TELKOMNIKA (Telecommunication Computing Electronics and Control)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control) ISSN: 1693-6930, e-ISSN: 2302-9293 is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD). The aim of this journal is to publish high-quality articles that highlight the latest significant developments in the fields of electrical and electronics engineering, as well as computer science. Its scope encompasses applications related to telecommunication, computing, electrical and electronics, instrumentation, and control. This journal holds accreditation from the National Journal Accreditation (ARJUNA) under Indonesia's Ministry of Education, Culture, Research, and Technology. It has been granted First Grade status, or "Peringkat 1", as per Decree No. 169/E/KPT/2024, dated 1 October 2024. This recognition emphasises the journal's commitment to upholding high academic standards and promoting knowledge advancement within its discipline. The quality and credibility of the articles published in its pages assure researchers and scholars of its integrity. First published in 2003, the journal initially operated as a bimonthly publication and has been releasing six issues per year since 2018. <a style="background-color: #fff; border-bottom: 0 solid transparent; border-left: 0 solid #495e57; border-radius: 0; border-right: 2px solid #495e57; border-top: 0 solid transparent; color: #45474b; display: inline-block; font-family: Roboto,Tahoma,Verdana,Segoe,sans-serif; font-size: 12px; font-weight: 400; mso-border-alt: none; padding-bottom: 0; padding-top: 0; text-align: center; text-decoration: none; width: auto; word-break: keep-all;" onmouseover="this.style.backgroundColor="#0070BB",this.style.color="#ffffff"" onmouseout="this.style.backgroundColor="#ffffff",this.style.color="#45474B"" href="/index.php/TELKOMNIKA/about/submissions#onlineSubmissions" target="_blank">Submit Now<a style="background-color: #fff; border-bottom: 0 solid transparent; border-left: 0 solid #495e57; border-radius: 0; border-right: 2px solid #495e57; border-top: 0 solid transparent; color: #45474b; display: inline-block; font-family: Roboto,Tahoma,Verdana,Segoe,sans-serif; font-size: 12px; font-weight: 400; mso-border-alt: none; text-align: center; text-decoration: none; width: auto; word-break: keep-all;" onmouseover="this.style.backgroundColor="#0070BB",this.style.color="#ffffff"" onmouseout="this.style.backgroundColor="#ffffff",this.style.color="#45474B"" href="https://www.youtube.com/watch?v=B9lB-4sZNI4" target="_blank">How to Register<a style="background-color: #fff; border-bottom: 0 solid transparent; border-left: 0 solid #495e57; border-radius: 0; border-right: 2px solid #495e57; border-top: 0 solid transparent; color: #45474b; display: inline-block; font-family: Roboto,Tahoma,Verdana,Segoe,sans-serif; font-size: 12px; font-weight: 400; mso-border-alt: none; text-align: center; text-decoration: none; width: auto; word-break: keep-all;" onmouseover="this.style.backgroundColor="#0070BB",this.style.color="#ffffff"" onmouseout="this.style.backgroundColor="#ffffff",this.style.color="#45474B"" href="https://www.youtube.com/watch?v=hQD9-HeNy1k" target="_blank">How to Submit
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Beyond reductionism: systems thinking for the next generation of electrical and computer engineering
Classical electrical and computer engineering has achieved remarkable progress through reductionist methodologies that decompose complex systems into manageable, analyzable, and optimizable components. While this paradigm remains indispensable for scientific rigor and engineering design, it is increasingly challenged by contemporary systems characterized by interconnectedness, dynamic interactions, and multi-scale complexity. This editorial argues that future engineering requires extending, rather than replacing, reductionist thinking with systems thinking capable of capturing interdependence, emergence, resilience, and holistic system behaviour. Beyond component-level optimization, engineering must increasingly consider interactions among technological, human, environmental, and societal dimensions that collectively shape system performance and long term sustainability. Systems thinking therefore provides a complementary paradigm for understanding how complex engineering systems adapt, evolve, and generate behaviours that cannot be inferred solely from individual subsystems. This perspective redefines electrical and computer engineering as an integrated socio-technical discipline in which analytical precision is combined with systemic understanding to address increasingly complex real-world challenges. Moving beyond reductionism does not diminish the value of analytical methods but expands their scope within broader interconnected contexts. This paradigm shift establishes the conceptual foundation for the subsequent evolution toward adaptive, human centred, and ultimately responsible engineering.
Design and simulation of a compact patch antenna for breast cancer tumors detection in the Wi-Fi band
The escalating need for safe, low-cost, and reliable medical diagnostic tools has propelled research into microwave imaging (MWI) for breast cancer detection. This study aims to design and evaluate, via simulation, a compact microstrip patch antenna operating in the 2.4 GHz wireless fidelity (Wi-Fi) band as a preliminary sensing element. The antenna, with overall dimensions of (14×14×3.1) mm³, is designed on a Rogers RT/duroid 5880 substrate and superstrate (ε_r= 2.2). To evaluate its detection capabilities, the antenna is placed over a simplified three-layer human breast phantom (skin, fat, and fibro-glandular tissue). Using Ansys high frequency structure simulator (HFSS) software, three scenarios were simulated: a healthy breast, a breast with a single tumor, and a breast with two tumors. The simulation results indicate measurable shifts in electromagnetic parameters used as diagnostic indicators. The presence of tumors caused a shift in the resonant frequency (from 2.42 GHz to 2.46 GHz) and a variation in the reflection coefficient (S11) magnitude. Specifically, the antenna demonstrates high sensitivity to dielectric loading changes, identified by a performance framework that correlates frequency shifts to tumor presence, while maintaining specific absorption rate (SAR) values within safety limits. This work demonstrates, through simulation, the feasibility of using a compact Wi-Fi band patch antenna for breast tumor detection, providing a foundation for future experimental development of portable diagnostic systems.
An improved harvested energy management mechanism for wireless sensor networks
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.
Integration of public electric vehicle charging stations into a single gateway platform for net zero emissions goals
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.
Design and evaluation of a simple load balancing prototype using the round robin algorithm in local networks
Load balancing plays a crucial role in ensuring efficient workload distribution and maintaining stable performance in web service systems. This study presents the design and experimental evaluation of a round robin–based load balancing system implemented in a multi-client local area network (LAN) environment using NGINX as a centralized controller. The system consists of three physical machines, comprising one load balancer and two backend servers hosting identical web applications. Multiple clients generate simultaneous hypertext transfer protocol (HTTP) requests, which are distributed alternately to the backend servers using the default round robin mechanism provided by NGINX. Experimental evaluation was conducted under three workload scenarios of 50, 100, and 200 concurrent requests. The results show that the round robin algorithm consistently distributes requests evenly between the backend servers. The average response time increased from approximately 110 ms at 50 requests to 165 ms at 100 requests and 290 ms at 200 requests, indicating stable performance under light to moderate load conditions. These findings demonstrate that the proposed system is lightweight, modular, and easy to deploy in resource limited environments. The implementation is particularly suitable for campus-scale networks and small institutional settings, serving as a practical platform for local server deployment, academic applications, and experimental learning in networking and distributed systems.
Experimental validation of a low-cost microcontroller-based rack-level thermal control prototype
The rapid growth of data centers (DCs), driven by digital transformation and the increasing adoption of artificial intelligence (AI), has intensified challenges related to thermal management and operational reliability. Cooling systems account for a substantial portion of total energy consumption and often show limited effectiveness in mitigating localized hotspots and dynamic temperature variations in high-density server environments. This study presents the development and experimental validation of a low-cost, microcontroller-based (MCU-based) localized thermal control system. The proposed architecture integrates a temperature sensor, an Arduino-based control unit, pulse-width modulation (PWM) driven fan actuation, and Ethernet communication for remote monitoring. The system was implemented in a standard 19-inch rack under controlled laboratory conditions using a simulated thermal load. Experimental results, based on the average of five independent tests, demonstrated that combined operation of the prototype with rack ventilation reduced the cooling time from 45 °C to 40 °C to 50 ± 2 s, compared to approximately 5 minutes with rack ventilation alone and more than 12 minutes under natural convection. The corresponding cooling rates were 0.10 °C/s, 0.015 °C/s, and 0.007 °C/s. These results indicate that simple, distributed thermal control strategies can effectively mitigate localized overheating and support rack-level thermal stability in data center microenvironments.
Evaluation of the efficiency of delay tolerant network routing protocols for smart environment development in Makassar
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.
NLP-driven hate speech detection on TikTok: a case study from UIN Sunan Ampel Surabaya
This study examines hate speech detection in TikTok comments using natural language processing (NLP) techniques within the student community of UIN Sunan Ampel Surabaya. A dataset of 10,000 comments associated with the hashtag #PBAKUINSA2023 was analyzed using a lexicon-based sentiment analysis approach implemented through the TextBlob library, combined with Indonesian text preprocessing techniques, including tokenization, normalization, stopword removal, and stemming using the Sastrawi library. The results indicate that the proposed approach achieved an accuracy of 0.85, with precision of 0.88, recall of 0.83, and an F1-score of 0.854. Most comments were classified as neutral, while 31.8% were positive, and only a small proportion were negative. These findings suggest that discussions related to campus activities tend to be neutral or supportive. However, the findings also reveal that sentiment polarity does not always directly correspond to hate speech, as certain harmful expressions may appear neutral in lexicon-based analysis. This limitation highlights the need for more context-aware approaches. Overall, the proposed method provides an efficient solution for monitoring online discourse in academic environments.
Behavioral fingerprints: driver profiling using transformer models on next generation simulation trajectory data
Characterizing individual driver behavior is essential for advancing intelligent transportation systems (ITS) and autonomous vehicle safety. While deep learn ing models excel at macroscopic traffic prediction, individual driving styles are often aggregated away. This paper addresses this gap by proposing a novel, weakly supervised transformer framework for driver behavior profiling using high-resolution next generation simulation (NGSIM) US-101 trajectory data. We extract microscopic behavioral features including acceleration, lane change dynamics, and headway management from 30-second observation segments. A transformer encoder learns complex temporal dependencies to classify drivers into ’aggressive’ and ’normal’ profiles, achieving a 97% F1-score on proxy labeled segments. Crucially, these “proxy labels” are derived from heuristic statistics, meaning the model is trained to learn the mapping from sequences to these behavioral indicators rather than identifying objective aggression. Our methodology enables the creation of precise “behavioral fingerprints” that cap ture individual driving nuances. These insights are vital for developing adaptive ITS that anticipate traffic stability issues and enhance autonomous vehicle safety by predicting human intent.
Predictive safety helmet for miners using internet of things and artificial intelligence
Mining is still responsible for many deaths since mines have dangerous environmental conditions including mine collapses, gas emissions, and high temperatures. However, traditional helmets do not provide adequate protection; besides, they cannot analyze miners’ health as well as the environmental hazards. In order to solve this issue, this work presents a predictive safety helmet equipped with several sensors and means of communication. Specifically, the helmet comprises a micro-electro mechanical systems (MEMS) accelerometer for vibration monitoring, a gas sensor for detecting the presence of harmful gases, a heartbeat sensor for assessing workers’ well-being, and a temperature sensor for monitoring the environmental parameters. Additionally, the device is provided with a global positioning system (GPS) module for location determination and a global system for mobile (GSM) module for transmitting alert notifications in case of emergency situations. The collected data is analyzed on an internet of things (IoT)-based system; any signs of danger cause alerts to be sent immediately.
Methods of finding the maximum common transitive subgraph: experimental comparison
The problem of finding a maximum common subgraph (MCS) in a graph has broad applications in practical domains. However, certain scenarios require subgraphs with special properties, such as transitivity, that must be kept during building the subgraph. We formally define the concept of a transitive subgraph, investigate its properties. We study four different algorithms for finding the max imum common transitive subgraph (MCTS), compiled a list of tests aim at com paring graphs after making various changes and evaluated their accuracy and efficiency on a set of test cases. Benchmarking on 64 tests ranks the algorithms by scalability and accuracy: branch matching is the most scalable (> 1000 ver tices) and accurate (F1: 0.9907). MCS tree search is viable for graphs of up to ∼ 250 vertices (F1: 0.9752). Backtracking is limited to < 30 vertices (ac curacy: 0.5625), and brute-force is only feasible for graphs with ≤ 10 vertices, despite its high accuracy (0.9375). We discuss the advantages and disadvantages of each method, the test cases where each method demonstrates a non-optimal MCTS,identify the classes on which the methods work correctly and found that the branch matching method based on the longest common subsequence (LCS) algorithm performed the best.
Interpretable machine learning for digital soil pH mapping using an optimized AdaBoost algorithm
Soil pH is a fundamental parameter determining nutrient availability, microbial activity, and crop productivity. Unlike previous studies that often prioritize prediction accuracy over explainability, this study proposes an interpretable machine-learning framework integrating hyperparameter optimized adaptive boosting (AdaBoost) with Shapley Additive exPlanations (SHAP) to unravel the spatial drivers of soil pH. A systematic workflow was implemented to evaluate a diverse set of algorithms, followed by Bayesian optimization to fine-tune the best-performing models. The results demonstrated that the optimized AdaBoost model yielded the largest performance improvement (~7.5%), achieving excellent accuracy on independent test data with a coefficient of determination (R²) of 0.817 and a mean absolute error (MAE) of 0.293. Furthermore, SHAP analysis identified iron (Fe) and calcium carbonate (CaCO₃) as the most influential predictors, revealing that Fe exhibits a strong inverse relationship with pH, while CaCO₃ shows a positive association. This framework successfully balances high predictive accuracy with pedological interpretability, offering a robust tool for digital soil mapping and precision agriculture.
Real-time anomaly detection system using best performed machine learning model
Effective anomaly detection is critical for protecting organizational networks against increasingly sophisticated cyber threats. However, most machine learning-based intrusion detection models are developed and validated using public benchmark datasets, which may not reflect the operational characteristics, traffic behavior, and threat patterns of real institutional networks. In the case of Umalusi, there is currently no anomaly detection model customized and validated using Umalusi-specific network traffic, creating a practical gap in deployable cybersecurity capability. This study proposes a hybrid machine learning framework tailored to support accurate, efficient, and operationally relevant anomaly detection. Using knowledge discovery in databases (KDD) process, network traffic data were collected and pre-processed through normalization, label encoding, missing value treatment, and dimensionality reduction using principal component analysis (PCA). The 16 hybrid models integrating unsupervised anomaly detection with supervised classification were implemented and comparatively evaluated. Experimental findings indicate that the density-based spatial clustering of applications databasescan (DBSCAN) + random forest (RF) model achieved 99.92% accuracy while maintaining a low false positive (FP) cost, making it suitable for a security operations centre (SOC). In addition, a Flask-based web application was developed to enable real-time deployment by sniffing live network traffic, executing inference, and persisting results in an SQLite database.
Decentralized multi-agent orchestration for legacy order-to cash optimization
Legacy enterprise resource planning (ERP) systems serve as the operational backbone of global commerce but often create bottlenecks due to their rigid, monolithic design. As organizations incorporate artificial intelligence (AI), these outdated systems struggle to support high-speed, parallel workflows, creating a significant integration challenge. This paper introduces a non intrusive modernization approach that overlays a decentralized multi-agent system (MAS) onto existing infrastructure without requiring invasive code changes. By developing a digital twin of the order-to-cash (O2C) process, we train autonomous agents through multi-agent reinforcement learning (MARL) to manage credit validation, inventory allocation, and fulfillment. We adapt the centralized training, decentralized execution (CTDE) framework to meet O2C constraints, enabling agents to learn globally optimal strategies while operating independently. Simulation results show that this architecture surpasses rule-based robotic process automation (RPA) baselines, increasing total throughput by 6.9% over a monolithic setup, though at a 6.3% error rate due to aggressive allocation policies. These results indicate that decentralized agent-based orchestration provides a scalable approach for modernizing legacy ERPs, offering increased agility without the risks associated with platform replacement.
Sentiment analysis in telecommunications: a systematic review of applications and challenges
The increasing amount of digital content on online platforms creates both opportunities and challenges for telecommunications operators aiming to improve customer experience. Sentiment analysis (SA), a subfield of natural language processing (NLP), allows for the extraction of subjective information from data generated by users. This paper presents a systematic literature review (SLR), conducted in accordance with Kitchenham’s guidelines, examining the applications and challenges of SA within the telecommunications sector. A structured methodology was used to select and analyze over 100 studies published between 2017 and 2025. The review categorizes SA methods and evaluates their application in practical telecom contexts, including customer satisfaction assessment, churn prediction, service monitoring, and reputation management. Findings indicate that machine learning and deep learning models can achieve performance levels up to 97% in specific experimental contexts. However, such results may vary depending on the datasets, evaluation protocols, and application contexts. Key challenges identified include handling informal language, domain dependency, multilingualism, sarcasm detection. Finally, the review highlights future directions such as real-time sentiment tracking, multimodal analysis, and the integration of federated learning for privacy-preserving customer analytics. This review provides a foundational reference for researchers and practitioners aiming to deploy effective sentiment-driven systems in the telecom industry.
Acoustic and vibration side channel analysis on post-quantum cryptography using image-based deep learning
Post-quantum cryptography (PQC) is designed to resist quantum-era attacks; however, practical implementations remain vulnerable to physical side channel leakage. This work proposes an image-based acoustic–vibration side-channel analysis framework to assess non-invasive leakage in PQC systems. Acoustic and vibration signals from secret-dependent executions are modeled and transformed into time–frequency spectrograms using short time fourier transform (STFT). The dataset comprises 1,545 samples (1,236 training and 309 testing), acquired at 16 kHz and segmented into 2.5-second windows. Leakage classification is performed using convolutional neural networks (CNNs) and vision transformers (ViTs) under single-modality and multimodal fusion settings. Results show that acoustic signals yield strong leakage, achieving up to 100% accuracy with CNN, while vibration signals reach up to 98.75%. Multimodal fusion improves training stability and overall performance, and ViT models demonstrate better generalization across modalities. These findings confirm that multimodal spectrogram based deep learning is effective for PQC side-channel analysis and underscore the need for rigorous physical security evaluation in real-world PQC implementations.
Mapping research trends on tropical cyclone–induced flood susceptibility: a bibliometric and systematic review method
Climate change has intensified tropical cyclones (TC), increasing extreme rainfall and flood hazards in many regions. Flood susceptibility (FS) mapping is therefore essential for understanding flood risk. This study analyzes global research trends on TC-induced FS by integrating bibliometric analysis and a preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based systematic literature review (SLR) using Google Scholar (GS) publications from 2014 to 2024. A total of 993 journal articles were analyzed, yielding an h-index of 101 and a g-index of 168, indicating strong and growing research interest. The results reveal an increasing application of machine learning (ML), deep learning (DL), remote sensing (RS), and geographic information systems (GIS) for FS mapping. Several gaps remain, including limited use of high-resolution data, underrepresentation of data-scarce and equatorial regions, restricted integration of hybrid models, and a lack of long-term assessments considering climate change and socio-economic factors. The model’s performance is also highly dependent on data quality and regional characteristics, limiting its generalizability across different conditions. The main contribution of this study is the knowledge mapping and synthesis of TC-induced FS research, providing a structured foundation for future studies and supporting evidence-based flood risk management and climate adaptation.
Adaptive trading system for sustainable forex markets
This study presents a sustainable and ethically aligned algorithmic trading system for the Euro/United States Dollar (EURUSD) currency pair, integrating reinforcement learning (RL) with a Sugeno-type fuzzy inference mechanism. The framework emphasizes responsible AI principles by combining adaptability and interpretability to support transparent and explainable financial decision-making. Historical EURUSD M15 data from 2020 to 2023 were used for training, while 2024 data served for out-of sample testing. The system employs EMA50-based state classification, tabular state–action–reward–state–action or SARSA learning, and a fuzzy logic layer comprising 27 expert-defined rules. During backtesting, the agent executed 785 trades, achieving a net profit of USD 61.85, a profit factor of 1.04, and a balanced win–loss ratio. Risk-adjusted analysis showed moderate resilience (sharpe ratio = 0.53) and a maximum drawdown of 59.74%. The model demonstrated strong equity stability (ESI = 0.9672) and sensitivity to macroeconomic events identified through cumulative sum (CUSUM) analysis. While the system maintained capital preservation and interpretability, responsiveness under volatile conditions requires improvement. Future work will focus on adaptive exit logic, volatility-aware reward mechanisms, and regime-sensitive policy optimization. This study contributes to advancing sustainable, transparent, and risk-aware artificial intelligence (AI) frameworks in algorithmic trading.
GenAI as an IoT programming assistant: a case study on automated debugging for air quality monitoring systems
The rapid expansion of internet of things (IoT) technology has necessitated the development of user-friendly programming solutions for non–experts. While generative artificial intelligence (GenAI) offers the potential to democratize code development, its ability to assist in the intricate task of automated debugging, particularly regarding hardware integration remains a critical area of research. A design research approach was employed, employing a structured four – phase workflow: error analysis, diagnostic execution through prompting, iterative solution analysis, and functional verification. The methodology was applied to an experimental case study involving an air quality (AQ) monitoring system. The study tested the artificial intelligence (AI)’s capacity to debug C++ code intended for the Arduino integrated development environment (IDE). Gemini AI successfully identified and resolved three critical logic errors arising from mismanaged MQ135 calibration variables, incorrect loop sequencing, and data desynchronization between the organic light emitting diode (OLED) display and the internal status logic. GenAI proved effective as a programming assistant for resolving bugs in IoT applications. However, effective debugging still depends on well-structured prompts and a basic understanding of the underlying IoT hardware.
XGBoost modeling for sparse spare-parts demand forecasting
Spare parts demand in many industrial systems is inherently sparse and intermittent. In practice, long periods of zero usage are common, even though inventory must still be maintained to ensure operational reliability. This situation increases holding costs and the risk of obsolescence, while also limiting the effectiveness of conventional forecasting techniques. This study demonstrates that a global XGBoost model trained across multiple spare-part items significantly outperforms item-specific models under sparse demand conditions. Using six years of historical spare-parts usage and procurement data from the energy sector, this study compares global and single-item extreme gradient boosting (XGBoost) modeling strategies. Forecast accuracy is evaluated using mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and median absolute error (MdAE), which is particularly suitable for zero-inflated demand patterns. The results consistently show that the global XGBoost model achieves lower errors across all metrics. In particular, the global model attains a markedly lower MdAE (0.00018), indicating greater robustness when demand is irregular and intermittent.
Classification of P300 event-related potentials using SNN, CNN and LSTM deep learning models
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.
Decision-tree-based machine learning for detecting coffee agroforestry using SPOT-7
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.
Privacy-preserving messaging for medical device networks
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.
Comparative evaluation of classical and machine learning methods for medical image enhancement
Medical imaging is critical for diagnostic accuracy, yet raw images often suffer from noise and low contrast. This study provides a comparative evaluation of classical methods, namely the Laplace transform (LT), Sobel operator (SO), and histogram equalization (HE), against a data-driven convolutional neural network (CNN) using the musculoskeletal radiographs (MURA) and Human Metapneumovirus (HMPV) lung computed tomography (CT) datasets. While quantitative analysis shows that HE and SO significantly outperform other methods in isolated contrast enhancement and edge definition, they often introduce artifacts. In contrast, the CNN based approach demonstrates superior detail preservation and entropy, offering a more balanced and adaptive solution for diverse diagnostic requirements. Our findings statistically validate that although classical operators remain highly effective for specific boundary detection tasks, machine learning (ML) frameworks provide the most robust performance for cross-modality image enhancement, bridging the gap between raw data acquisition and clinical interpretation.
A vector-valued PDE model unifying anisotropic diffusion and shock filter for color images denoising and deblurring
This paper proposes a novel and efficient method for the restoration of color images degraded by the combined effects of noise and blur. To address this challenging inverse problem, a mixed partial differential equation (PDE) model based on a semi-implicit numerical scheme is developed for color image restoration. The proposed approach integrates anisotropic diffusion and shock filtering within a unified vector-valued framework, allowing the different color channels to be processed jointly while preserving their mutual correlation. This formulation enables selective smoothing that efficiently suppresses noise in homogeneous regions while simultaneously enhancing and preserving significant edge and structural features. Extensive experimental results on various color images demonstrate that the proposed method consistently outperforms existing color image restoration techniques. Quantitative evaluations based on standard image quality assessment metrics, including peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), confirm the superior restoration performance of the proposed approach. Furthermore, the proposed model maintains chromatic consistency across color channels during simultaneous denoising and deblurring, effectively preventing the introduction of color artifacts and ensuring visually faithful restoration results. The numerical implementation of the proposed method is based on a semi-implicit scheme.
A new hybrid dyadic-tent chaotic function for secure digital image encryption
Image encryption is significant for protecting visual data in modern digital communication systems. The sensitivity of chaotic functions to initial conditions and strong pseudo-randomness has led to their usefulness as cryptography techniques. This study developed a new hybrid chaotic function which combines the Dyadic Transformation Map and the Tent Map to create a stronger and more secure mechanism for image encryption. To further enhance keystream unpredictability, SHA-256 hashing was used as a whitening layer. The chaotic characteristics of the proposed function were evaluated using the Lyapunov exponent, confirming its usefulness in cryptography. The hybrid function was tested on both grayscale and color images to evaluate its performance in entropy, histogram uniformity, pixel decorrelation, and resistance to statistical attacks, and the effect of SHA-whitening. Results show that the hybrid function provides strong pixel diffusion and confusion while maintaining low computational cost, making it a lightweight yet highly secure encryption method. Thus, the proposed approach offers an effective and reliable solution for image protection in resource-constrained environments.
Reconfigurability of graphene-based hexagonal patch operating in the Ku band
This paper presents a novel graphene-based impedance reconfigurability approach for Ku-band hexagonal patch antennas, demonstrated at both the single-element and 4×4 array levels. Unlike conventional metallic or diode based reconfigurable antennas, frequency tuning is achieved by exploiting the tunable surface conductivity of graphene integrated into E-shaped slots, without altering the antenna geometry or employing active lumped components. Antenna performance is evaluated using full-wave electromagnetic simulations for two graphene states (“on” state and “off” state), representing distinct surface impedance conditions. The single element exhibits dual resonances at 14.81 GHz and 15 GHz, with reflection coefficients of -52.56 dB and -38.18 dB, bandwidths of 783 MHz and 517 MHz, and a peak gain of 7.8 dBi. The 4×4 array exhibits multiple resonances between 12.5–16.8 GHz (off state) and 12.6–17.15 GHz (on state), achieving bandwidths up to 2750 MHz and a maximum gain of 13.64 dBi. These results demonstrate a scalable, geometry-preserving reconfigurable antenna solution for compact Ku-band systems.
Design and construction of microcontroller-based exhaust emission measurement equipment for freight transportation
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.
Heading stabilization of a mecanum wheel mobile robot using Kalman filter and SMC under variation condition
This paper presents a robust heading stabilization system for a mecanum wheel mobile robot by integrating a Kalman filter (KF) with sliding mode control (SMC). A two-state KF estimates the robot’s heading angle and gyroscope bias from ICM20948 inertial measurement unit (IMU) measurements, reducing sensor noise by 65% and compensating for bias drift of 0.3° per second. The estimated heading is regulated using SMC with a boundary layer to minimize chattering. Implemented on a Raspberry Pi 3B, the system was validated under varying surface friction conditions and external disturbances. The controller achieved heading stabilization with root mean square error (RMSE) between 0.380° and 0.589° across all surfaces, steady-state error within ±0.5°, and convergence within 2.0–2.6 seconds. Under severe disturbances causing heading deviations up to 238°, rapid recovery within 0.5 seconds was achieved with only 3.04° final steady state error. The results demonstrate the feasibility of implementing robust heading stabilization on low-cost embedded platforms for autonomous navigation applications.
Extended-state observer control with online payload identification for HRI in series elastic actuators
Safe human–robot interaction requires actuators that combine compliance with accurate force inference. Series elastic actuators (SEAs) are particularly suitable for this purpose; however, separating payload-induced torques from voluntary human interaction forces without dedicated sensors remains a critical challenge. This paper proposes a unified observer-based control framework for SEAs that integrates three key components: a linear parameter-varying extended state ob server (LPV-ESO), an online algebraic payload estimator, and a disturbance driven motion intention identification (MII) mechanism. The payload estimator continuously updates the observer gains to adapt to load variations, while the LPV-ESO decouples payload dynamics from human interaction forces. The dis turbance estimate is then processed through the MII block to infer operator intent in real time without additional sensing hardware. Numerical simulations under nominal conditions and ±10% parametric uncertainty confirm bounded trajec tory tracking error, rapid convergence of payload estimation, and effective sepa ration of payload-induced torques from voluntary interaction forces. The results demonstrate that the proposed framework achieves sensorless force decompo sition and reliable motion intention inference, offering a practical solution for SEA-based collaborative robots in rehabilitation, assistance, and industrial ap plications.
Study of the effect of changes in cell output temperature on the net efficiency of solid oxide fuel cell power generation
Solid oxide fuel cell (SOFC) power plants offer a promising pathway toward highly efficient energy conversion compared to conventional systems. This study explores the impact of output temperature variations on system performance through simulation analysis. The results demonstrate that increasing the temperature difference between the fixed input (700 °C) and the cell output significantly enhances net efficiency, while gross efficiency remains relatively stable. At an output temperature of 875 °C, the system achieves a net efficiency approaching 50% and a gross efficiency of approximately 61%. These findings emphasize the critical role of output temperature in determining overall system efficiency and highlight the importance of thermal management in SOFC operation.
Beyond coal: optimization of hybrid floating PV–hydropower systems for Indonesia’s decarbonization
Indonesia’s failure to meet the 23% renewable energy target, resulting in continued reliance on fossil fuels, necessitated a multi-criteria evaluation of hybrid energy systems. This study aimed to simultaneously minimize the levelized cost of energy (LCOE), reduce carbon dioxide (CO2) emissions (decarbonization), and maximize the renewable energy penetration by integrating a coal-fired power plant (coal), hydroelectric power plant (hydro), and floating photovoltaic (FPV) power plant at sites in western Java (West Java and Banten provinces). Thirteen configurations were evaluated using European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 climate data (1991-2020) and hybrid optimization of multiple energy resources professional (HOMER Pro) simulations. The optimal coal hydro-FPV-Li-Ion hybrid configuration achieved 91.1% renewable energy penetration, 94.5% decarbonization, avoided ~4.1 million kg CO2 annually, delivered an LCOE of $0.1201/kWh with an internal rate of return (IRR) of 7.6%, return on investment (ROI) of 5.9%, and payback period of 8.7 years. This configuration outperformed the pumped hydro storage alternative, demonstrating 38% lower capital expenditure (CAPEX) and 49% faster payback period, confirming its economic suitability for Indonesia’s fiscal constraints. These findings indicate that a hybrid energy system with battery storage technology is a technically and economically viable pathway toward Indonesia’s decarbonization agenda.
Fast-decoupled power flow optimization in 20 kV systems
Power flow optimization in electrical power distribution systems is crucial for maintaining voltage stability and energy efficiency. 20 kV distribution systems often face voltage profile instability issues due to high power losses, where conventional power flow analysis methods are sometimes inefficient in handling load fluctuations in medium-voltage networks. This study proposes power flow calculation optimization using the fast-decoupled approach to evaluate and improve voltage profiles quickly and accurately, as well as integrating reactive power compensation through strategic capacitor bank placement. Simulation results show that under existing conditions, the system experiences significant voltage drops, especially at Bus 27 (17.740 kV) and Bus 53 (7.640 kV). After implementing a 2×900 kVAr capacitor bank, the voltage profile increased dramatically: Bus 27 rose to 19.160 kV and Bus 52 reached 19.020 kV. This proves that the integration of the fast decoupled method and reactive power compensation is effective in minimizing voltage deviation and improving the operational stability of the distribution system.