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30,185 Article Results

Technological and digital literacy challenges in implementing flipped learning: insights from Eastern Indonesia

10.11591/ijere.v15i2.37784
Haerazi Haerazi , Lalu Ari Irawan , Rimajon Sotlikova , Moti Alemayehu
This study explores the challenges faced by English as a foreign language (EFL) learners and teachers in Eastern Indonesia when implementing flipped learning, with a focus on technological access and digital literacy. Despite the potential benefits of flipped learning, such as increased learner autonomy and deeper cognitive engagement, these factors significantly hinder its effectiveness in under-resourced regions. The study employs a qualitative research design, utilizing interviews and questionnaires with 199 EFL learners and 10 certified EFL teachers from both West and East Nusa Tenggara. The findings reveal that limited internet access, lack of personal digital devices, and low digital literacy are the primary obstacles to successful engagement with flipped learning. These challenges prevent learners and teachers from adequately preparing for class, leading to reduced participation in interactive activities and ultimately hindering language acquisition for learners. In response, the study proposes strategies such as improving internet and device access, offering digital literacy training, and adopting a blended learning approach that combines both online and face-to-face learning. The study contributes to the existing literature by providing context-specific insights into the barriers faced by EFL learners in Indonesia and offering practical recommendations for overcoming these challenges to improve the efficacy of flipped learning in similar educational settings.
Volume: 15
Issue: 2
Page: 1776-1786
Publish at: 2026-04-23

Unlocking academic potential: framework for effective research utilization and commercialization in higher education institutions

10.11591/ijere.v15i2.36233
John Joshua Federis Montañez , Anna Liza Mendrique Mateo
Traditional academic research pathways in higher education institutions (HEIs) often emphasize publication and extension activities, while the utilization and commercialization of research outputs remain underdeveloped. This study aims to assess the institutional readiness, strategies, challenges, and success metrics related to research utilization and technology commercialization in state universities and colleges (SUCs), and to develop a framework to strengthen entrepreneurial and innovation-driven practices in HEIs. A mixed-methods approach was employed, combining case study analysis with a survey of nine SUCs in the Bicol Region, Philippines. The survey instrument was developed through key informant interviews (KIIs) and focus group discussions (FGDs) with experts in technology transfer and intellectual property (IP) management, and demonstrated excellent reliability (Cronbach’s α=0.92). Results indicate that all participating SUCs have dedicated offices for IP management and technology transfer, reflecting a high level of institutional readiness. However, major challenges persist, including limited funding, weak industry linkages, gaps in IP policy implementation, and the absence of sustainable revolving funds. Success in commercialization is primarily measured through patent filings, licensing agreements, and revenue generation, with limited use of qualitative impact indicators. The study concludes that while SUCs exhibit strong structural readiness, strengthening policy coherence, funding mechanisms, industry collaboration, and commercialization culture is essential. The proposed framework provides a practical guide for enhancing research utilization and commercialization in HEIs.
Volume: 15
Issue: 2
Page: 1091-1102
Publish at: 2026-04-23

Belonging mediates the relationship between emotional contagion and digital competence among university students

10.11591/ijere.v15i2.37371
Mohamed Ali Nemt-allah , Ghada Mahros Abdelhafiez , Randa Rabie Abdelbadie , Soma Abdelrazek Elfeshawy , Sara Awadallah Mohammed , Ashraf Ragab Ibrahim
Emotional contagion (EC) significantly influences student experiences in university settings, yet its relationship with digital competence—a key requirement for contemporary academic success—remains unclear. This study examined the mediating role of sense of belonging in the relationship between EC and digital competence among Egyptian university students. Two samples from Al-Azhar University were recruited: a psychometric validation sample (N=486) and a main study sample (N=737). Participants completed validated Arabic versions of the sense of belonging inventory, the susceptibility to emotional contagion (SEC) scale, and the digital competence scale for university students. Data were analyzed using correlation analyses and structural equation modeling (SEM) with bootstrap testing. Results showed that sense of belonging partially mediated relationships between both types of EC and digital competence. Positive EC had significant direct effects on belonging (β=.354) and digital competence (β=.195), with 39.4% of its total effect mediated through belonging. Negative EC also had significant direct effects on belonging (β=-.119) and digital competence (β=-.141), with 23.4% mediated through belonging. These findings suggest that higher education institutions should implement belonging-enhancement initiatives to strengthen digital competence and maximize the benefits of positive EC while mitigating negative emotional influences.
Volume: 15
Issue: 2
Page: 1103-1113
Publish at: 2026-04-23

Adoption of artificial intelligence tools for academic writing

10.11591/ijere.v15i2.37993
Nguyen Thu Hoai , Lai Thi Thu Thuy
The rapid advancement of artificial intelligence (AI) presents both significant opportunities and challenges for academic writing. This study investigates the factors influencing the adoption of AI writing tools among lecturers in Vietnam by proposing an integrated theoretical framework that combines the unified theory of acceptance and use of technology (UTAUT) with perceived risk theory (PRT). The model incorporates performance risk (PR) and ethical risk (ER) as key inhibitors alongside the core UTAUT constructs. Data were collected through a cross-sectional survey of 404 lecturers from public universities across North, Central, and South Vietnam, including both public and private educational institutions, and analyzed using structural equation modeling (SEM). The results show that the proposed model has strong explanatory power, accounting for 77.9% of the variance in behavioral intention (BI) and 75.3% in use behavior (UB). All seven hypotheses were supported. Performance expectancy (PE) was the most potent predictor of intention, while PR was the strongest deterrent. Facilitating conditions (FC) and BI were found to be critical antecedents of actual use. The study contributes by empirically validating an integrated UTAUT–PRT framework in the context of AI writing tool adoption. The findings suggest that universities should prioritize performance-enhancing support mechanisms and risk-mitigation policies to promote responsible AI adoption.
Volume: 15
Issue: 2
Page: 1737-1748
Publish at: 2026-04-23

Exposure to nature through an urban natural monument

10.11591/ijere.v15i2.33911
Isabel María Muñoz-García , Jorge Alcántara-Manzanares , Jerónimo Torres-Porras
Society is experiencing a decrease in opportunities to connect with nature, a problem that is particularly acute during childhood. Numerous studies indicate that increasing the frequency of participation in nature-related activities in urban environments, through elements such as interpretive trails and sensory trails, improves important variables such as connection with nature (NC) and biodiversity awareness. Therefore, the objective of this study is to determine whether it is possible to foster NC and improve biodiversity awareness in children through a sensory trail in a natural urban environment. This study is part of a project carried out by an educational association that operates in three schools in the city in collaboration with the University of Córdoba, Spain. Therefore, the student population was determined by the association itself, with a total of 111 students aged 10 to 12 (48% female, 52% male). The study consisted of pre-post analyses, and the instruments used were the Cheng and Monroe NC scale and questions to determine children’s knowledge of environmental biodiversity. Data analysis included descriptive statistics to determine correctly identified biodiversity, correlation analysis between variables, and nonparametric tests to determine significant differences. The results reveal a relationship, before completing the route, between NC and nature awareness, and that the intervention had a positive impact on all variables. It is concluded that sensory routes in urban green spaces are an excellent educational resource for fostering NC in children, and their knowledge about biodiversity.
Volume: 15
Issue: 2
Page: 1227-1236
Publish at: 2026-04-23

Transforming e-government projects by developing a RAF using Scrum integrated with CASE tool in Botswana

10.12928/telkomnika.v24i2.27431
Thapelo; North-West University Monageng , Bukohwo Michael; North-West University Esiefarienrhe
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.
Volume: 24
Issue: 2
Page: 466-480
Publish at: 2026-04-01

Taxonomy of cooperative adaptation level for cooperative adaptive mobile applications

10.12928/telkomnika.v24i2.27542
Berhanyikun Amanuel; Addis Ababa University Gebreselassie , Nuno M.; University of Lisbon Garcia , Dida; Addis Ababa University Midekso
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.
Volume: 24
Issue: 2
Page: 500-513
Publish at: 2026-04-01

Hybrid classical–quantum ensemble learning for real-time flight delay prediction at Tribhuvan International Airport

10.12928/telkomnika.v24i2.27240
Pavan; Civil Aviation Authority of Nepal Khanal , Nanda Bikram; Tribhuvan University Adhikari
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.
Volume: 24
Issue: 2
Page: 527-535
Publish at: 2026-04-01

Score-level biometric information fusion with generalized power mean

10.12928/telkomnika.v24i2.27356
Leila; Ferhat Abbas University Hellal , Naceur-Eddine; Ferhat Abbas University Boukezzoula , Mohamed; Ferhat Abbas University Setif-1 Cheniti , Zahid; State University of New York Polytechnic Institute Akhtar
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.
Volume: 24
Issue: 2
Page: 648-662
Publish at: 2026-04-01

Identification of paleographic curvature using skeletonization and key point detection

10.12928/telkomnika.v24i2.27502
Fadhilatul; Universitas Yudharta Pasuruan Fitriyah , Dian; National Research and Innovation Agency (BRIN) Andriana , Muhammad Zulhaj; Universitas Pembangunan Nasional Veteran Jawa Timur Aliansyah , Lukman; Universitas Yudharta Pasuruan Hakim , Muhammad Faishol; Universitas Yudharta Pasuruan Amrulloh
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.
Volume: 24
Issue: 2
Page: 620-634
Publish at: 2026-04-01

Smart hydroponic greenhouse with solar energy for urban agriculture

10.12928/telkomnika.v24i2.27630
Zeluyvenca; Takumi Polytechnic Avista , Muhammad Asep; Takumi Polytechnic Rizkiawan , Yudha; Takumi Polytechnic Witanto
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.
Volume: 24
Issue: 2
Page: 727-736
Publish at: 2026-04-01

Hybrid PSO-WOA approach for an efficient task offloading in mobile edge computing

10.12928/telkomnika.v24i2.27293
Fatima Zohra; Mustafa Benboulaid University (Batna 2) Cherhabil , Sonia Sabrina; Mustafa Benboulaid University (Batna 2) Bendib , Maamar; Mustafa Benboulaid University (Batna 2) Sedrati , Chahrazad; Mustafa Benboulaid University (Batna 2) Adouane , Sifeddine; Mustafa Benboulaid University (Batna 2) Benflis
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.
Volume: 24
Issue: 2
Page: 514-526
Publish at: 2026-04-01

Adaptive fuzzy sliding mode control with exponential reaching law and MPL method for the coupled-tank system

10.12928/telkomnika.v24i2.27437
Thanh Tung; Vinh Long University of Technology and Education Pham , Le Minh Thien; Saigon University Huynh
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.
Volume: 24
Issue: 2
Page: 707-716
Publish at: 2026-04-01

A comprehensive analysis of feature selection and XAI for machine learning classifiers to recognize guava disease

10.12928/telkomnika.v24i2.27599
Sujon Chandra; University of Frontier Technology, Bangladesh (UFTB) Sutradhar , Md. Mehedi; University of Frontier Technology Hasan
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.
Volume: 24
Issue: 2
Page: 574-587
Publish at: 2026-04-01

Next generation LoRa-based resilient communication system for disaster mitigation and relief operations

10.12928/telkomnika.v24i2.27383
Al-Baraa; Islamic University of Madinah Ebad , Mohammed; Islamic University of Madinah Abaker , Bilal A; Islamic University of Madinah Khawaja , Arshad Karimbu; Islamic University of Madinah Vallappil , Sameer; IQRA University Qazi , Muhammad; National University of Sciences and Technology (NUST) Mustaqim
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.
Volume: 24
Issue: 2
Page: 371-386
Publish at: 2026-04-01
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