Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

27,404 Article Results

General trends on the impacts of evidence-based university accreditation on quality assurance enhancement

10.11591/ijere.v14i3.31271
Nurali Kairanbayev , David Arulraj David
Traditional accreditation process although has relevant impacts on quality assurance in higher education. Research and practices have shown the value of emerging evidence-based university accreditation. The study therefore aims to understand the impacts of evidence-based university accreditation on quality assurance enhancement. The research relied on literature review and document analysis as suitable methods. The study’s results demonstrated that the final decision for academic accreditation should be based on evidence that all stakeholders took part in quality assurance, namely staff and students. This study also explores the university accreditation practices in the United Kingdom (UK), United Arab Emirates (UAE), and Kazakhstan. The analysis presented here allows us to compare and discuss the practices of three different quality assurance practices. The three cases Quality Assurance Agency for higher education (QAA), Commission for Academic Accreditation (CAA), and Independent Kazakh Agency for Quality Assurance in Education (IQAA) indicate relevant use of evidence-based approaches to university accreditations that support quality assurance enhancement, given the explicit approaches grounded in data and evidence. The future of evidence-based approach will be furthered with the support of technology and sophisticated tools that will support explicit policies and practices. This research is expected to benefit researchers, policy makers and practitioners in quality assurance.
Volume: 14
Issue: 3
Page: 1939-1948
Publish at: 2025-06-01

Effect of integrating student-developed videos into a virtual environment

10.11591/ijere.v14i3.32637
Khoo Yin Yin , Mohamad Rohieszan Ramdan
Many users experience loneliness’ and feel disconnected from teachers and friends during online learning. Lack of engagement between teacher and students can hinder learning and lead to unpleasant feelings such as anxiety and a lack of motivation. Hence, some lecturers require students to develop videos in pairs and share them with peers. The purpose of this study was to measure the effect of integrating of student-developed videos into a virtual environment. The quasi-experimental method with was conducted to investigate students’ perceptions of interest, motivation, engagement and performance. A total of 333 students was divided into experimental and control group. Results showed a positive impact of this approach, which can draw out students’ creativity and their understanding of the content knowledge to integrate these with information and communication technology skills. Examination of the qualitative results suggest that the students need to be closely monitored while making the video to prevent free-riders. This study also recommends that the design of the video must be integrated into the course in order to achieve the learning outcome. This study contributed to literature on the effect of student-developed videos.
Volume: 14
Issue: 3
Page: 2369-2380
Publish at: 2025-06-01

Navigating complexities in on-the-job training at vocational institutions: a systematic literature review

10.11591/ijere.v14i3.31977
Selvi Rajamanickam , Ridzwan Che Rus , Mohd Nazri Abdul Raji
This study aims to systematically review and analyze the integration of fourth industrial revolution (IR 4.0) technologies into technical and vocational education and training (TVET) through on-the-job training (OJT), focusing on key themes such as skills development in the digital age, workforce productivity, relevance of IR 4.0 technologies, and the role of OJT in TVET. Additionally, it seeks to identify the challenges and best practices associated with this integration, offering actionable insights for policymakers, educators, and industry stakeholders to enhance skills development and workforce adaptability in the context of the IR 4.0. A systematic literature review was conducted to understand the multifaceted challenges and opportunities surrounding OJT programs within TVET institutions. Given TVET’s vital role in equipping individuals with workforce-relevant skills, optimizing OJT programs is crucial for meeting modern industry demands. The PRISMA framework guided the review, using advanced search techniques on databases such as Scopus, ERIC, and IEEE, leading to the analysis of 35 primary sources. The review addressed areas including the adaptation of training to modern technologies, labor market outcomes, innovative practices for competency development, and ensuring equity and access in vocational training. It identified best practices, highlighted knowledge gaps, and provided recommendations to optimize OJT in TVET. Key findings emphasized aligning OJT with emerging technologies, enhancing employment outcomes, promoting innovative training methods, and ensuring inclusive and effective vocational training. The study concludes by offering recommendations to improve the quality and outcomes of OJT in TVET, ensuring alignment with evolving workforce and industry needs.
Volume: 14
Issue: 3
Page: 1856-1869
Publish at: 2025-06-01

A review of research on environmental awareness based on bibliometric analysis: initiation, progress and future

10.11591/ijere.v14i3.30037
Ömer Cem Karacaoğlu , Aysun Aynur Yılmaz , Abdulkadir Özkaya
“Environmental awareness” (EA) is an important factor in helping to eliminate environmental problems and mobilizing individuals and communities. This study addresses the vast literature on EA by presenting a bibliometric analysis of 564 EA studies in the social science citation index (SSCI) in the Web of Science (WoS) database. The study started with a query of EA-related publications in the WoS database and included an exploration stage covering topics such as distribution by years, most cited journals, publishing countries and active universities. The second stage involved the visualization of EA research on keywords through analysis and visual maps using Biblioshiny and VOSviewer software. The third stage involves naming the constructs and identifying their main characteristics. The analysis of keywords and the cluster names made in the light of these words provide a broad perspective of EA research. The final stage, validation, aims to determine the validity of the constructs based on the relationships between concepts. The overall results of the study show that EA research is influential across the world and is shaped around various themes. The identified themes guide future research and policymaking by emphasizing environmental education, sustainability, early childhood education, active learning, and interdisciplinary collaboration.
Volume: 14
Issue: 3
Page: 1770-1789
Publish at: 2025-06-01

Emotional empathy predicting subjective well-being: undergraduate and graduate comparison

10.11591/ijere.v14i3.32444
Samer Adnan Abdel Hadi , Mahmoud Fisal Alquraan
The current study aims to determine if emotional empathy predicts subjective well-being among undergraduate and graduate students. The current quantitative investigation is based on the survey research design. Participants were students from Al Ain University’s Abu Dhabi and Al Ain campuses (n=307). Data were gathered using the multidimensional emotional empathy scale (MDEES) and the subjective well-being scale (WeBs). The study found that increasing emotional empathy resulted in enhanced subjective well-being among undergraduate and graduate students. The findings also revealed that an increase in the emotional attention component of emotional empathy is associated with a decrease in subjective well-being. The suffering component of emotional empathy makes the greatest contribution to predicting subjective well-being among undergraduate and graduate students. The component of feeling for others ranks second in terms of capacity to predict subjective well-being among undergraduate students. Positive sharing is the second most effective predictor of subjective well-being among graduate students. We discovered that there is a need to increase college students’ subjective well-being, which has a major impact on their overall well-being.
Volume: 14
Issue: 3
Page: 1695-1705
Publish at: 2025-06-01

The influence of mobile communication technologies in long-term e-learning

10.11591/ijere.v14i3.29863
Elena Susimenko , Alena Gura , Alsu Rakhmanova , Olga Butylchenko
Communicative abilities constitute a crucial element of successful learning and interaction. The psychological impact of the prolonged lack of face-to-face contact with the peer audience and teachers typically remains an unresolved problem, despite the availability of appropriate online learning methodologies and technical tools. This study aims to ascertain a quantitative assessment of social maladjustment and a reduction in the level of communicative competence resulting from prolonged distance learning and the use of mobile devices in communication. The research employs a quantitative approach and is based on a survey of students who participated in eight socio-psychological training sessions (A-trainings). The training sessions are oriented towards refining the personal qualities of individuals and facilitating their adaptation to the fluctuating conditions of learning environments. The analysis of pre-and post-training results was compared with the results of the control group. The research findings indicate a positive impact of socio-psychological training on the enhancement of communicative skills and emotional well-being of students.
Volume: 14
Issue: 3
Page: 1904-1915
Publish at: 2025-06-01

Impact of natural-white and red-blue light-emitting diode lighting on hydroponic basil growth and energy efficiency

10.11591/ijaas.v14.i2.pp406-415
Chaiyant Boonmee , Warunee Srisongkram , Wipada Wongsuriya , Patcharanan Sritanauthaikorn , Paiboon Kiatsookkanatorn , Napat Watjanatepin
Advanced phosphor-converted white light-emitting diodes (pc-WLEDs) have been developed to mimic the natural sunlight spectrum, potentially enhancing plant growth compared to traditional red-blue (R-B) LEDs. This study aimed to compare the effects of natural-white pc-WLED (nsW-pcLED) and conventional R-B LED (R:B 3.24) on the growth, yield, and energy efficiency of hydroponically grown sweet basil. It was cultivated in a deep-water culture system under identical conditions with a photosynthetic photon flux density (PPFD) of 200±10 µmol·m⁻²·s⁻¹ and a 16/8 light/dark photoperiod over 28 days. Key growth parameters, including plant height, stem diameter, leaf number, and plant fresh weight (PFW), were measured, while energy consumption was recorded to assess efficiency. Results indicated that nsW-pcLED significantly enhanced growth, with plants achieving an average height of 44.30±1.51 cm, stem diameter of 6.68±0.21 mm, and a PFW of 34.20±6.12 g, compared to 35.88±4.05 cm, 4.66±0.88 mm, and 23.02±5.26 g under R-B LED (p <0.05), respectively. The nsW-pcLED treatment produced an average net growth of 1,221 g·m⁻² versus 536.43 g·m⁻² for R-B LED and delivered 33.05 g·m⁻²·kW·h⁻¹ compared to 11.17 g·m⁻²·kW·h⁻¹, while consuming 23% less energy. These findings highlight nsW-pcLED’s superior performance for indoor hydroponic cultivation. Future studies should explore its application in large-scale systems and across diverse crop species.
Volume: 14
Issue: 2
Page: 406-415
Publish at: 2025-06-01

Optimization of cashew apple extract as a tomato sauce substitute in chicken steak marinades

10.11591/ijaas.v14.i2.pp590-597
Siti Susanti , Fatma Puji Lestari , Agus Setiadi , Budi Hartoyo , Ahmad Ni'matullah Al-Baarri
This study aims to optimize the use of cashew apple extract (CAE) as a substitute for tomato sauce in marinades and evaluate its effects on the chemical and sensory qualities of chicken steak. Four different marinade formulations containing varying concentrations of CAE (0, 5, 10, and 15%) were applied to chicken steak samples. Chemical analyses measured protein, fat content, and polycyclic aromatic hydrocarbon (PAH) levels, while sensory evaluations were conducted to assess tenderness, juiciness, aroma, and overall preference using a semi-trained panel. The results demonstrated that increasing CAE concentrations significantly elevated protein content (p<0.05) and reduced fat levels. PAH levels were below detectable limits in all samples, suggesting the marinade’s potential in reducing PAH formation. Sensory analysis revealed that the 5% CAE marinade was the most preferred, particularly for tenderness and juiciness. These findings suggest that CAE is a viable alternative to tomato sauce in marinades, offering both nutritional benefits and consumer acceptability.
Volume: 14
Issue: 2
Page: 590-597
Publish at: 2025-06-01

Combining XGBoost and hybrid filtering algorithm in e-commerce recommendation system

10.11591/ijaas.v14.i2.pp618-626
Vincentius Loanka Sinaga , Antoni Wibowo
This study proposes a hybrid filtering algorithm (HFA) that combines extreme gradient boosting (XGBoost), content-based filtering (CBF), and collaborative filtering (CF) to improve recommendation accuracy in electronic commerce (e-commerce). XGBoost first leverages demographic data (e.g., age, gender, and location) to address cold start conditions, producing an initial product prediction; CBF refines this prediction by measuring product similarities through term frequency-inverse document frequency (TF-IDF) and cosine similarity, while CF (implemented via singular value decomposition) further incorporates user interaction patterns to enhance recommendations. Experimental results across multiple datasets demonstrate that HFA consistently outperforms standalone XGBoost in key metrics, including precision, F1-score, and hit ratio (HR). HFA’s precision often exceeds 90%, indicating fewer irrelevant recommendations. Although recall levels remain modest, HFA exhibits stronger adaptability under cold start scenarios due to its reliance on demographic features and user-item interactions. These findings highlight the efficacy of combining advanced machine learning with hybrid filtering techniques, offering a more robust and context-aware solution for e-commerce recommendation systems.
Volume: 14
Issue: 2
Page: 618-626
Publish at: 2025-06-01

Enhancing artificial neural network performance for energy efficiency in laboratories through principal component analysis

10.11591/ijaas.v14.i2.pp310-321
Desmira Desmira , Norazhar Abu Bakar , Mustofa Abi Hamid , Muhammad Hakiki , Affero Ismail , Radinal Fadli
This study investigates energy efficiency challenges during laboratory activities. Inefficient energy use in the practicum phase remains a critical issue, prompting the exploration of innovative forecasting models. This research employs artificial neural network (ANN) models integrated with principal component analysis (PCA) to predict energy consumption and optimize usage. The findings reveal that PCA components, including eigenvalues, eigenvectors, and matrix covariance values, significantly influence the ANN model's performance in forecasting energy production. The ANN training achieved a high correlation coefficient (R=1) with a mean squared error (MSE) of 0.045931 after 200,000 epochs, demonstrating the model's robustness. While testing results showed a moderate correlation (R=0.46169), the models demonstrated potential for refinement and scalability. This integration of ANN and PCA models provides a reliable framework for accurately forecasting energy usage, offering an effective strategy to enhance energy efficiency in laboratory settings. By optimizing energy consumption, this approach has the potential to reduce operational costs and environmental impact. The strong performance metrics highlight the practical utility of these models in educational contexts, contributing to sustainable energy management and better resource allocation. Furthermore, the reduction in energy-related environmental impacts underscores the broader applicability of these models for fostering sustainable development in similar contexts.
Volume: 14
Issue: 2
Page: 310-321
Publish at: 2025-06-01

Comparative study on fine-tuning deep learning models for fruit and vegetable classification

10.11591/ijaas.v14.i2.pp384-393
Abd Rasid Mamat , Mohamad Afendee Mohamed , Mohd Fadzil Abd Kadir , Norkhairani Abdul Rawi , Azim Zaliha Abd Aziz , Wan Suryani Wan Awang
Fruit and vegetable recognition and classification can be a challenging task due to their diverse nature and have become a focal point in the agricultural sector. In addition to that, the classification of fruits and vegetables increases the cost of labor and time. In recent years, deep learning applications have surged to the forefront, offering promising solutions. Particularly, the classification of fruits using image features has garnered significant attention from researchers, reflecting the growing importance of this area in the agricultural domain. In this work, the focus was on fine-tuning hyperparameters and the evaluation of a state-of-the-art deep convolutional neural network (CNN) for the classification of fruits and vegetables. Among the hyperparameters studied are the number of batch size, number of epochs, type of optimizer, rectified unit, and dropout. The dataset used is the fruit_vegetable dataset which consists of 36 classes and each class contains 1,000 images. The results show that the proposed model based on the batch size=64 and the number of epochs=25, produces the most optimal model with an accuracy value (training) of 99.02%, while the validation is 95.73% and the loss is 6.06% (minimum).
Volume: 14
Issue: 2
Page: 384-393
Publish at: 2025-06-01

Security analysis of Indonesia e-commerce platform against the risk of phishing attacks

10.11591/ijaas.v14.i2.pp533-541
Gede Arna Jude Saskara , Made Ody Gita Permana , I Made Gede Sunarya
This research analyses the security of e-commerce platforms in Indonesia against the risk of phishing attacks using the social-engineer toolkit (SET) application. Of the 31 platforms tested, it was found that 22 platforms have a low-security level because they can be easily replicated to carry out phishing attacks. In contrast, 9 platforms showed a high level of security, as they implemented the step-wise authentication and embedded login methods, which proved effective in protecting the platform from phishing attacks. The effectiveness rate of the SET application in conducting tests was recorded at 70.9%; the percentage is included in the high category. This research also identified that most low-security platforms still use the single-page login method or a special URL for login, making them very vulnerable to phishing attacks. The action research method was used as the research framework, involving five stages: diagnosis, planning, action, evaluation, and learning. The results of this study provide important guidance for platform owners to improve security mechanisms, how to build a login page to avoid the risk of misuse by cybercrime actors to conduct phishing attacks, and for users as a reference to choose a more secure e-commerce platform.
Volume: 14
Issue: 2
Page: 533-541
Publish at: 2025-06-01

Usability evaluation of ToAksara as Balinese script learning mobile application

10.11591/ijaas.v14.i2.pp490-501
Gede Indrawan , Sariyasa Sariyasa , Luh Joni Erawati Dewi , Made Santo Gitakarma , I Made Agus Oka Gunawan , Putu Ade Pranata
ToAksara application transliterates Latin text into Balinese script and has been used in high school teaching and learning activities in Buleleng Regency, Bali, Indonesia. This application was expected to provide comfort and satisfaction for students while learning the Balinese language and script. To measure the comfort and satisfaction level, a usability evaluation was carried out that focused on the application's end user. This research used a combination of concurrent think-aloud (CTA) and user experience questionnaire (UEQ) to evaluate ToAksara. In CTA, data collection involved nine respondents given a task scenario and expressing their problems or input. In UEQ, data collection involved 385 respondents who chose the value closest to their impression of 26 statements. Based on the analysis results, CTA produced several recommendations for improving the application regarding navigation, functionality, and errors. Based on the analysis, the user satisfaction results showed that all aspects were included in the excellent category. The aspects of attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty each produced a value of 2.144, 2.220, 2.385, 2.345, 2.139, and 2.101. The excellent category shows that ToAksara was included in the range of the top 10% of products compared to the UEQ benchmark.
Volume: 14
Issue: 2
Page: 490-501
Publish at: 2025-06-01

Energy efficient direct transesterification of Nannochloropsis sp. using hydrodynamic cavitation

10.11591/ijaas.v14.i2.pp394-405
Jiran Nirmalasari , Martomo Setyawan , Siti Jamilatun , Joko Pitoyo , Dhias Cahya Hakika
The increasingly limited supply of fossil fuels requires renewable fuel as an alternative source. Nannochloropsis sp. is a microalgae species containing a lipid content of between 12 and 53%, which can be converted to biofuel as an alternative source of fossil fuels through a transesterification process. Up to this date, the literature has reported no studies on biodiesel production from Nannochloropsis sp. via direct transesterification with catalyst using hydrodynamic cavitation. The direct transesterification process introduced 7.5 g of microalgae, 40 ml of methanol, 90 ml of hexane, and 0.0225 g of sodium hydroxide into the sample chamber. These mixtures were passed within the cavitation using a pressure driver and transformed into fatty acid methyl ester (FAME). The catalytic hydrodynamic cavitation method produces a higher extract yield than the stirring one. Regarding the FAME composition, the catalytic hydrodynamic cavitation method is dominated by saturated fatty acid (palmitic), while the stirring catalytic method is dominated by monounsaturated fatty acid (oleic). The hydrodynamic cavitation method provides a lower average degree of unsaturation and shorter chain length than the stirring catalytic method.
Volume: 14
Issue: 2
Page: 394-405
Publish at: 2025-06-01

A course review analysis using bidirectional long short-term memory model

10.11591/ijaas.v14.i2.pp580-589
Murthy Venkata Surya Narayana Tatavarthy , Naga Lakshmi Vadlamani
In recent years, sentiment analysis and online review analysis have gained popularity as critical components in the growth and development of educational courses. An innovative method has been created to increase the quality of learning experiences by rapidly collecting relevant data from course comments. This technique leverages bidirectional encoder representation from transformers (BERT) for word vector training. When combined with a learning mechanism, the recommended BERT accurately predicts the sentiment of online course reviews. Additionally, a dual-channel model based on Bi-directional long short-term memory (Bi-LSTM) is employed to improve sentiment data and semantics. Following data collection from the Coursera dataset, preprocessing approaches such as tokenization, stop words removal and sentence metric creation are applied to convert input data into word vectors and identify fundamental text units using text segmentation. The results demonstrate the proposed approach’s superiority over existing methods, offering an accuracy of 81.45%, recall of 94.9%, precision of 93.7%, and F-score of 93.7%.
Volume: 14
Issue: 2
Page: 580-589
Publish at: 2025-06-01
1 2 3
...
1826 1827
Show 19 of 1827

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration