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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Next generation LoRa-based resilient communication system for disaster mitigation and relief operations
This paper presents the development of a long range (LoRa) based communication system meant for off-grid environments. It aims to send data over long distances with minimal power consumption. Using Arduino boards, LoRa transceiver modules, and Bluetooth modules, an analysis of LoRa devices was carried out, focusing on their performance in terms of signal strength and range. LoRa technology is recognized for its low-power consumption and extended range capabilities. The experiments were conducted at the Islamic University of Madinah (IUM), Madinah, Saudi Arabia. The readings of the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) are collected at various distances. The study involved testing with a 2.15 dBi antenna, and the results indicated that the device achieved a maximum range of 240 m with RSSI = –128 dBm and SNR = –20 dB. This work explored LoRa modulation using software-defined radio (SDR) and demonstrated the feasibility of LoRa technology for off-grid communication. The system offers long-range text messaging capabilities with user-friendly features, serving as a valuable solution where traditional network infrastructure is unavailable.
Dual-band performance enhancement of terahertz patch antennas via slotting method
Sixth-generation (6G) wireless networks are designed to provide ultra-high data rates with latencies as low as one microsecond, operating at frequencies higher than those used in fifth-generation (5G) networks. This study focuses on a compact and flexible dual-band terahertz (THz) rectangular patch antenna utilizing a slot cutting technique. The antenna features strategically placed rectangular slots on its patch, with dimensions of 1×1×0.1 mm. Its performance was simulated using computer simulation technology (CST) Studio Suite simulation software. The results indicate that the antenna operates at frequencies of 147.42 GHz and 202.5 GHz, achieving gains of 2.66 dB and 4.52 dB, respectively. Notably, the designed antenna demonstrates excellent impedance matching, as evidenced by deep return loss values of −19.818 dB and −44.776 dB. Furthermore, the findings report a voltage standing wave ratio (VSWR) of 1.01. This antenna design is suitable for applications in aerospace, 5G handheld devices, wireless communication, and THz medical imaging.
Design of vehicle to vehicle communication: accident collision prevention using light fidelity and wireless fidelity technology
Vehicle-to-vehicle (V2V) communication is a key component of intelligent transportation systems (ITS), enabling seamless data exchange between vehicles to limit collision risks. This study presents a hybrid communication framework that integrates light fidelity (LiFi) and wireless fidelity (WiFi) technologies to enhance safety and reliability in accident prevention. Lifi using visible light communication, provides line-of-sight for short-range communication, while WiFi ensures long-range coverage in dynamic traffic environments. The proposed system allows vehicles to share speed, braking, and positional data, enabling timely warnings to drivers in high-risk scenarios. The system fuses data communication protocol design, simulation, prototype development, testing, and evaluation. The prototype model was designed and simulated to evaluate the performance of the system in terms of functionality, timing and reliability. Results indicate that the hybrid LiFi-WiFi system improves data transmission efficiency and reduces delay compared to standalone wireless systems. This approach demonstrates significant potential in developing safer transportation networks by combining complementary wireless technologies for V2V communication.
Noise-suppression method for UAV-OFDM systems by introducing CV-VSS-NLMS algorithm and single-antenna architecture
In this paper, we address the critical challenge of impulsive interference in orthogonal frequency division multiplexing (OFDM)-based unmanned aerial vehicle (UAV) communication systems, which can severely degrade data transmission reliability. Specifically, we propose a novel complex-valued variable step-size normalized least mean square (CV-VSS-NLMS) adaptive filtering algorithm dedicated for adaptive filtering of complex-valued signals, providing real-time, lightweight, and efficient impulsive-noise suppression for UAV-OFDM signals. In contrast, real-valued VSS-LMS filters treat the real and imaginary parts separately, resulting in poorer mean square error (MSE) convergence for complex signals. The algorithm is developed by efficiently adapting LMS-based filtering strategies to impulsive interference scenarios and adequately integrating prior concepts of electromagnetic pulse suppression within a well-designed single-antenna UAV architecture. This new configuration is especially suited for size, weight, and power-constrained UAV platforms, where reducing complexity is highly desirable. In contrast to conventional blind source separation approaches, the proposed solution ensures reliable communication without excessive processing demands, since it efficiently suppresses impulsive noise and greatly reduces the number of matrix operations. Simulation results demonstrate a significant improvement in bit error rate (BER), confirming that the proposed CV-VSS-NLMS technique provides a robust, dependable, and practical solution for modern UAV communication links.
Secure two-way relaying with successive interference cancellation and fountain codes: performance analysis
This paper proposes a secure two-way relaying (TWR) scheme using fountain codes (FCs), successive interference cancellation (SIC), and digital network coding (DNC). Using FCs, two sources exchange their data by first encoding the data into a series of packets (called encoded packets). These encoded packets are then exchanged between the sources via the help of a common relay, and they are also overheard by an eavesdropper. The packet exchange is carried out over two time slots: i) in the first time slot, both sources send their encoded packets to the rela y; and ii) the relay applies SIC to decode two received packets, and then broadcasts the exclusive OR (XORed) packet to both sources in the second time slot. The sources and the eavesdropper try to collect a sufficient number of encoded packets to successfully recover the original data. This paper derives and validates exact closed-form expressions for system throughput (TP), system outage probability (SOP), and system intercept probability (SIP) over Rayleigh fading channels. Furthermore, our findings reveal a reliability-security trade-off as well as the impact of system parameters on the network performance.
System dynamics control simulation for sustainability of Indonesia’s cocoa supply chain
Indonesia’s cocoa sector faces challenges in greenhouse gas emissions and smallholder income volatility. This study develops a system dynamics model to simulate the interrelationship between carbon emissions and economic performance across the cocoa value chain, identify leverage points, and evaluate alternative policy scenarios. The model integrates environmental and economic variables into dynamic feedback structures, enabling scenario-based assessment of intervention strategies. Five scenarios were simulated: composting cocoa waste increased farmer income by 2% and reduced farm-level emissions from 0.43 to 0.303 kg CO₂-eq/kg (29.79% total reduction); biogas conversion raised income by 13.56% and reduced emissions by 11%; converting cocoa waste into animal feed slightly increased income by 0.23% while cutting emissions by 58.6%; combining composting with improved transport efficiency reduced emissions by 14%; and integrating composting, logistics optimization, and government-supported input subsidies yielded the highest performance, with a 13.50% income increase and a 70% emission reduction. These results demonstrate that integrated, system-based interventions can enhance both economic resilience and environmental sustainability. The system dynamics model provides policymakers and supply chain actors with actionable insights for designing effective, climate-aligned strategies in Indonesia’s cocoa industry.
Hybrid intrusion detection in IoT devices: a deep learning approach using Kitsune and quantized autoencoder
Internet of things (IoT) has been transforming the way to connect and communicate in smart homes, healthcare, and businesses so fast and rapidly around the world. But this growth has complicated security, because IoT devices are more likely to be hacked as they’re smaller, without even regular security practices, and under attack by more sophisticated threats. Traditional intrusion detection systems (IDS) are not functioning well in IoT environments as they are computationally expensive and struggle to accommodate the heterogeneous nature of IoT networks. This paper introduces a cross-domain intrusion detection based on adaptive adversarial training using Kitsune and quantized autoencoders (QAE) for anomaly detection and classification. The model is capable of capturing different attacking techniques, such as distributed denial of service (DDoS), Mirai botnet attacks, address resolution protocol (ARP) spoofing, and data exfiltration, by leveraging the reconstruction error generated by Kitsune autoencoders. The degree-based classification enables the system to dynamically categorize anomalies according to their severity, rendering the model exceptionally adaptive to various attacks. The anomalies are also classified into different types of attacks (normal, suspicious, and malicious) based on binarized error values. The approach achieves a high accuracy with an F1 score of 85.9% and supports real-time characterization to increase security in IoT scenarios.
Transforming e-government projects by developing a RAF using Scrum integrated with CASE tool in Botswana
The digital transformation in Botswana has placed strong emphasis on e-government initiatives aimed at improving public service delivery. However, these projects continue to face low success rates due to challenges such as inadequate and reactive risk management practices, limited technical expertise, and fragmented implementation. This study proposes an integrated risk assessment framework (RAF) that combines Scrum methodology with computer-aided software engineering (CASE) tools that allows for the development of an automated, proactive, and iterative approach to risk management that is specific to the socioeconomic circumstance of Botswana. A quantitative survey was conducted with 32 project management specialists involved in e-government projects to assess their familiarity with agile methods and CASE tools, perceptions of traditional risk management approaches, and acceptance of the proposed model. The results revealed that 90.6% of respondents were familiar with Scrum, 78.1% had used CASE tools, and 81.25% supported the new framework, highlighting the urgent need for real-time risk tracking and continuous stakeholder engagement. The proposed e-government risk assessment framework (e-GRAF) model offers a flexible and adaptive solution to strengthen risk management processes, increase the success rate of e-government projects, and improve the quality and resilience of digital governance systems in Botswana.
Time encoded signal processing and recognition with vector quantization: applied to Arabic numerals
This article presents our contribution to speaker recognition using Arabic numerals. This recognition is based on hybridization between the time encoded signal processing and recognition (TESPAR) technique and vector quantization (VQ), in order to consolidate the classification step thanks to this combination. To set up an effective and efficient recognition system, we used a corpus recorded under ideal conditions, minimizing the differences between the reference corpus and the test corpus. We also applied the linear discriminant analysis (LDA) technique in order to discriminate the acoustic vectors and minimize the representative space. This hybridization indicated a quantifiable increase in the speaker recognition rate with the ten Arabic numerals (0–9).
The impact of EPS on procurement performance: the mediating role of supplier relationship quality in Ghana
This study examines the effect of e-procurement systems on procurement performance (PP) in Ghana, highlighting the mediating role of supplier relationship quality (SRQ). A quantitative, cross-sectional survey of 370 procurement professionals from public and private organisations was conducted to assess four dimensions of e-procurement: system integration, data transparency, user-friendliness, and automation. Results indicate that all four dimensions significantly enhance PP, with system integration and user-friendliness emerging as the strongest predictors. Mediation analysis further reveals that SRQ, characterised by trust, communication, and collaboration, partially strengthens the relationship between e-procurement and procurement outcomes. Nonetheless, challenges such as inadequate staff training, limited supplier digital skills, weak infrastructure, and insufficient managerial support hinder optimal system effectiveness. Grounded in the resource-based view (RBV) and transaction cost economics (TCE), the study demonstrates the importance of combining technological and relational capabilities. Recommendations include enhancing digital skills training, strengthening supplier engagement, improving system design, and fostering institutional support.
Taxonomy of cooperative adaptation level for cooperative adaptive mobile applications
Adaptive mobile applications (AMAs) are software systems designed to dynamically adjust their behavior in response to contextual changes. When multiple AMAs coexist on the same device, they create an ecosystem of heterogeneous applications with distinct functionalities, interaction models, and sensor requirements. This diversity enables opportunities for cooperative adaptation, where applications synchronize their behavior for collective benefit. Building on prior work that identified cooperation as a key dimension of adaptive mobile systems, this study proposes a refined taxonomy of cooperation levels for AMAs. The taxonomy is validated through case studies and formal specification methods, demonstrating its theoretical soundness and practical applicability. The findings advance the understanding of cooperative adaptation mechanisms and provide structured guidance for designing and classifying cooperative AMAs.
Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing
Offering a promising solution for latency-sensitive and resource-constrained internet of things (IoT) applications, mobile edge computing (MEC) extends cloud capabilities to the network edge. However, the decentralized nature of edge resources, coupled with stringent latency requirements and IoT energy constraints, presents significant challenges for efficient task offloading. Integrating IoT with MEC and software-defined networking (SDN) can meet the growing demands for low latency and energy-aware resource management. This paper proposes a hybrid evolutionary algorithm combining whale optimization algorithm (WOA) and particle swarm optimization (PSO) with crossover, mutation, and Lévy flight operators (CML) to balance exploration and exploitation. The algorithm minimizes a weighted sum function (energy 35%, delay 35%, and monetary cost 30%) for joint task offloading and resource allocation in SDN-enabled MEC environments. The proposed approach is evaluated against six well-known metaheuristics, analyzing performance across various metrics including scalability with up to 100 users. Experimental results, validated by non-parametric statistical tests, demonstrate that the proposed algorithm achieves statistically significant improvements in convergence speed, solution quality, and scalability, making it a robust and promising candidate for real-time MEC task scheduling.
Hybrid classical–quantum ensemble learning for real-time flight delay prediction at Tribhuvan International Airport
This study investigates ensemble learning using classical and quantum-inspired models to predict flight delays at Tribhuvan International Airport (TIA), Nepal. It combines traditional machine learning algorithms with quantum-based approaches, quantum boosting (QBoost) and the hybrid QBoostPlus, leveraging quantum properties for faster computation. The dataset includes flight records from 2020 to 2024 and Meteorological Aerodrome Reports (METAR), analyzed across four sea- sons to capture delay patterns in domestic and international flights. A combined seasonal dataset assesses model generalization. Six models; VotingClassifier, adaptive boosting (AdaBoost), xtreme gradient boosting (XGBoost), categorical boosting (CatBoost), QBoost, and QBoostPlus are evaluated based on accuracy, precision, recall, F1 score, area under the curve(AUC), and execution time. CatBoost achieved high accuracy (up to 0.97) but slower execution (up to 10,570.63 ms). QBoostPlus provides competitive AUC scores (0.83–0.95) with faster execution, improving speed by up to 99.94% and generating predictions in as little as 6.46 ms. Although quantum-inspired models have slightly lower accuracy, their computational efficiency and stability show strong potential for real-time flight delay prediction. This is the first study applying quantum-inspired ensemble learning to Nepalese aviation data, showing promise for regional airports with limited infrastructure.
A comparative MRI-based study of ResNet-152 and novel deep learning approaches for early Alzheimer’s disease classification
Alzheimer’s disease (AD) is the leading cause of dementia, making early-stage detection essential for timely intervention. Most prior studies have focused on binary AD classification, which limits sensitivity to disease progression. This study addressed this gap by evaluating whether tailored convolutional neural network (CNN) architectures could improve stage-aware classification using a publicly available magnetic resonance imaging (MRI) dataset containing 35,984 images across four diagnostic categories. The dataset underwent grayscale conversion, resizing, contrast enhancement, normalization, and class balancing prior to model development. Four models were trained and compared: ResNet-152, a custom multiclass CNN, a one-vs-one (OvO) model, and a one-vs-rest (OvR) model. Performance was measured using accuracy, precision, recall, F1 score, and confusion-matrix–based metrics. The custom multiclass CNN achieved the strongest performance, yielding the highest accuracy and balanced results across all evaluation metrics. These findings demonstrate the value of systematically comparing decomposition strategies for multi-stage Alzheimer’s detection and highlight the potential of the proposed approach to enhance early diagnostic support. Future work may incorporate multimodal inputs or hybrid architectures to improve sensitivity to subtle structural changes and further strengthen clinical applicability.
Improving multilabel classification of hate speech and abusive language in Indonesian using MAML
This study investigates automated multi-label detection of hate speech and abusive language (HSAL) in Indonesian social media, addressing challenges of data imbalance, especially in minority labels. Two training approaches are compared: standard supervised learning and meta-learning using the model-agnostic meta-learning (MAML) algorithm. IndoBERTweet-BiGRU is adopted as the baseline model, while MAML is leveraged to enhance generalization and adaptability with limited training data. Both models are trained on a multilabel dataset with 13 HSAL categories exhibiting highly imbalanced distributions. The best supervised model achieved an F1-Micro of 84.02% and an F1-macro of 77.97%, whereas the best MAML-trained model reached 84.12% and 76.85%, respectively. Although the overall gap is small, MAML demonstrates notable improvements on minority classes such as hate speech (HS) physical, gender, and race, shown through higher F1-score and area under the receiver operating characteristic curve (AUROC) values. These results highlight its strength in low-resource classification settings. This study is limited to Indonesian language and YouTube transcript contexts, and MAML incurs higher training complexity. Cultural and linguistic nuances also present potential bias in real-world use. Despite these constraints, the proposed system offers practical benefits by enabling fine-grained HSAL classification and supporting earlier detection of harmful online content.
Evaluating learning rate effects on long short-term memory for Indonesian sentiment classification
Hyperparameter optimization is a crucial process for enhancing the performance of deep learning models, particularly in the context of Indonesian sentiment classification. This study examines the impact of varying learning rates on a long short-term memory (LSTM) architecture trained with the adaptive moment estimation (Adam) optimizer. The dataset comprises 9,295 Indonesian comments automatically labeled by the Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) model. Stratified k-fold cross-validation was employed to maintain class balance during training. Learning curves were analyzed to evaluate convergence and identify potential overfitting, while early stopping was applied when performance improvements became insignificant. The one-way analysis of variance (ANOVA) test (p-adj = 0.000575 < 0.05) revealed significant differences among the learning rate variations. Post-hoc analysis indicated the learning rates of 0.0001, 0.001, and 0.002 differ significantly from 0.02. Descriptive statistics showed that a learning rate of 0.001 was the most optimal, achieving the highest validation accuracy while maintaining a relatively low variance. Evaluation across two data categories demonstrated that lower learning rates (0.0001 and 0.002) achieved the best accuracy, 78.71% on in-domain data, whereas higher learning rates (0.01 and 0.02) performed better on cross-domain data with 36% accuracy. These findings highlight the crucial role of learning rate selection in determining model stability and generalization capability.
A comprehensive analysis of feature selection and XAI for machine learning classifiers to recognize guava disease
Recognizing and classifying diseases in guava is crucial for managing farms to keep crops healthy and increase harvest quality. Cultivators face the most severe challenges when it comes to recognizing and diagnosing guava fruit and leaf illnesses, a task that is nearly impossible to perform manually. This research focuses on developing a robust disease identification model using image data collected locally from guava trees. After data collection, various image processing techniques, including scaling and contrast enhancement, are utilized to make the data more suitable for use. K-means clustering is employed to quickly divide the images into groups, followed by the extraction of important characteristics. Two separate feature ranking approaches, analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO), are used to select the best characteristics, identifying the 10 most important attributes. The adaptive boosting (AdaBoost) classifier achieves the highest accuracy among six classifiers for the top seven characteristics indicated by LASSO among the specified features. To enhance the model’s interpretability, two explanation methods, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP), are employed to illustrate how the classifier reaches its conclusions. This approach not only simplifies disease identification but also clarifies the reasoning behind predictions, opening the door to real-world applications in detecting and preventing dangerous diseases.
Attributes conducive to anthropomorphism in artificial intelligence
The rapid development of artificial intelligence (AI), particularly large language models (LLMs), has generated both enthusiasm and concern regarding its role in society. While these systems demonstrate impressive technical capabilities, public acceptance is often hindered by perceptions of unpredictability, mistrust, and fears amplified by media narratives. One potential strategy to improve user acceptance is anthropomorphism, the attribution of human-like qualities to AI systems which can make interactions feel more natural and trustworthy. This paper investigates the attributes most conducive to anthropomorphism by conducting a structured review across psychology, human-robot interaction, communication studies, and business applications. The analysis identifies key traits such as emotional expressiveness, conversational coherence, adaptive social behavior, and role-based framing that enhance perceptions of AI as relatable and dependable. By synthesizing these insights, we propose a conceptual framework that highlights the psychological, social, and technical dimensions of anthropomorphism in AI. The findings provide guidance for designing AI systems that balance efficiency with user trust, thereby supporting more effective integration of AI into business, research, and everyday life.
Low-cost ESP32-based sound data acquisition system with MATLAB integration for real-time noise monitoring
This study presents the design and implementation of a low-cost ESP32-based sound data acquisition system (SDAS) for real-time noise monitoring. The system integrates a micro-electro-mechanical systems (MEMS) microphone for accurate acoustic data capture, an ESP-WROOM-32 microcontroller for signal processing and wireless data transmission, and MATLAB for real-time visualization and analysis. Designed and simulated in KiCAD 8.0, the SDAS includes a microSD module for local data backup and offline analysis. The system was tested in four indoor locations within Mindanao State University – Iligan Institute of Technology, recording mean noise levels ranging from 14.2 dB in laboratory environments to 32.1 dB in classrooms, with corresponding standard deviations of 1.2–7.0 dB. Expert evaluation from eight assessors confirmed the system’s usability, data reliability, and robustness. The system demonstrates effective monitoring for both quiet and dynamic settings. Limitations include single-node configuration, indoor-only testing, and MATLAB-based USB data transfer. Despite these, the proposed SDAS provides a scalable and reproducible model for smart campus and urban environmental monitoring, supporting sustainable development goals (SDG) 3, 9, and 11.
Design and evaluation of a low‑cost real‑time fluid-level monitoring system for fuel stations
Accurate fluid level management in fuel stations is hampered by inventory errors, delayed shortage detection and costly proprietary sensors. We designed and built a low‑cost, open‑source monitoring system using an Arduino Uno, an HC‑SR04 ultrasonic sensor, a NodeMCU ESP8266 and a DHT11 temperature sensor. Validation was restricted to static short-term conditions, with a prototype tested in a 200 cm tank over 62 hours and 32 paired measurements collected at two-hour intervals. Prototype readings were compared with dipstick measurements after temperature compensation. The system achieved a mean error of 0.03 cm, a mean absolute error of 0.91 cm, a standard deviation of 1.06 cm and a root‑mean‑square error of 1.05 cm, with a 95 % confidence interval of ±0.37 cm. These results demonstrate that a calibrated and temperature‑compensated ultrasonic sensor can deliver centimetre‑level accuracy suitable for inventory management in resource‑constrained fuel stations. Future work will extend validation to dynamic transfers, sloshing/vibration, humidity effects, and long-term drift in operational tanks.
Identification of paleographic curvature using skeletonization and key point detection
Jawi script represents a vital component of the Islamic intellectual heritage of the Nusantara, preserved across numerous classical manuscripts. A primary challenge in digitizing these documents is character segmentation, particularly where handwritten characters connect without distinct boundaries. This research proposes a skeletonization-based segmentation method to address this issue, utilizing a dataset from 17 pages of the “Kitab Syair Perahu” manuscript containing 269 test characters. The pre-processing stage involves grayscale conversion, binarization, and noise removal through connected component analysis (CCA). The segmentation process then integrates skeleton structures, centroid positioning, intersection points, and loop detection. Evaluation results show the system successfully identified 187 out of 269 characters, achieving an accuracy of 0.801, a precision of 0.895, a recall of 86.38%, and an F1-score of 88.91%. While these results demonstrate the method’s effectiveness, the small dataset from a single manuscript limits its generalizability. Nevertheless, this study establishes a foundational step toward an automated Jawi image-processing system and the digital preservation of Islamic Nusantara literacy, contributing a tailored skeletonization-based approach for Jawi script.
Application of the traveling salesman problem to optimize skeletonization and stroke reconstruction
The preservation of Turots Nusantara manuscripts written in Pegon script faces significant challenges due to physical deterioration and the complexity of handwritten styles. This study proposes a novel digitization approach based on image processing to extract and reconstruct handwriting strokes by combining skeletonization and the travelling salesman problem (TSP) algorithm. The novelty of this research lies in the application of a modified Greedy TSP algorithm capable of recognizing branching and cyclic structures typical of Arabic–Pegon characters, enabling accurate reconstruction of handwritten stroke sequences. The process involves preprocessing (grayscale, thresholding, and morphological operations), skeleton extraction using a thinning method, and weighted graph construction based on Euclidean distance between skeleton points. The proposed system achieved an average precision of 0.552, recall of 0.815, F1-score of 0.657, and accuracy of 0.82. These results demonstrate the method’s effectiveness in detecting and reconstructing character shapes from Pegon manuscripts. Practically, this approach offers potential applications in the automatic digitization, preservation, and analysis of Pegon script, contributing to the conservation of Indonesia’s Islamic intellectual and cultural heritage.
Score-level biometric information fusion with generalized power mean
To overcome the fundamental shortcomings of single-trait biometric systems, multimodal solutions have gained considerable interest. In this work, a score-level fusion scheme for biometric authentication is introduced, where information from multiple modalities is combined using conventional mean operators such as arithmetic, harmonic, geometric, and quadratic means, with particular attention given to the power mean formulation. The proposed framework increases system robustness while preserving low computational complexity and requiring no training phase. Performance is assessed on three well-known public datasets: National Institute of Standards and Technology (NIST)-fingerprint, NIST-face, and XM2VTS, using standard score normalization methods and commonly employed evaluation metrics. The experimental analysis shows that the quadratic mean attains a genuine acceptance rate (GAR) of 91.50% on the NIST-fingerprint dataset, while the power mean with α = 5 achieves 82.40% on NIST-face. Furthermore, the half total error rate (HTER) on XM2VTS is reduced to 0.059. In comparison with learning-based fusion techniques, the proposed approach provides a more straightforward, computationally efficient, and dependable alternative for real-world biometric applications.
Dynamic pooling using average-thresholding to improve image classification performance
Pooling layers are essential in convolutional neural networks (CNNs) for reducing data size while preserving key features. Traditional methods such as Max and Average pooling have limitations. Max pooling is sensitive to noise, while Average pooling treats all activations equally. Although T-Max-Avg pooling addresses these limitations through adaptive top-k selection, its rigid decision rule requires multiple threshold comparisons and limits efficiency, motivating a simpler decision mechanism. This study introduces average-thresholding pooling (ATP), a simplified adaptive method that replaces multiple threshold comparisons with a single decision based on the average of the top-k activations. This design improves computational efficiency and reduces sensitivity to outliers. Experiments on the STL-10 dataset using a LeNet-5 architecture show that the proposed method achieves accuracy comparable to T-Max-Avg pooling (~55.5%) while consistently improving both training efficiency and inference speed. These results indicate that ATP provides a lightweight and practical alternative for CNN-based image classification, offering an improved balance between classification performance and computational efficiency.
A counter-centric binary-to-binary coded decimal and multiplexed seven-segment driver on an Artix-7 FPGA
This paper presents a complete field-programmable gate array (FPGA) implementation for showing a 4-bit binary value (0–15) as a two-digit decimal number on the Nexys-4 double data rate (DDR) seven-segment display. The design comprises: (i) a compact binary-to-binary-coded decimal (BCD) converter tailored to the 0–15 range; (ii) a seven-segment decoder for active-low, common-anode digits; and (iii) a counter-based clock-enable controller that time-multiplexes the digits at a rate chosen to be flicker-free yet energy-efficient. A simple timing model links the divider width , the number of digits , and the refresh rate . Simulation verified hazard-free switching and one-hot anode selection; hardware tests on the Nexys-4 DDR (100 MHz clock) confirmed the analysis. Selecting yields ms and Hz, which removes ghosting while avoiding unnecessary high-frequency scanning. The system displays all inputs correctly and provides a clear sizing rule for wider inputs and more digits. The approach is fully synthesizable, resource-light, and portable to larger word-lengths and displays.
Ahmed Mohamed Abdellatif Abdelrahman Elngar
Hardware simulation of cooperative adaptive cruise control based on fuzzy logic
Cooperative adaptive cruise control (CACC) is a system designed to maintain a vehicle’s distance according to the driver’s preset value. It is an advancement of adaptive cruise control (ACC), which suffers from response delays when reacting to changes in the leading vehicle’s speed. CACC addresses this limitation by reducing response delay through the use of speed data from the preceding vehicle, obtained via wireless communication. In this research, the CACC system was simulated using a 1:10 scale remote-control (RC) car equipped with ultrasonic sensors and radio frequency (RF) communication. Speed control was implemented using a fuzzy logic controller with the Mamdani inference method, while an active steering assistance system was added to maintain lane alignment. Hardware simulation results demonstrate that the CACC system functions effectively and significantly improves the response time of the following vehicle when the leading vehicle changes speed. Experimental results show that the CACC maintained the desired following distance in 10 seconds, compared to 15 seconds for the ACC system.
Performance assessment of an adaptive model predictive control with torque braking for lane changes
The growing demand for autonomous vehicles requires robust control systems that can maintain safety during complex maneuvers like lane changes. However, a significant research gap exists in developing controllers that effectively manage the combined challenges of steering and braking across diverse and unpredictable driving conditions, such as varying speeds and low-friction road surfaces. This research addresses this gap by proposing and evaluating an adaptive model predictive control (MPC) system integrated with a torque braking distribution strategy. The key advantage of our adaptive method is its ability to continuously update its internal model in real-time, allowing it to anticipate and respond to changing road friction and vehicle dynamics more effectively than a static controller. In simulations of a lane change maneuver across speeds of 10-25 m/s and road friction levels from 0.3 (icy) to 1 (dry asphalt), the proposed system demonstrated a substantial performance improvement. The proposed framework demonstrated a 52.8% average reduction in lateral tracking error and enhanced stability by reducing the yaw rate by up to 41.8% on low-friction surfaces, compared to a non-adaptive MPC baseline. These results quantitatively confirm that our framework’s synergistic coordination of steering and braking significantly enhances the safety, precision, and reliability of autonomous lane change maneuvers.
Adaptive fuzzy sliding mode control with exponential reaching law and MPL method for the coupled-tank system
This study develops an adaptive fuzzy sliding mode control (ASMC) scheme incorporating an exponential reaching law (ERL) and a minimum parameter learning (MPL) strategy to achieve liquid-level regulation in a coupled-tank system. Such systems are widely used in industrial applications, including chemical and petrochemical processing, water treatment, power generation, and the manufacturing of construction materials, as well as in boilers, evaporators, reactors, and distillation columns. The ERL-based sliding mode controller is formulated to guarantee finite-time tracking of the desired liquid level while effectively suppressing chattering near the sliding surface. The MPL approach is embedded within the fuzzy system (FS), resulting in a single online adaptive parameter, which significantly reduces computational complexity and enhances real-time performance. The stability of the closed-loop system is rigorously established using Lyapunov theory. Simulation studies conducted in MATLAB/Simulink validate the effectiveness of the proposed controller, demonstrating a rise time of 6.1918 s, a settling time of 11.2553 s, zero overshoot, convergence of the steady-state error to zero, and a noticeable reduction in chattering.
Simultaneous faults diagnosis and prognostic in induction motor drives under nonstationary conditions
In this paper, an auto regressive (AR) model-based approach is applied in the stator current analysis under non-stationary conditions (case of frequency variation due to variable speed operation). Under these conditions, the identification of fault signatures is almost impossible due the variation of the fundamental frequency using conventional analysis methods. Moreover, this approach is used in the diagnosis of multiple faults occurring simultaneously in induction motor drives. In this aim, the stator current signal is decomposed into short segments then the AR modeling approach is applied on each segment. This approach called short-time ROOT-AR is then applied to solve the problem of the non-stationarity of the stator current signal under variable speed operation. The efficiency of the short-time ROOT-AR approach is evaluated through experimental tests in the diagnosis of multiple faults occurring simultaneously in induction motor drive. Finally, the superiority of the proposed approach is highlighted in comparison with conventional techniques in terms of accuracy, computational time and robustness against the noise.
Smart hydroponic greenhouse with solar energy for urban agriculture
Increased industrial activity in South Cikarang has limited the availability of agricultural land, encouraging the adoption of controlled environment agriculture systems. This study describes the design and implementation of a smart hydroponic greenhouse that is fully supported by a 600 Wp solar photovoltaic (PV) system and controlled using an industrial-grade programmable logic controller (PLC). This system automatically regulates temperature and humidity through exhaust fans and sprayers based on real-time sensor feedback. Experimental results show that when the internal temperature exceeds 31 °C, the control system recovers to 29.7 °C within 15 minutes and maintains a temperature range of 24–30 °C. Relative humidity is maintained within the optimal range of 75–90%. The PV system produces an average daily energy output of approximately 2.0 kWh, resulting in an energy self-sufficiency ratio (ESR) of 138%, which indicates excess energy production compared to system demand. These results prove that the integration of industrial automation with renewable energy provides reliable environmental control, high energy efficiency, and operational stability for hydroponic greenhouse applications in urban industrial areas.
Business intelligence for measuring global systems for mobile communication provider performance
Internet access is getting easier in various places, including Indonesia. Telecommunication media are no longer dominated by the use of pulse signals but have shifted to relying on internet access. This study aims to create a data visualization of internet speed in Bekasi urban sub-districts using the business intelligence (BI) model with online analytical processing (OLAP). Clustering was carried out using two methods, namely the K-means and K-medoids methods which were selected based on the Davies Bouldin index (DBI) value. This study produced a visual data prototype from the results of clustering from the data mining process and was accompanied by supporting data in the form of information on the highest and lowest speeds in the studied sub-districts. The clustering process uses K-means for uploading data with a DBI value of 0.847, while the data download uses K-medoids with a DBI value of 0.871. The prototype displays observation data, maximum and minimum value information, and the clustering result. The functional test result for the prototype showed conformity with the requirements, while the validation test showed that the prototype passed the validation test with a score of 0.8833.