in densely populated and low-lying areas, where delayed response and limited monitoring infrastructure significantly increase flood risks. Existing flood monitoring systems are typically limited to passive observation or fixed-threshold alerting without integrated autonomous mitigation and flexible configuration. This study proposes autonomous control logic for IoT-based monitoring and actuation (ACLiMA), an IoT-based autonomous flood monitoring and mitigation system using a database-driven threshold control approach to enable real-time monitoring and immediate response. The system integrates ultrasonic water-level sensing, centralized database management, web-based visualization, and autonomous pump actuation within a unified architecture. Flood conditions are classified into four operational states—SAFE, CAUTION, DANGEROUS, and FLOOD—based on configurable threshold values stored in the database, allowing dynamic adjustment without firmware modification. Experimental results demonstrate stable system integration with deterministic control behaviour and low response latency between sensing and actuation, enabling timely pump activation during critical conditions. The system also provides multi-temporal visualization for monitoring and analysis, while the database-driven configuration enhances flexibility, scalability, and ease of deployment across different environments. Overall, the proposed system offers a low-cost, modular, and autonomous solution for real-time flood mitigation, contributing to the transition from passive monitoring toward active mitigation in smart city and resource-constrained urban applications.
Volume: 16
Issue: 4
Page: 1867-1875
Publish at: 2026-08-01

Evaluation of the efficiency of delay tolerant network routing protocols for smart environment development in Makassar

10.12928/telkomnika.v24i4.27102
Abdul; Universitas Handayani Makassar Latief Arda , Symsu; Universitas Handayani Makassar Alam , Agussalim; Universitas Pejuang Nasional Veteran Surabaya Agussalim , Matalangi; Universitas Kristen Indonesia Paulus Matalangi
Delay tolerant network (DTN) has become a promising communication paradigm for internet of things (IoT)-based smart environments, where intermittent connectivity and heterogeneous node mobility challenge reliable data delivery. However, selecting an appropriate DTN routing protocol remains difficult because existing protocols offer different trade-offs among delivery ratio, latency, communication overhead, hop count, and energy consumption. Although many studies have evaluated DTN routing protocols, few have examined their performance under heterogeneous urban mobility conditions representing emerging smart cities such as Makassar. This study compares four DTN routing protocols Epidemic, probabilistic routing protocol using history of encounters and transitivity version 2 (PRoPHETv2), spray and wait, and MaxProp using The ONE Simulator with a Makassar-inspired mobility scenario involving 50–125 nodes over a 12-hour simulation. Performance was evaluated using delivery ratio, average latency, overhead ratio, average hop count, and estimated energy consumption. The results show that MaxProp achieved the highest delivery ratio (0.69 at 125 nodes), whereas spray and wait consistently produced the lowest latency (≈118 s), overhead ratio (< 45,000), and average hop count (≈2). In contrast, epidemic generated excessive overhead and energy consumption because of uncontrolled message replication. These findings indicate that spray and wait is the most resource-efficient protocol, while MaxProp is preferable for applications prioritizing delivery reliability.
Volume: 24
Issue: 4
Page: 1143-1156
Publish at: 2026-08-01
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