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31,456 Article Results

Mobile application for distributing information to students at the Sciences and Humanities University

10.11591/ijeecs.v37.i2.pp1085-1092
Patricia Condori-Obregon , Carlos Huallpa-Juarez , Carlos Palomino-Vidal
Currently, educational institutions around the world have implemented many standards and rules to ensure teaching quality. Many of these standards and rules are related to the use of technologies that provide students with services and facilities to learn. However, in Peru, a Latin American country, these standards and rules have been recently implemented, and as a result, information systems are required to guarantee teaching quality. This research exposes the implementation of a mobile application for distributing and managing information for students and teachers who require data about courses, grades, absences, and receive news about important university announcements. This work applied both research methods and Scrum methodologies together to demonstrate how the education process benefits from the use of technologies. As a result of these implementations, processes like finding academic information improved by an average of 50%. These results support that the implementation of mobile application technologies in educational environments is beneficial for guaranteeing process improvement and teaching quality.
Volume: 37
Issue: 2
Page: 1085-1092
Publish at: 2025-02-01

Optimization machine learning models for selecting transmit antennas in 5G/6G systems

10.11591/ijeecs.v37.i2.pp819-828
Abdellah Ouldammar , Abdelmounaim Moulay Lakhdar , Ahmed Bouida , Khaled Merit
Transmit antenna selection (TAS) plays a crucial role in improving the performance and spectral efficiency of 5G/6G systems. This study proposes to use the GridSearchCV method for hyperparameter optimization in two supervised learning models, support vector machine (SVM) and K-nearest neighbors (KNN), to optimally select antenna peers based on channel gain. These models were applied to Alamouti’s space-time block coding to improve performance, resulting in increased signal-to-noise ratio (SNR) and reduced bit error rate (BER). The results show that optimizing the hyperparameters led to a significant improvement in the performance of the SVM and KNN models. The SVM and KNN models were evaluated using a variety of metrics, with the SVM demonstrating superior predictive performance in terms of accuracy, average macro recall, average macro precision, average macro F1 score, and cross-validation score. Even before optimization, the SVM outperforms the KNN in terms of performance metrics. After optimization, this gap widens further, demonstrating the robustness of SVM for classification tasks. Although KNN is faster to train.
Volume: 37
Issue: 2
Page: 819-828
Publish at: 2025-02-01

Modeling of Glugur Substation grounding systems using MATLAB graphical user interface

10.11591/ijece.v15i1.pp15-23
Indra Roza , Yoga Tri Nugraha , Rizkha Rida , Muhammad Irwanto , Mohd. Azlishah Othman
The grounding system in substations generally uses electrode rods, because electrodes can affect the effectiveness of fault current conduction, so the equipment will be safer. Considering the importance of the grounding system, the installed grounding system must be considered and maintained properly. One of them is the grounding found in Glugur. The main objective of this research is to comprehensively evaluate the substation grounding system by modeling the grounding system at the Glugur Substation using MATLAB graphical user interface (GUI). The grounding resistance follows a grid system with an area of 20×15=300 m² with specific resistance being clay using 100 rod electrodes. From the results of ground resistance simulation modeling using MATLAB GUI, it can be concluded as follows: for a certain resistance value, the number of electrodes for 100 Ωm is 3.55 Ω, for ground resistance with a constant depth and a varying number of 100 electrodes, it is 3.45 Ω, and for. The grounding resistance with a constant and varying number of 1,000 rods is obtained at 2.65 Ω. From these results, the modeling carried out is in accordance with the standards of electricity regulations in Indonesia.
Volume: 15
Issue: 1
Page: 15-23
Publish at: 2025-02-01

Efficient deep learning approach for enhancing plant leaf disease classification

10.11591/ijeecs.v37.i2.pp1112-1120
Meroua Belmir , Wafa Difallah , Abdelkader Ghazli
The widespread occurrence of plant diseases is a major factor in the reduction of agricultural output, affecting both crop quality and quantity. These diseases typically begin on the leaves, influenced by alterations in plant structure and growing techniques, and can eventually spread over the entire plant. This results in a notable decrease in crop variety and yield. Successfully managing these diseases depends on accurately classifying and detecting leaf infections early, which is essential for controlling their spread and ensuring healthy plant growth. To address these challenges, this paper introduces an efficient approach for detecting plant leaf diseases. A concatenation of pre-trained convolutional neural networks (CNN) for enhanced plant leaf disease using transfer learning technique is implemented, with a specific focus on accurate early detection, utilizing the comprehensive new plant diseases dataset. The combined residual network-50 (ResNet-50) with densely connected convolutional network-121 (DenseNet-121) architecture aims to provide an efficient and reliable solution to these critical agricultural concerns. Various evaluation metrics were utilized to evaluate the robustness of the proposed hybrid model. The proposed ResNet-50 with the DenseNet-121 hybrid model achieved a rate of accuracy of 99.66%.
Volume: 37
Issue: 2
Page: 1112-1120
Publish at: 2025-02-01

Performance analysis of a neuromodel for breast histopathology decision support system

10.11591/ijai.v14.i1.pp102-108
Adedayo Olukayode Ojo , Adetoro Mayowa Sola , Florence Omolara Ojo , Moses Onibonoje Oluwafemi
Breast cancer detection and diagnosis are crucial in reducing mortality rates among women globally. This research article explores an artificial intelligence technique for early breast cancer detection, aiding doctors in making informed decisions for improved patient management. The study employs histopathological analysis of breast tissue microscopically to detect abnormalities, with the aim of categorizing normal tissue, benign lesions, in situ carcinoma, and invasive carcinoma. The proposed technique utilizes an artificial neural network trained using the resilient backpropagation algorithm (RP_ANN). The study further compares the observed performance with those of three other algorithms, including gradient descent algorithm (GDA_ANN), Levenberg-Marquardt algorithm (LM_ANN), and layer sensitivity-based (LSB_ANN) algorithm based on various evaluation metrics. RP_ANN and LSB_ANN demonstrated superior performance, with high validation and training variance accounted for (VAF) and low root mean squared error (RMSE). The results underscore the potential of deep learning-based algorithms for improving breast cancer detection, promising better patient outcomes and enhanced diagnostic accuracy.
Volume: 14
Issue: 1
Page: 102-108
Publish at: 2025-02-01

SRCNN-based image transmission for autonomous vehicles in limited network areas

10.11591/ijeecs.v37.i2.pp903-912
Anindya Afina Carmelya , Arief Suryadi Satyawan , Galura Muhammad Suranegara , Mokhamamad Mirza Etnisa Haqiqi , Helfy Susilawati , Nizar Alam Hamdani , Puput Dani Prasetyo Adi
High-quality images are crucial for navigation, obstacle detection, and environmental understanding, but transmitting high-resolution images over constrained networks presents significant challenges. This study introduces an image transmission system using super-resolution convolutional neural networks (SRCNN) to enhance image quality without increasing bandwidth requirements by transmitting low-resolution images and upscaling them with SRCNN. The first phase of the research involved data collection, in which information was acquired directly from an appropriate locus to produce training, validation, and testing datasets. The second, three SRCNN models (915, 935, and 955) were trained using such a training dataset. The last was an evaluation, in which model 915 showed quick learning and stable performance with initial high loss, while model 935 had rapid convergence but potential overfitting. Model 955 achieved high initial performance. Three SRCNN model configurations were tailored to the specific needs of autonomous electric vehicles operating in limited areas, such as the locus. Input image resolution ranged from 128×128 pixels to 256×256 pixels, while output resolution varied from 256×256 pixels to 512×512 pixels. These resolutions can be acceptable for efficient image transmission over IEEE 802.11ac, but on the long range (LoRa) network, it still produces some delay.
Volume: 37
Issue: 2
Page: 903-912
Publish at: 2025-02-01

Recognizing geographical locations using a GAN-based text-to-image approach

10.11591/ijeecs.v37.i2.pp1168-1182
Dina M. Ibrahim , Amal A. Al-Shargabi
Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location services. To produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image-text matching loss needed to train the AttnGAN’s generator is computed using our multimodal similarity model. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset comprises photographs, we’ve added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that AttnGAN’s proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective.
Volume: 37
Issue: 2
Page: 1168-1182
Publish at: 2025-02-01

Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification

10.11591/ijai.v14.i1.pp62-72
Muhlasah Novitasari Mara , Sidiq Syamsul Hidayat , Farika Tono Putri , Dwi Rahmawati , Sri Wahyuni , Muhamad Cahyo Ardi Prabowo , Noer Ni'mat Syamsu Kabir , Ragil Tri Indra
This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Volume: 14
Issue: 1
Page: 62-72
Publish at: 2025-02-01

Literacy extension programs of higher education institution in the Philippines: insights from stakeholders

10.11591/ijere.v14i1.29736
Matronillo Del Mundo Martin , Lhea dela Cruz Ildefonso
This descriptive research investigates the impact of extension programs from a college of teacher education on public elementary school teachers. Three programs, literacy (n=75), Catch Me and Teach Me strategy literacy program (n=64), and campus journalism (n=48), were evaluated. Participants reported profound positive impact from these programs, considering them effective or very effective. The literacy program significantly improved reading and writing skills, fostered a love for learning, and benefitted the community. However, challenges like outdated materials and overemphasis on rote memorization need addressing. The Catch Me and Teach Me strategy literacy program excelled in practical teaching strategies and hands-on learning but required additional time and support, emphasizing the need for a more personalized approach. The campus journalism program was lauded for its informativeness but faced venue, food quality, time management issues, and a desire for more diverse topics. These findings stress the importance of customized professional development, long-term impact assessment, cultural sensitivity, and community involvement. Effective, engaging training methods, feedback, and efficient resource allocation are vital for program improvement. The research aims to enhance extension services’ quality for the community's benefit, providing valuable guidance to improve these programs and cater to participants' diverse needs.
Volume: 14
Issue: 1
Page: 544-556
Publish at: 2025-02-01

Mapping research on peace education: the bibliometric analysis for research agenda in the future

10.11591/ijere.v14i1.29097
Wahyu Nanda Eka Saputra , Prima Suci Rohmadheny , Nur Hidayah , Trikinasih Handayani , Agus Supriyanto , Agungbudiprabowo Agungbudiprabowo
This study aims to analyze the trend of scientific publications with the theme of peace education. This study uses bibliometric analysis to describe trends in peace education research and reveal its bibliometric profile. The data was taken from the Scopus database covering 1961 to 2023 with the keywords “peace education” and “violence.” The results of the analysis show that there is a positive trend toward an increase in publications with the theme of peace education. The most prominent country that contributes to peace educationthemed publications is the United States. The University of Toronto and the University of Kwazulu-Natal are the most famous universities that publish research results on peace education. The Journal of Peace Education is the favorite journal for publication on the theme of peace education. Vaughn Mitchell John and Johan Galtung are prominent names who have influenced publications on peace education. Potential themes regarding peace education are discussed in this paper. This research contributes to analyzing structure, trends, collaboration opportunities, and research roadmaps as a basis for future research.
Volume: 14
Issue: 1
Page: 61-73
Publish at: 2025-02-01

Graduate students’ work status, perception, and online satisfaction predicting in-person instruction intention

10.11591/ijere.v14i1.30896
Ayesha Calmerin Penuela , Ryan Michael Flores Oducado
Along with easing COVID-19 restrictions, schools are reopening and starting face-to-face classes. Intending in-person instruction after a relatively long period after off-campus and online learning requires exploration. This study determined the factors that predict students’ intention to attend in-person instruction among graduate students. The responses of 178 college of education graduate students were gathered electronically using valid and reliable survey instruments. Findings showed that graduate students are moderately to highly satisfied with online learning but also highly intend to attend in-person instruction. The majority of graduate students think that it is unsafe and possible to spread COVID-19, and felt worried to less worried when school reopens. Multiple regression revealed that work status, perceived safety and worry about school reopening, and online learning satisfaction predicted intention to attend in-person instruction. School administrators should ensure the safety operation of schools should students return to campuses, especially when there are threats to their health and security.
Volume: 14
Issue: 1
Page: 222-228
Publish at: 2025-02-01

State-of-the-art in teachers’ online pedagogical competencies in higher education from 2011 to 2022

10.11591/ijere.v14i1.28717
Thi-Thanh-Hai Pham , Hung-Hiep Pham , Dinh-Hai Luong
University teachers play an important role in teaching and learning activities in the online environment. However, the emerging remote education due to the COVID-19 pandemic has revealed the shortcomings of online teaching competencies of teachers. The bibliometric study investigated the knowledge base of online teacher pedagogical competencies in higher education by analyzing 131 Web of Science-indexed documents from 2011 to 2022. The findings revealed the sudden decline of the number announced in the two years 2021 and 2022. Spain, Russia, the United States, and Canada are emerging countries in the network of international collaboration. The research community is based on small research groups and has emerged in recent years. Sources focus on subjects such as education, e-learning, computer science, developmental and educational psychology, among which education sciences is the most prominent one in the last years. With nine identified themes, these of interest are online teacher roles, remote emergency teaching, online professional development, and online teaching in COVID-19. Emerging keywords highlighted potential topics in the foreseeable future such as competency frameworks, global education, and teaching professionalism.
Volume: 14
Issue: 1
Page: 659-670
Publish at: 2025-02-01

Development and validation of doctoral student social support perception scale

10.11591/ijere.v14i1.30920
Xiaohan Yang , Kee Jiar Yeo , Shih-Hui Lee , Boon Yew Wong , Lina Handayani
The perception of social support is crucial for doctoral students' academic careers, yet there is a notable absence of scales specifically designed to measure the social support that doctoral students receive. Consequently, there is a clear need for an effective tool to assess the level and nature of support perceived by these students. The Doctoral Students Social Support Perception Scale (DSSPS) is a multidimensional instrument developed to evaluate social support received by doctoral students from supervisors, family, and peers/friends. This scale operates in two phases: the first phase uses exploratory factor analysis to identify three potential dimensions of perceived social support: resource provision, emotional inspiration, and appropriate attention. The second phase employs confirmatory factor analysis to demonstrate the scale's robust overall fit. The results also indicate high internal consistency as well as convergent and discriminant validity. These findings suggest that the DSSPS is both an effective and reliable measure to assess the extent and nature of social support perceived by doctoral students.
Volume: 14
Issue: 1
Page: 1-9
Publish at: 2025-02-01

Real-time paddy grain drying and monitoring system using long range-internet of things

10.11591/ijece.v15i1.pp448-454
Ayong Hiendro , Syaifurrahman Syaifurrahman , F. Trias Pontia Wigyarianto , Fitriah Husin
Grain drying environmental parameters are an important issue throughout the paddy grain production process. A real-time monitoring system requires rapid, online, and accurate measurement results. In the paddy grain drying process, the heated air velocity, temperature, relative humidity, and moisture content have to be carefully monitored and maintained to ensure product quality and safety. This study aimed to propose a real-time paddy grain drying and monitoring system using a long-range internet of things (LoRa-IoT). The real-time monitoring system consisted of sensors, LoRa, and IoT platforms. The LoRa end node and gateway were utilized as a wireless radio communication platform of IoT for long-distance signal transmission. From the experiment, the gateway received data from the end node at a distance of 2 km with a time on air (ToA) of 981 ms. As a result, the proposed monitoring system succeeded in measuring and recording the heated air velocity, temperature, and relative humidity data during the paddy grain drying process from 25% moisture content down to 14%. Regarding moisture content, the accuracy of real-time monitoring information was confirmed with a direct measurement method, resulting in a root mean square error (RMSE) of 6.17%.
Volume: 15
Issue: 1
Page: 448-454
Publish at: 2025-02-01

Educational management in a digital age: boss phubbing and teacher motivation

10.11591/ijere.v14i1.29046
Mukadder Erdem , Esra Kaya
The study aims to assess the level of phubbing behavior an antisocial common mobile phone using behavior of school principals and how it affects the motivation of the teachers working at Anatolian High Schools and Vocational High Schools. This relational screening modelled study was conducted in these schools in the 2nd term in 2022 to 2023 school year. For data collection purposes, Boss Phubbing Scale and Multidimensional Work Motivation Scale were used and 268 answers were collected from the whole population. The data was analyzed through descriptive statistics, exploratory factor analysis (EFA), Cronbach’s alpha reliability analysis, one-way analysis of variance (ANOVA), unpaired t test, Pearson correlation analysis and regression analysis. The data derived from these analyses indicates that while school principals phub teachers at a low level and their boss phubbing scores differentiate in accordance with the work experience of teachers; teachers’ motivation is high and there is a significant and positive relationship between boss phubbing of school principals and two factors of Multidimensional Work Motivation Scale, which were extrinsic motivation and amotivation. It may be suggested that school principals should alter their boss phubbing behavior or modify it to make the best of it.
Volume: 14
Issue: 1
Page: 50-60
Publish at: 2025-02-01
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