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31,456 Article Results

Classifying Arabic handwriting using convolutional neural network

10.11591/ijai.v15.i5.pp4015-4025
Anas Quteishat , Asharaf Al Sharah , Ahmed Qtaishat , Tareq Alawneh
This paper presents a classification of Arabic handwritten letters using convolutional neural networks (CNNs). The paper aims to address the unique challenges of recognizing Arabic letters due to their special characteristics such as dots and similarities. Leveraging deep learning, particularly CNNs, the project employs an architecture specifically designed for image classification tasks, which is well-suited for optical character recognition (OCR) of Arabic handwritten letters. The dataset used consists of images collected from the "Arabic handwritten alphabets, words, and paragraphs" (AHAWP) and "Arabic handwritten character database" (AHCDB), which have been carefully processed to ensure uniformity and diversity. The model was built using deep network designer, a tool in MATLAB, and trained over three epochs to prevent overfitting, achieving an accuracy of 89.2%.
Volume: 15
Issue: 5
Page: 4015-4025
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

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

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

Traffic flow optimization using reinforcement and thread-based V2X communication for smart traffic management

10.11591/ijai.v15.i5.pp4061-4070
Parul Arora , Ritika Wason , Devansh Arora , M. N. Hoda , Sumit Gupta
This research proposes an advanced framework that integrates reinforcement learning techniques with thread-based vehicle-to-everything (V2X) communication protocol to address urban traffic jams, which lead to increased delay, inefficient fuel consumption, and deteriorating air quality, posing a significant challenge to sustainable living. This framework empowers cars to “talk” directly to each other as well as with traffic signals, adapting to the real-time flow on the roads. By placing the decision-making process closer to where it’s needed, right at the network’s edge, the system is able to respond quickly to changes in traffic without relying on the usual pre-set signal cycles. Unlike many traditional traffic management solutions that are slow to adjust, the proposed framework allows vehicles and intersections to coordinate in real time. This reduces unnecessary waiting, smooths out traffic flow, and helps cut down on emissions. Through detailed simulations using the simulation of urban mobility (SUMO) traffic model, it was found that this system led to significant improvements: average waiting time was reduced by over 40%, queue lengths were cut by more than half, and vehicle throughput increased by nearly 30%. Additionally, fuel consumption dropped by approximately 24%, and carbon dioxide (CO2) emissions were reduced by more than 25% compared to existing systems.
Volume: 15
Issue: 5
Page: 4061-4070
Publish at: 2026-10-01

Evaluating YOLOv8-pose variants for soccer keypoint detection and homography mapping

10.11591/ijai.v15.i5.pp4819-4829
Durrotun Nashihin , Yohanes Yohanie Fridelin Panduman , Dwi Cahyo Kartiko , Anna Noordia , Khoirul Islam
Soccer analytics intensely relies on accurate pitch keypoint detection and homography mapping, yet soccer broadcast data poses challenges caused by camera angle variation and partial pitch coverage. This study systematically evaluates you only look once version 8 (YOLOv8)-pose variants (nano, small, medium, large, and x-large) for keypoint detection and homography mapping. These models were retrained on broadcast and scouting datasets, while homography mapping was computed using direct linear transform (DLT) and random sample consensus (RANSAC). Three metrics were used for evaluation: detection accuracy, computational efficiency, and homography reliability. Results showed that the medium variant achieved the best balance, with the lowest mean pixel error (35.67 px), competitive inference speed 49.6 frames per second (FPS), and moderate model size (52.2 MB). Furthermore, it also reached the highest homography success rate (96.2%), surpassing all other variants. These findings suggest that the medium variant is the most effective balance for real-time soccer analytics, providing robust performance in accuracy, efficiency, and reliability under diverse broadcast data. Future studies would focus on enlarging the dataset with additional keypoints, incorporating temporal smoothing, and integrating a player tracking system. For reproducibility, the dataset and code are openly available on GitHub.
Volume: 15
Issue: 5
Page: 4819-4829
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

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

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

Language anxiety, speaking proficiency, and language learning beliefs among English major students: a mediation analysis

10.11591/ijere.v15i5.38887
Charmaine Kaye Puno Balboa , Gideon Sindad Sumayo
This study examined the relationship between English language anxiety and speaking proficiency among Bachelor of Secondary Education (BSEd) major in English students at the University of Southern Mindanao (USM)–Libungan Campus, with language learning beliefs as a mediating variable. Many tertiary learners experience anxiety in speaking English, yet the mechanisms through which this affects oral performance remain unclear. Using a quantitative descriptive–correlational design, data were collected from 150 students through standardized questionnaires and a speaking proficiency assessment. Descriptive statistics, regression analysis, and mediation analysis were employed to analyze the data. Results showed that students experienced English language anxiety at an “agree” level, with fear of negative evaluation as the most prominent factor. Their speaking proficiency was generally at a modest level, while language learning beliefs were positive, particularly in terms of motivation and expectations. Regression results indicated that language anxiety did not directly predict speaking proficiency. However, mediation analysis revealed that language learning beliefs significantly mediated the relationship between anxiety and speaking performance. The findings suggest that anxiety influences speaking proficiency primarily through learners’ beliefs about language learning. Addressing both emotional and cognitive factors is therefore essential in improving students’ oral communication skills.
Volume: 15
Issue: 5
Page: 4625-4633
Publish at: 2026-10-01

Teacher readiness and blended learning implementation in Chinese vocational colleges: a quantitative survey study

10.11591/ijere.v15i5.38761
Ding Ge , Mohamad Zuber Abd Majid
Teacher readiness remains a key constraint on blended learning adoption in vocational education, yet limited research has examined how multiple readiness dimensions jointly shape implementation among vocational economics educators in China. This study investigated how technological literacy (TL), attitudes toward technology (AT), educational service quality (ES), and knowledge to create content (KC) predict teacher readiness for blended learning (RL), and how readiness influences actual practice among 390 economics teachers in vocational colleges in Hunan Province, China. A quantitative cross-sectional survey design was employed. Path analysis showed that all four antecedent variables significantly predicted readiness (R²=37.0%), while readiness significantly predicted blended learning practice (β=0.322, p
Volume: 15
Issue: 5
Page: 4080-4092
Publish at: 2026-10-01

MRI segmentation and classification using 1-D and 2-D empirical mode decomposition

10.12928/telkomnika.v24i5.27976
Giang Hong; University of Science Nguyen , Yen Thi Hoang; University of Science Hua , Ngan Vuong Thuy; University of Science Nguyen , Liet Dang; Dong Nai University Van
This study proposes a new framework for brain magnetic resonance imaging (MRI) tumor detection and classification using 1-D and 2-D empirical mode decomposition (EMD) / bidimensional empirical mode decomposition (BEMD). These adaptive algorithms optimize image denoising, segmentation and feature extraction. Tumors are detected using a global threshold derived from EMD-generated bimodal curves. A visual geometry group 19 (VGG19) model extracts features from the regions of interest (ROI) across both denoised images and BEMD components. The method was validated on the public Figshare brain tumor dataset. For segmentation, the proposed method performed similarly to multilevel Otsu, Kapur-Harmonic, and K-means algorithms. It achieved an average precision of 96.70%, slightly trailing K-means of 97.07%, and a sensitivity of 91.07%, outperforming K-means but falling behind Kapur and Otsu. Notably, it achieved the highest F1-score of 93.70%, tying the Otsu technique. These results highlight enhanced tumor localization, reflecting segmentation that closely aligns with the ground truth. For tumor classification, ensemble learning was used to develop nine models, all of which surpassed 93% accuracy; one achieved 100%, and three reached 96.7%. The proposed method outperformed seven state-of-the-art techniques with a peak accuracy of 99.02%, demonstrating its clinical viability. Overall, integrating EMD and BEMD algorithms provides a superior framework for brain tumor segmentation and classification compared traditional methods.
Volume: 24
Issue: 5
Page: 1728-1744
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

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
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