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31,456 Article Results

Satellite imagery and deep learning framework for highway construction with minimal vegetation impact

10.11591/ijai.v15.i5.pp4933-4945
Meenakshi Meenakshi , Rajesh Thalwagal Matha
Constructing roads through vegetated terrain forces a trade-off between minimizing ecological harm and preserving constructional ease. This work presents a unified, end-to-end framework that reconciles both objectives, with three major contributions. First, propose a hybrid perception model that fuses a graph convolutional network (GCN)—capturing scene-level spatial context—with a capsule network (CapsNet) that encodes part-whole relationships for reliable detection of small, clustered vegetation; this configuration attains an average precision (AP) of 0.925, average recall (AR) of 0.948 (intersection over union (IoU) = 0.5), and 98% land-cover classification accuracy on unseen data. Second, we introduce an ecological-sensitivity cost map fusing vegetation normalized difference vegetation index (NDVI) with terrain slope, over which we evaluate Dijkstra, A*, a novel Eco-Aware A* embedding a vegetation-avoidance penalty, and an non-dominated sorting genetic algorithm II (NSGA-II) multi-objective search that jointly minimizes path length and vegetation damage. Third, on a benchmark modeled on the Bengaluru–Kanakapura road (NDVI = 0.7, slope = 0.3), the eco-sensitive route lowers segment NDVI by roughly 69% and eliminates all high-canopy exposure while adding only about 8% to path length versus an optimized non-vegetated route. Together, these components enable automated, ecologically aware road planning directly from imagery.
Volume: 15
Issue: 5
Page: 4933-4945
Publish at: 2026-10-01

Dual-path one-dimensional convolutional BiLSTM with attention for silk cocoon price forecasting

10.11591/ijai.v15.i5.pp4679-4690
Shivananda Shivanna , Lincy Meera Mathews , Sivagnanam Rajamanickam Mani Sekhar
Accurately forecasting silk cocoon market prices is very crucial for enhancing decision-making silk cocoon supply chain. This work introduces a hybrid deep learning model, the dual-path one-dimensional convolutional neural network–bidirectional long short-term memory with lightweight attention mechanism (DPCBLA), which combines temporal sequence data with static features to predict daily maximum silk cocoon prices. The model captures short-term temporal patterns by using a one-dimensional convolutional neural network (1D CNN), then passes to a bidirectional long short-term memory (BiLSTM) network that captures long-term dependencies, and a lightweight attention mechanism that highlights key time steps for better interpretability. Static inputs such as market variety, seasonal indicators, and weather conditions are processed through a dense layer and integrated with temporal features in a dual-pathway design. The model was trained using a multi-source dataset obtained from the Ramanagara Government Silk Market in Karnataka, India. The dataset comprises historical transaction records, weather information, and seasonal disease data. The proposed model DPCBLA performed better than the traditional convolutional neural network (CNN), long short-term memory (LSTM), and BiLSTM models, achieving a mean absolute error (MAE)of24.73, a root mean square error (RMSE) of 33.93, a mean absolute percentage error (MAPE) of 4.05, a coefficient of determination (R2) of 0.94, and a prediction accuracy of 95.95%.
Volume: 15
Issue: 5
Page: 4679-4690
Publish at: 2026-10-01

Multimodal retrieval-augmented generation with sentiment-aware responses for depression

10.11591/ijai.v15.i5.pp4907-4918
Aluri Suguna Ratnapriya , A. Mary Sowjanya
In view of the rapid growth in conversational interfaces, there is a demand for more adaptive retrieval systems. Traditional chatbots, built on pre-trained models, struggle to understand and respond to a wide range of real-world user queries. In view of this, a framework based on multimodal retrieval-augmented generation with sentiment-aware responses (MRAG-SAR) for depression that addresses the challenges and strengthens conversational interface performance has been developed. The proposed system can process unstructured data in multiple formats of inputs, such as audio, image, and text, using a multimodal late-fusion mechanism, making it suitable for managing diverse, domain-specific information. It also leverages powerful embedding models for capturing contextual similarity and scalable vector databases for efficient retrieval. The user queries are augmented to provide precise and nuanced context-related responses. The proposed framework retrieves content that captures the dimensions of the topic to enhance the relevance of the output responses. Introduction of sentiment-aware response generation allows the system to respond in a tone based on the emotional context of the user queries, which is essential in making use of personalized conversations. Relevant bidirectional encoder representations from transformers score (BERTScore) and other metrics highlight the framework's effectiveness in delivering high-quality responses.
Volume: 15
Issue: 5
Page: 4907-4918
Publish at: 2026-10-01

Dynamic sports environments: automatic feature players tracking and re-identification with similar appearances

10.11591/ijai.v15.i5.pp4292-4303
Adil Abdulhur Abushana , Riyadh Alsaeedi
Tracking multiple players in sports videos remains challenging due to frequent occlusions, similar appearances among players, and rapid motion. This paper presents a comprehensive player tracking framework that integrates deep player re-identification (DPRI), an individual probabilistic overlapping bounding box occupancy mapping (IPOM) for robust spatial localization under severe overlap, and an identity-aware k-shortest path optimization algorithm (KSP-ID) for improved trajectory association. The DPRI module combines pose-based embeddings, jersey appearance cues, and facial features to generate discriminative identity representations. Experimental evaluation on the autonomous production of images based on distributed and intelligent sensing (APIDIS) and Shantou University (STU) datasets demonstrates strong performance, achieving 85.5% multiple object tracking accuracy (MOTA), 69.0% generalized multiple object tracking accuracy (GMOTA), and 90.2% rank-1 re-identification accuracy. Furthermore, the proposed framework significantly reduces identity switches (IDS) compared with conventional k-shortest path (KSP) approaches, probabilistic occupancy models, and recent deep learning-based tracking methods. These results demonstrate that integrating semantic identity information with spatial reasoning enables robust and efficient player tracking in highly dynamic sports environments.
Volume: 15
Issue: 5
Page: 4292-4303
Publish at: 2026-10-01

Machine learning and failure mode and effects analysis for sustainable medical equipment spare parts management

10.11591/ijai.v15.i5.pp4273-4282
Fabian Halley Pata Alban Dattu , Syed Tramizi Syed Shazali , Shirley Johnathan Tanjong , Abdul Rani Achmed Abdullah , Nurlaila Rosli
Spare parts for essential medical equipment are vital to ensure uninterrupted healthcare provision, especially in areas where resources are limited. This paper presents an integrative approach that integrates failure mode and effects analysis (FMEA) with supervised machine learning (ML) models in order to allow better predictive maintenance and optimize management of spare parts. In order to increase the accuracy of the forecast, FMEA was first used to compute risk priority numbers (RPNs) of the components, quantifying the risk of each component, and then using it as an important input feature to the ML algorithms. Data from six hospitals and ten critical medical devices, such as ventilators, defibrillators, dialysis machines, and computed tomography (CT) scanners, were analyzed to identify high-risk failure modes and prioritize maintenance tasks. Three different supervised learning models (random forest (RF), artificial neural network (ANN), and support vector machine (SVM)) was built and tested based on four metrics: accuracy, precision, recall, and F1-score. The precision of RF and ANN was 1.00 and SVM showed an excellent recall value of 0.94, indicating promising results for practical applications. The solution proposed consists of a hierarchical decision system, connecting risk assessment carried out by experts with data-driven forecasts, to create proactive and sustainable management of spare parts. This approach helps healthcare entities lessen the risk of equipment breakdowns, adhere to international healthcare standards, and increase system reliability and efficiency.
Volume: 15
Issue: 5
Page: 4273-4282
Publish at: 2026-10-01

Optimizing cloud energy with deep reinforcement learning: an artificial bee colony-Q-learning hybrid approach

10.11591/ijai.v15.i5.pp4645-4655
Mohammed Ziaur Rahman , Anandaraj Shanthi Pichandi
Load balancing in the cloud is a crucial characteristic for distributing resources from data centers. However, load balancing in a cloud environment remains a challenging issue due to the influx of numerous incoming tasks, particularly in an infrastructure as a service (IaaS) cloud environment. To address the complexity and high dimensionality of the process, machine learning (ML) techniques combined with optimization algorithms are used, which fasten the process and minimize execution time. The deep reinforcement learning (DRL) – artificial bee colony with Q-learning (ABCQ) approach is proposed to solve load balancing in a cloud environment. The agent in DRL makes intelligent decisions to handle a large number of tasks efficiently at lesser time and increasing speed. The artificial bee colony (ABC) algorithm uses a global guiding factor to initialize position updates, enhancing the searchability and performance of the algorithm. The proposed DRL-ABCQ approach optimizes scheduling and resource utilization, increases the throughput of virtual machines (VMs), and balances the load between VMs. The DRL-ABCQ approach achieves a makespan of 125.52 s, energy utilization of 99.81 kJ, and a throughput of 2.39 jobs/s, compared to existing approaches.
Volume: 15
Issue: 5
Page: 4645-4655
Publish at: 2026-10-01

Navigating cybersecurity paradox: opportunities and risks of artificial intelligence integration in healthcare systems

10.11591/ijai.v15.i5.pp3981-3990
Rani Tiyas Budiyanti , Roro Isyawati Permata Ganggi
Artificial intelligence (AI) has been widely used in healthcare services, such as assisting in diagnosis and clinical decision-making. AI can potentially improve cybersecurity within healthcare services. Nevertheless, AI can also pose new threats that are dangerous to the system. The objective of this research is to investigate the contradictory roles that AI plays in the context of healthcare security. This study was a bibliometric study and systematic review from the Scopus database that were published in 2015-2025. This research shows that AI can bring benefits for security, such as enhanced threat detection, automation, and efficiency, as well as improved patient care. However, AI has many risks and challenges in healthcare security, such as new vulnerabilities, data privacy concerns, and ethical and bias issues. Integration of AI in healthcare services, especially to strengthen cybersecurity, needs to be accompanied by clear regulations, robust data protection standards, and ongoing supervision. In addition, principles of patient data security and privacy must be carefully observed to balance the benefits of using AI in healthcare security with the potential risks that may arise.
Volume: 15
Issue: 5
Page: 3981-3990
Publish at: 2026-10-01

MatchIA: a semantic artificial intelligence solution for interoperability in schema matching

10.11591/ijai.v15.i5.pp4213-4223
Mohamed Raoui , Moulay Hafid El Yazidi , Ahmed Zellou
Schema matching plays a crucial role in data integration by aligning attributes from heterogeneous sources. Traditional rule-based and statistical techniques often lack the flexibility to adapt to diverse schema structures, requiring extensive manual effort. To address these challenges, this paper introduces MatchIA, a machine learning solution that combines word embeddings and random forest to enhance the accuracy of schema matching. The approach accounts for both structural dependencies and semantic relationships between attributes, enabling more automated, and reliable correspondence identification. Unlike traditional methods, MatchIA leverages pretrained embeddings to capture contextual similarities and integrates learned patterns to generalize across various domains. Experimental evaluations on standard datasets demonstrate that MatchIA achieves a precision of 0.94, a recall of 0.92, and an overall matching accuracy of 0.93, significantly outperforming state-of-the-art methods. These results highlight the potential of MatchIA for real-world applications requiring scalable and automated data interoperability.
Volume: 15
Issue: 5
Page: 4213-4223
Publish at: 2026-10-01

A unified artificial intelligence-enabled intelligent home assistant robot for smart automation and safety

10.12928/telkomnika.v24i5.27862
Md. Taslim; University of Frontier Technology Bangladesh (UFTB) Arif , Abu Salyh; University of Frontier Technology Bangladesh (UFTB) Muhammad Mussa , Jul Jalal; Daffodil International University (DIU) Al-Mamur Sayor , Md.; University of Frontier Technology Bangladesh (UFTB) Ashiqussalehin , Md. Mehedi; University of Frontier Technology Bangladesh (UFTB) Hasan , Mst Deloara; University of Frontier Technology Bangladesh (UFTB) Khushi , Md. Moshiur; University of Frontier Technology Bangladesh (UFTB) Rahman
Intelligent home assistant systems are increasingly required to enhance safety, efficiency, and user convenience in smart environments. This paper presents LUNA, a unified artificial intelligence (AI)-enabled home assistant robot de signed for smart automation and safety in indoor settings. The system integrates autonomous navigation, You Only LookOnceversion8(YOLOv8)-basedobject detection, gesture-based interaction, real-time gas and fire hazard detection, mo bile application control, and automated floor cleaning within a single low-cost embedded platform. Unlike existing systems that focus on isolated functional ities, the proposed approach combines perception, mobility, safety monitoring, and user interaction in a coordinated framework suitable for real-world appli cations, particularly for elderly and disabled users. The system is implemented using Arduino, Raspberry Pi, and ESP32 to enable efficient sensing, processing, communication, and actuation. Experimental results demonstrate robust perfor mance, achieving 93.5% navigation accuracy, 91.2% object detection accuracy, 2.5 s emergency response time, 1.2 s control latency, and 88.7% cleaning effi ciency. These results indicate that LUNA is an effective and practical solution for next-generation smart home assistance.
Volume: 24
Issue: 5
Page: 1680-1689
Publish at: 2026-10-01

Machine learning-based joint optimization for LoRaWAN reliability and energy efficiency

10.12928/telkomnika.v24i5.27916
Nana; University of Indonesia Rusdiana , Novi; University of Indonesia Indriyani , Adi; University of Indonesia Nugroho , Riri; University of Indonesia Fitri Sari , Dino; University of Indonesia Budi Prakoso
Dense long-range wide area network (LoRaWAN) deployments often experience reduced packet delivery reliability (PDR) and increased energy consumption due to suboptimal adaptive data rate (ADR) mechanisms. This study proposes a machine learning (ML) based joint optimization method to improve PDR or reliability while minimizing node energy consumption. The proposed solution employs random forest (RF) and light gradient boosting machine (LightGBM) models, which are suitable for resource constrained end devices (ED). Using combinations of data rate (DR), frequency channel, and transmission power (TxPower) from 500 ED, the model extracts 14 features for parameter optimization. Unlike conventional ADR, the proposed approach can learn complex relationships beyond the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) parameters. The model achieves higher prediction accuracy within a 5 km coverage area. Network simulator 3 (NS-3) simulation results demonstrate better performance and energy efficiency compared to conventional ADR. The proposed approach improves PDR by 6.1–11.6% and reduces node energy consumption by 20–25%, providing a better balance between communication reliability and energy efficiency in scalable LoRaWAN networks.
Volume: 24
Issue: 5
Page: 1581-1596
Publish at: 2026-10-01

NiteTron: a novel nighttime bunaken sea turtle species detection

10.12928/telkomnika.v24i5.27678
Muhamad; Sam Ratulangi University Dwisnanto Putro , Jane; Sam Ratulangi University Litouw , Abdul; Sam Ratulangi University Haris Ontowirjo , Sherwin Reinaldo; Sam Ratulangi University U. Sompie , Hebron; Sam Ratulangi University Prasetya
Automated monitoring is essential for sea turtle conservation, and vision-based algorithms offer a non-intrusive approach to observing turtles without disturb ing their natural habitat. However, nighttime conditions introduce significant challenges, as low illumination severely reduces the visibility of discriminative features. This limitation requires robust feature extraction to capture the lim ited important features. Recent advances in computer vision, such as you only look once (YOLO) 11-nano, enable real-time detection and allow optimization under challenging conditions. In this study, we propose NiteTron, a nighttime sea turtle species detector that uses a lightweight architecture with enhanced feature refinement. The network modifies YOLO11-nano to create a more ef ficient variant, YOLO11-pico. To improve detection in low-light scenarios, we introduce a strong feature modulation (SFM) module. This module enhances attention to relevant features by expanding the receptive field in the final back bone stage. We also incorporate a sparse separated convolution (S2C) module to extract key information efficiently while balancing accuracy and computational cost. The proposed model achieves high precision with fewer parameters and lower giga floating-point operations per second (GFLOPs) than YOLO11-nano. Experiments demonstrate superior performance, achieving 0.682 mean average precision (mAP)@0.5:0.95 for nighttime sea turtle detection. Inference tests also confirm efficient central processing unit (CPU) deployment, reaching 21.18 frames per second (FPS) on a desktop and 2.65 FPS on an edge device.
Volume: 24
Issue: 5
Page: 1561-1580
Publish at: 2026-10-01

Adaptive fuzzy proportional integral derivative control for azimuth rotational positioning system

10.12928/telkomnika.v24i5.27906
Mufid; UIN Sunan Gunung Djati Bandung Ridlo Effendi , Rina; UIN Sunan Gunung Djati Bandung Mardiati , Muhammad; UIN Sunan Gunung Djati Bandung Bagja Nasrulloh , Aan; Politeknik Manufaktur Bandung Eko Setiawan
This paper presents an adaptive fuzzy logic-based proportional integral derivative (PID) control strategy for an azimuth rotational positioning system characterized by nonlinear angular dynamics. Conventional fixed gain PID controllers often experience performance degradation when operating across varying error regions, resulting in slower responses and residual steady-state errors. To address this limitation, a region-based fuzzy gain scheduling mechanism is proposed to dynamically adjust the proportional (𝐾𝑝), integral (𝐾𝑖), and derivative (𝐾𝑑) gains according to the instantaneous angular error. The proposed controller is implemented on a laboratory-scale embedded platform consisting of a stepper motor actuator, potentiometer-based angular feedback, and an Arduino Nano controller. Experimental evaluations were conducted at reference angles of 45°, 90°, 135°, and 180° and compared with a conventional PID controller. Results demonstrate that the adaptive fuzzy–PID controller completely eliminates steady-state error at all tested setpoints and reduces rise time by up to 56.7%, depending on the operating condition. The adaptive gain adjustment enables faster transient response for large angular deviations while maintaining stable convergence near the setpoint. These findings demonstrate that the proposed embedded region-based adaptive fuzzy–PID approach provides an effective and practical solution for improving positioning accuracy and dynamic performance in rotational positioning systems.
Volume: 24
Issue: 5
Page: 1795-1804
Publish at: 2026-10-01

Web traffic optimization and institutional branding in higher education

10.11591/ijere.v15i5.40342
Mohammad Syahidul Haq , Ahmad Abdullah Zawawi , Muhamad Sholeh , Mufarrihul Hazin , Muhammad Ivan Dwi Saputra
Digital visibility has become an important component of institutional branding in higher education, yet limited empirical evidence explains how faculty-level website optimization contributes to brand image formation. This study evaluates web traffic optimization as a strategic capability using the context, input, process, output, and outcome (CIPOO) framework. An evaluative quantitative design was applied at Universitas Negeri Surabaya, involving 341 respondents consisting of 11 faculty website administrators and 330 website users. Data were collected through a five-point Likert-scale questionnaire and analyzed using descriptive evaluation and exploratory Pearson correlation based on aggregated faculty-level scores. The results show that context and outcome achieved the highest evaluation scores, indicating strong institutional alignment and positive branding outcomes, while process showed greater variability across faculties. Correlation analysis revealed a very strong significant positive association between output and outcome, whereas the input–process and process–output relationships were positive but not statistically significant. The novelty of this study lies in integrating faculty-level web traffic optimization, digital governance, and institutional branding within a single CIPOO-based evaluation model. These findings suggest that universities should strengthen website governance, digital capacity, and traffic monitoring to support institutional brand image.
Volume: 15
Issue: 5
Page: 3882-3891
Publish at: 2026-10-01

Robustness analysis of distributed CFAR detection with K-out-of-L fusion in heterogeneous environments

10.12928/telkomnika.v24i5.27784
Hung Van; Le Quy Don Technical University Pham , Tinh Dinh; Le Quy Don Technical University Nguyen
To operate effectively in complex environments, distributed radar systems re quire a robust constant false alarm rate (CFAR) processor. Conventional distributed cell-averaging (D-CA) CFAR detectors are optimal in homoge neous noise. However, they suffer severe performance degradation in non homogeneous scenarios, specifically under multiple interfering targets and abrupt clutter edges. This study presents a comprehensive performance analysis of distributed order statistics (D-OS) CFAR systems employing the K-out-of-L fusion rule. We derive the exact global detection and false alarm probabilities using the Poisson binomial distribution. These models are implemented via vari able precision arithmetic to ensure numerical stability under extreme masking conditions. Detection thresholds are rigorously calibrated to maintain a fixed global false alarm rate of 10−4 in homogeneous backgrounds. Quantitative re sults demonstrate that D-OS-CFAR significantly outperforms the D-CA-CFAR baseline. It maintains stable detection performance (PD > 0.9) under clutter power variation and suppresses false alarm surges at clutter edges to approxi mately 4.5 × 10−4. The analysis concludes that lower-K rules (e.g., K = 1,2) offer the optimal trade-off, limiting the homogeneous CFAR loss to below 0.5 dB while ensuring stable operation in highly heterogeneous environments.
Volume: 24
Issue: 5
Page: 1699-1709
Publish at: 2026-10-01

Analysis of software-defined network on Proxmox with quality of-service testing using Open vSwitch

10.12928/telkomnika.v24i5.27549
Shafira; STMIK AMIK Bandung Febriani , Riezkan; STMIK AMIK Bandung Aprianda Firmansyah
This study designs and implements a virtual network based on software defined networking (SDN) in the Proxmox Virtual Environment (VE) using Open vSwitch (OVS) and OpenDaylight (ODL) controller, with performance testing of quality of service (QoS). The parameters tested include rate limiter, throughput, packet loss, and forwarding rate. Proxmox VE is selected for its centralized virtualization and clustering capabilities, while OVS manages network traffic among virtual machines (VMs). ODL is chosen for its compatibility with OpenFlow and OVS database (OVSDB) protocols, essential for OVS management. Testing uses iPerf3 to generate network traffic and analyze performance, including database service access simulation to mimic typical cloud application traffic loads. Forwarding rate measures the system’s packet forwarding capability per unit time, providing a comprehensive network performance overview. Results contribute to optimized SDN-based virtual network implementations in Proxmox environments and provide scientific and practical references for developing QoS-based virtual networking systems.
Volume: 24
Issue: 5
Page: 1504-1512
Publish at: 2026-10-01
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