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31,456 Article Results

Quantitative chest computed tomography-based hybrid deep learning for chronic lung disease detection and staging

10.11591/ijai.v15.i5.pp4568-4591
Garima Jain , Amit Kumar Goel , Rahul Kumar
This research proposes a hybrid deep learning model to accurately detect chronic lung diseases in chest computed tomography (CT) scans. Current deep learning methods, such as convolutional neural networks (CNNs) and transformer models, often suffer from high computational requirements and low interpretability. This research combines a pre-trained residual network 50 layers (ResNet50) for deep feature extraction with hand-crafted texture features. The features are combined via hybrid concatenation, and the model is classified using an optimized extreme gradient boosting (XGBoost) ensemble with hyperparameter tuning to improve performance and reduce overfitting. This research compared the proposed model with radiomics-only, ResNet50 CNN, vision transformer (ViT-B16), and Swin transformer methods. This research used a public dataset containing adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and normal classes. The classification accuracy of the hybrid framework was 92.38%, with better precision, sensitivity, specificity, F1-score, and receiver operating characteristic-area under the curve (ROC-AUC) than the baselines. A dual-mode explainable artificial intelligence (XAI) framework integrating gradient-weighted class activation mapping (Grad-CAM) and Shapley additive explanation (SHAP) was implemented to enable spatial lesion localization and feature attribution for clinical transparency. Grad-CAM was able to localize the lesions successfully, and SHAP revealed the influence of entropy-based texture features and semantic embeddings on the predictions. Overall, the framework develops advanced XAI systems for chest CT analysis.
Volume: 15
Issue: 5
Page: 4568-4591
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

Performance analysis of convolutional neural network-based image classifiers for agricultural farmland mapping

10.11591/ijai.v15.i5.pp4403-4411
Anjela C. Tolentino , Anazel P. Gamilla , Amir Ledesma
Precise farmland mapping is essential for crop monitoring, land-use inventory, and subsidy audits. Particularly, the office of the municipality’s assessor or the registry of deeds needs adequate image classifiers to categorize farmland on a large scale across different seasons and regions. The study evaluated three convolutional neural network (CNN) models, such as efficient network baseline 3 (EfficientNetB3), residual network 50 layers version 2 (ResNet50V2), and inception version 3 (InceptionV3), to identify the model that best balances accuracy and efficiency for classifying high-resolution farmland imagery. EfficientNetB3 proved to be the most suitable model, demonstrating higher accuracy, robustness, and a stronger ability to identify rice farmland patterns. While ResNet50V2 and InceptionV3 are widely used for image analysis, their performance declined when processing farmland images that exhibit substantial differences in visual characteristics. EfficientNetB3 delivered more consistent results for farmland image classification in large-scale agricultural applications. These findings highlight its potential for supporting data-driven agricultural management and policy implementation.
Volume: 15
Issue: 5
Page: 4403-4411
Publish at: 2026-10-01

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

Confidence-driven feature selection and ensemble classification using ResNet, AlexNet, and DenseNet for scene recognition

10.11591/ijai.v15.i5.pp4523-4539
Sumathi Krishnamurthy , Shetty Pramod Kumar , Halasur Rudrappa Mahadevaswamy , Ujwala Bagepalli Satishbabu
Indoor scene recognition is a significant problem because of its structural complexity, illumination variations, and visual similarities between the various scene classes. Recent approaches use convolutional neural networks (CNNs) and transfer learning to extract features for classification. In these methods, all the features were considered for classification, which leads to an increase in computational complexity and training time. This leads to more time consumption for processing. To address these challenges, this study proposed a hybrid indoor scene recognition framework that integrates illumination-aware pre-processing, multi-model deep feature extraction, and confidence-based feature fusion. The pre-processing technique uses contrast-limited adaptive histogram equalization (CLAHE) and Retinex enhancement. Deep features are extracted using Alex Krizhevsky network (AlexNet), densely connected convolutional network (DenseNet), and residual network 101 layers (ResNet101) to capture spatial and semantic information. The features extracted are fed to confidence-driven world cup optimization (CD-WCO) to extract the significant features. The final representation is classified using a deep liquid state machine (DLSM) classifier to enhance class discrimination. Our experiments, conducted on the NYU indoor scene dataset with 20 scene categories and the MIT Indoor-15 class subset results indicate the effectiveness of the proposed framework, achieving an overall classification accuracy of 96.5% and 95.2%, respectively.
Volume: 15
Issue: 5
Page: 4523-4539
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

Design and implementation of an IoT-based PLC-HMI trainer for pneumatic control system education

10.12928/telkomnika.v24i5.27815
Chairul Gagarin; Universitas Trisakti Irianto , Singgi; Universitas Trisakti Fadly , Endang; Universitas Trisakti Djuana , Dianing; Universitas Trisakti Novita Nurmala Putri , Tyas; Universitas Trisakti Kartika Sari , Gema; Universitas Trisakti Takbir Yasir
Industrial automation education requires practical learning media that integrate control systems, visualization, and physical processes. However, many pneumatic training systems used in laboratories still operate as standalone devices and lack system integration and remote monitoring capability. This study presents the design and implementation of an internet of things (IoT)-based programmable logic controller (PLC) and human-machine interface (HMI) trainer for pneumatic control education. The proposed platform integrates PLC control, HMI visualization, pneumatic actuators, and IoT monitoring within a unified learning environment. A modular PLC software architecture was developed to support multiple pneumatic learning scenarios using a single control program. Functional testing was conducted to verify system reliability, actuator operation, and communication performance. An educational evaluation using pre-test and post-test assessments was also performed. The results show that the developed trainer operates according to the intended design specifications and improves student understanding. The average student score increased from 62 in the pre-test to 84 in the post-test, representing an improvement of approximately 35.5% in learning performance. These results demonstrate that the proposed PLC–HMI–IoT trainer effectively supports practical learning in pneumatic automation education.
Volume: 24
Issue: 5
Page: 1779-1794
Publish at: 2026-10-01

Optimization of MSMEs promotion strategy using GIS with random forest algorithm and A-star algorithm

10.12928/telkomnika.v24i5.26692
Sulyono; Darmajaya Institute of Informatics and Business Sulyono , Suci; Darmajaya Institute of Informatics and Business Mutiara , Agus; Darmajaya Institute of Informatics and Business Rahardi , Sri; Darmajaya Institute of Informatics and Business Lestari , Yulmaini; Darmajaya Institute of Informatics and Business Yulmaini , Ruki Rizal; Darmajaya Institute of Informatics and Business Nul Fikri , Ali; Darmajaya Institute of Informatics and Business Syarifuddin
Micro, small, and medium enterprises (MSMEs) make a main contribution to improving a country’s economy, but it still faces various problems including limited market access, ineffective promotion, and lack of understanding in business profiles. The purpose of this study is to design a geographic information system (GIS) combined with the random forest (RF) algorithm and the A-star algorithm. The RF algorithm functions are to classify MSMEs. Meanwhile, the A-star algorithm is to recommend the shortest route to support accessibility. In addition, it is equipped with a dashboard and MSMEs profile as a reference for stakeholders in making MSMEs development policies and as information to the public in order to expand the reach of promotion in introducing MSMEs and attracting investors, thus optimizing MSMEs promotion strategies. In addition, the evaluation results of the classification model show an accuracy value of 99.6%, indicating that the model is able to predict very well, making it highly recommended for MSME classification.
Volume: 24
Issue: 5
Page: 1526-1534
Publish at: 2026-10-01

Review on badminton players hand-grip analysis and tactical dynamics prediction using egocentric vision

10.12928/telkomnika.v24i5.27915
Sriramachandra; Alliance School of Advanced Computing Murthy K. , Shekhar; Alliance School of Advanced Computing R. , Athira B.; Alliance School of Advanced Computing Kaimal
Many works have already been carried out on computer vision-based sports analytics, activities in egocentric vision to improve player performance. But less is explored in terms of vision based kinematic analysis in badminton where shuttle trajectory is explored, angles are derived from features and analyzing biomechanics of the player first person vision cameras. This paper reviews existing research on badminton hand-grip analysis and tactical prediction using egocentric vision. It highlights key methods, datasets, and challenges in applying computer vision techniques to sports analytics. The review focuses on computer vision approaches for analyzing hand-grip patterns and predicting tactical dynamics in badminton using egocentric video data. The study summarizes current advancements, identifies research gaps, and outlines future directions for improving egocentric-vision-based sports analytics.
Volume: 24
Issue: 5
Page: 1721-1727
Publish at: 2026-10-01

Internet of things-based smart irrigation system using soil moisture and weather data

10.12928/telkomnika.v24i5.27889
Zakarie; Universiti Teknologi Malaysia (UTM) Abdi Mohamud , Rozeha; Universiti Teknologi Malaysia (UTM) Binti A. Rashid , Yazid; Universiti Teknologi Malaysia (UTM) Abubakar Sufyan
Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.
Volume: 24
Issue: 5
Page: 1690-1698
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

Engineering orchestration: adaptive and human-centred design for complexity, collaboration, and resilience

10.12928/telkomnika.v24i5.3777
Tole; Universitas Ahmad Dahlan Sutikno
Engineering design is undergoing a profound transformation as electrical and computer engineering systems become increasingly interconnected, intelligent, and embedded within dynamic socio-technical environments. Building upon systems thinking, future engineering can no longer rely solely on deterministic optimization or fixed design assumptions but must instead embrace adaptive, resilient, and human-centred approaches capable of responding to uncertainty and continuous change. This editorial argues that these developments fundamentally redefine engineering design as the orchestration of adaptive socio-technical systems that continuously align technological intelligence, human expertise, resilience, and lifecycle evolution. Adaptive engineering extends traditional design principles through continuous learning, feedback, and system reconfiguration, while human centred engineering ensures that technological capabilities remain aligned with human judgement, values, and operational needs. Resilience further enables engineering systems to anticipate, absorb, recover from, and adapt to evolving disturbances across increasingly complex operating environments. Engineering excellence therefore depends not merely on optimizing technological performance but on orchestrating these complementary capabilities into coherent engineering ecosystems capable of generating enduring value. This evolving perspective establishes a conceptual foundation for the future of electrical and computer engineering, where engineering orchestration enables adaptive design, human collaboration, resilient innovation, and ultimately responsible and sustainable engineering practice.
Volume: 24
Issue: 5
Page: 1449-1456
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

Improving PaddleOCR optical character recognition for food composition labels using hybrid SymSpell and LayoutLM correction

10.12928/telkomnika.v24i5.27607
Ahmad; Universitas Qomaruddin Wahyu Rosyadi , Siti; Universitas Qomaruddin Ma’shumah , Muhammad; Institut Teknologi Sepuluh Nopember Qomaruz Zaman
A high level of text recognition accuracy is crucial for optical character recognition (OCR) applications, particularly those designed to extract information from food composition labels. However, packaging materials often introduce distortions, reflections, or irregular printing, which lead to recognition errors and word concatenation. To mitigate this issue, this study proposes a novel post-processing system that integrates PaddleOCR with the spelling correction (SymSpell) algorithm and layout language model (LayoutLM)-guided bigram-based word segmentation to enhance OCR accuracy. The implementation begins with capturing a composition label image using a smartphone camera, followed by initial text extraction performed by PaddleOCR. Consequently, SymSpell corrects misspelled tokens through a domain-specific food and beverage lexicon, while LayoutLM leverages spatial and contextual information to separate concatenated words. The performance was evaluated using the character error rate (CER) metric across four packaging surface categories: flat, curved, reflective, and textured. The results demonstrate consistent improvements across all categories, achieving an average CER of 0.0991, with the greatest reduction on flat surfaces (from 0.0885 to 0.0659). The experiments show that the system is robust across label conditions, lightweight for real-time use, and domain-aware. Its model-agnostic design adds flexibility, enabling effective post-OCR correction across different engines and improving readability for real-world food label applications.
Volume: 24
Issue: 5
Page: 1668-1679
Publish at: 2026-10-01
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