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School resilience through quality management in early childhood education: a case study from Indonesia

10.11591/ijere.v14i1.30384
Nurul Arifiyanti , Siti Irene Astuti Dwiningrum , Amir Syamsudin , Harun harun
Poor management of the education system can cause a decrease in school quality. Moreover, the increasing of similar institutions in early childhood education (ECE) in Indonesia gives a school must have good resilience. These institutions must ensure that the educational programs they provide to children are of high quality, thus earning the trust of preschool parents. This article aimed to highlight the importance of paying attention to school management. Therefore, schools that already have good resilience need to be studied to have an impact on schools that are in the stagnant category. The method used was a case study with in-depth interviews. The results indicate that the resilience schools use four strategies to remain resilient. These strategies include having quality human resources, unique school programs, and school promotion. The research results highlight the importance of quality human resource management, superior programs such as foreign language learning and international curricula, and effective promotion in maintaining the resilience of educational institutions amidst competition.
Volume: 14
Issue: 1
Page: 269-278
Publish at: 2025-02-01

Levenberg-Marquardt-optimized neural network for rainfall forecasting

10.11591/ijai.v14.i1.pp182-192
Gujanatti Rudrappa , Nataraj Vijapur , Sateesh Hosamane
Rainfall is a crucial meteorological indicator with applications in agriculture, aviation, and military. Forecasting is essential due to unpredictable environmental changes. Current methods use complex statistical models, which are timeconsuming. The present study is targeted for forecasting rainfall with the help of meteorological parameters, viz., temperature, humidity, wind speed, wind direction, and rain, using a specialized artificial intelligence (AI) method and real-time data captured over the study area. The weather station installed at KLE Dr. M. S. Sheshgiri College of Engineering and Technology in Karnataka, India, collects meteorological data. The models used were principal component regression (PCR) and Levenberg-Marquardt -optimized neural network (LMAONN). The Levenberg-Marquardt (LMA) backpropagation (BP) algorithm performed better than other BP algorithms. The coefficient of determination (R2) observed for the PCR and LMAONN models were 0.57 and 0.87, respectively. The LMAONN model provided a better fit for rainfall forecasting than the PCR model, with an index of agreement (IoA) of 0.96, indicating good forecasting.
Volume: 14
Issue: 1
Page: 182-192
Publish at: 2025-02-01

Exploring university students’ perceptions and engagement in game-based learning

10.11591/ijere.v14i1.30494
Burhan Hamadneh , Amr Fikry , Mohammed Talaat , Ahmed Abbas , Reham Ghanem , Samah Ramzy , Mohamed Aly , Ahmed Elkilani , Sherin Mabrouk , Reema Aloqlah
Students in academia are facing serious challenges such as engagement, which causes academicians to question the quality and effectiveness of the teaching pedagogy. As such, new adapted innovative technological tools such as gamifications have been introduced to learning activities experiences and skills acquisition. However, little is known about the implementation of such technologies, which have been relatively underexplored in education literature. The primary objective of this study is to identify the perceptions of students and evaluate their engagement considering the use of gamification in the learning process. There were 210 respondents enrolled in the private university in Jordan were surveyed using a quantitative questionnaire. The study used descriptive statistics and independent sample t-test analyses on the data. The results indicated that the students have positive perceptions of game-based technology in both learning and classroom activity engagement. In addition, student perceptions and engagement did not differ significantly based on age or gender, but significant differences based on user experience were identified. These findings provide insight into the dynamic interaction between students and gamified technology to enhance student perceptions and promote meaningful learning. This research also provides implications on gamification literacy education and the pedagogically rich design of educational games to enhance student engagement. The recommendations are enumerated based on the obtained findings.
Volume: 14
Issue: 1
Page: 525-534
Publish at: 2025-02-01

EDK-LEACH: improving LEACH protocol-based machine learning in wireless sensor networks

10.11591/ijeecs.v37.i2.pp1251-1261
Taous Lechani , Samia Ourari , Fayçal Rahmoune , Said Amari , Hayet Termeche
In wireless sensor networks (WSNs), many sensor devices are spread throughout the environment with the goal of collecting data and sending them to a base station (BS) for further studies. The issue of their limited battery power has aroused the interest of researchers, and several protocols were developed to optimize energy use and thus increase the network’s lifetime. The present research enhances the well-known low-energy adaptive clustering hierarchy (LEACH) protocol with a new artificial intelligence (AI) protocol named energy distance K-means LEACH (EDK-LEACH). For this purpose, an innovative clustering strategy built on the machine learning K-means algorithm is used in WSNs to improve the cluster formation process and maximise network stability. By implementing an objective function that considers each node’s residual energy and distance from the cluster centre when selecting the cluster head (CH) of each cluster, EDK-LEACH also eliminates the inherent randomness in LEACH during the CH election process. The proposed protocol has the advantage of ensuring better CH distribution throughout the network surface with a balanced load across all network nodes. In comparison with the known LEACH, the simulation results demonstrate the efficiency of our approach: the lifetime of the network is extended and the energy consumption is reduced.
Volume: 37
Issue: 2
Page: 1251-1261
Publish at: 2025-02-01

Comparative study of pothole detection using deep learning on smartphone

10.11591/ijeecs.v37.i2.pp995-1004
Achyar Ulul Amri , Gede Putra Kusuma
Potholes present a significant problem in many countries, leading to vehicle damage and traffic accidents. These road imperfections pose safety risks and impose economic burdens. Despite existing detection methods using sensors and computer vision deep learning processed on PCs, a gap remains in deploying cost-effective, widely accessible solutions. This study aims to bridge this gap by developing deep learning models optimized for smartphones, reducing costs and enhancing deployment feasibility. We developed multiple models for pothole detection, utilizing transfer learning and Bayesian hyperparameter tuning to optimize detection accuracy and resource efficiency. Our evaluations focused on computationally light models such as YOLOv8 small, YOLOv8-nano, YOLOv7 tiny, and faster R-CNN MobileNetV3. In terms of detection accuracy, YOLOv8 small and YOLOv8 nano stood out, achieving average precisions (AP) of 83.5% and 82.5%, respectively. YOLOv8 nano proved the most efficient, offering high detection accuracy, a file size three times smaller than YOLOv8 small in TFLite format, and the fastest inference time of 0.72 seconds per image. This study highlights the potential of smartphones in urban pothole detection, contributing to improved road maintenance and urban policy.
Volume: 37
Issue: 2
Page: 995-1004
Publish at: 2025-02-01

Convolutional neural network modelling for autistic individualized education chatbot

10.11591/ijai.v14.i1.pp109-118
Raseeda Hamzah , Nursuriati Jamil , Nor Diana Ahmad , Syed Mohd Zahid Syed Zainal Ariffin
The traditional education system for autistic kids needs integration with computer technology that embraces artificial intelligence to help school instructors and management. An application that enables the teacher to retrieve information from a trusted source is essential since the information is only sometimes available on time. Thus, developing a chatbot application that utilizes natural language processing can enhance the management of autistic schools and will help individualized education for autistic students. This research uses a deep learning model that utilizes a convolutional neural network to develop a chatbot as a teaching assist tool for teachers. The results show that the chatbot has achieved ˜0.03% loss when trained with different epoch numbers. In terms of usability, the chatbot achieves mean system usability scores of 80.48 ± 13.03. This may open opportunities for more effective individualized education for students with special needs and increase the potential to improve inclusive education for disabled students. It is useful to include future actions that enable the simplification of the use of this chatbot tool in a wide range of contexts. To close the education gap for children with disabilities, chatbots could help people with communication disabilities and could also significantly enhance the rate of communication.
Volume: 14
Issue: 1
Page: 109-118
Publish at: 2025-02-01

Generic green skills: maturity level of vocational education teachers and students in Indonesia

10.11591/ijere.v14i1.29191
Farid Mutohhari , Putu Sudira , Pardjono Pardjono , Suyitno Suyitno , Warju Warju , Fajar Danur Isnantyo , Nuur Wachid Abdul Majid
Vocational education (VE) is one of the institutions that must answer environmental problems by providing green job skills to its students. However, VE in Indonesia still experience various obstacles in providing this provision, so the aim of this research aims to analyze the extent of the level of green skills (GS) in teachers and students in this country, which includes the dimensions of cognitive, interpersonal and intrapersonal competence as an illustration for developing a priority scale for improvement. In addition, examining the differences and correlations between dimensions and the contribution of dimensions to GS as a whole is an additional goal. The survey method was carried out using a generic GS questionnaire instrument in VE that have Adiwiyata status. Data were analyzed using three stages: descriptive analysis, ANOVA-post hoc Tukey test, and path analysis. As a result, students’ GS still show a low category, while teachers get a high category. Between dimensions show no significant differences. Finally, all competencies have a significant relationship and can construct overall GS. These results indicate that there is still a need to strengthen teacher competencies in GS-based learning management and strengthen collaboration with all levels of society.
Volume: 14
Issue: 1
Page: 179-187
Publish at: 2025-02-01

A novel model to detect and categorize objects from images by using a hybrid machine learning model

10.11591/ijai.v14.i1.pp667-679
Nilambar Sethi , Vetukuri Venkata Siva Rama Raju , Venkata Srinivas Lokavarapu , Ravi Babu Devareddi , Shiva Shankar Reddy , Silpa Nrusimhadri
As humans, we can easily recognize and distinguish different features of objects in images due to our brain’s ability to unconsciously learn from a set of images. The objectives of this effort are to develop a model that is capable of identifying and categorizing objects that are present within images. We imported the dataset from Keras and loaded it using data loaders to achieve this. We then utilized various deep learning algorithms, such as visual geometry group (VGG)-16 and a simple net-random forest hybrid model, to classify the objects. After classification, the accuracy obtained by VGG16 and the hybrid model was 84.7% and 89.6%, respectively. Therefore, the proposed model successfully detects objects in images using a simple net as a feature extractor and a random forest for object classification, achieving better accuracy than VGG16.
Volume: 14
Issue: 1
Page: 667-679
Publish at: 2025-02-01

The role of authoritative parenting and self-regulation in controlling adolescent aggressiveness

10.11591/ijere.v14i1.26650
Muhamad Hasan Abdillah , Zurqoni Zurqoni , Wildan Saugi , Ibnu Sutoko
Seeing the many cases of adolescent violence that occurred in Yogyakarta, Indonesia, this study was interested in examining the suitability of authoritative parenting and self-regulation abilities to the level of adolescent aggressiveness. The subjects of this study were 154 class XII students of State Senior High School 1 Sleman. The sample selection was obtained using a stratified random sampling technique. The aim is to obtain student representation from each class. Researchers use the scale as the main tool in obtaining research data on aggressiveness, authoritative parenting and self-regulation. The data was then analyzed using multiple linear regression with the SPSS V26 program. The results of the analysis showed that, simultaneously, authoritative parenting and self-regulation were very significant in suppressing the emergence of aggressiveness in adolescents (F=51.76 and Sig.=.000). Then, both authoritative parenting (Beta=-.37 with Sig.=.000) and self-regulation (Beta=-.35 with Sig.=.000) both were able to have a significant effect on adolescent aggressiveness partially. The conclusion is that adolescents need authoritative parenting at this phase; this is necessary to avoid the emergence or development of aggressiveness in adolescents. Besides that, adolescents must also be equipped with self-regulation skills because most activities are carried out without parental supervision.
Volume: 14
Issue: 1
Page: 452-462
Publish at: 2025-02-01

iFoodAR: augmented reality for high school food design technology

10.11591/ijere.v14i1.29702
Nur Ain Safura Azizoon , Wan Nurlisa Wan Ahmad , Qistina Ahmad Fizal , Tang Jing Rui , Mohd Yusof Kamaruzaman
Technology is advancing with the times. Augmented reality (AR) received great attention in education because it focuses on technology that connects the real and virtual worlds in real-time. The blending of AR technology and educational content aims to generate innovative thinking that can improve the effectiveness of teaching and learning for students in real-life scenarios. This study aimed to develop an AR module and application (apps.) for high school students to learn about food technology. Five phases of the analysis, design, development, implementation, and evaluation (ADDIE) model were used to develop food technology applications in the classroom based on AR technology. An interview was conducted to obtain the usability of the AR module from the selected expert. The results showed that iFoodAR fulfilled the requirement in line with the standard curriculum and assessment document (DSKP) of the Malaysian Ministry of Education and gained positive feedback on using AR in the classroom from the expert teachers. It is concluded that iFoodAR apps have the potential to cater the diversity learning styles, leading to a shift in teaching methods incorporating more interactive and visually stimulating learning experiences.
Volume: 14
Issue: 1
Page: 406-414
Publish at: 2025-02-01

Multi-class chronic lung disease classification based on guided backpropagation convolutional neural network using chest X-ray images

10.11591/ijeecs.v37.i2.pp1328-1338
Rakesh Selva Raj , Pavan Kumar Madalu Palakshamurthy , Bidarakere Eswarappa Rangaswamy
Clinical diagnosis is crucial as chronic lung disease is a leading cause of mortality worldwide. Chest X-ray imaging is essential for the early and accurate diagnosis of lung diseases. However, due to the complexity of pathological abnormalities and detailed annotation, the computer-aided diagnosis of lung diseases is challenging. To overcome this challenge, this research proposes the guided backpropagation convolutional neural network (GBPCNN) for the classification of chronic lung disease into 14 classes, by adjusting the network’s weights in CNN layers. The GBP technique enhances result accuracy by pinpointing the regions in an input image. Initially, the chest X-ray radiography (CXR) dataset is collected for estimating the effectiveness of the classifier. After collecting the dataset, the pre-processing is performed by utilizing image denoising using gaussian filter and normalization techniques. Then, the pre-processed data is fed to the feature extraction process, and it is done by using EfficientNetB2. Finally, extracted features are provided to the classification process to categorize chronic lung disease into 14 classes. The experimental results show that the proposed GBPCNN method attains better results and it achieves the accuracy of 97.94% as compared to the existing approaches like MobileLungNetV2 and CX-Ultranet. These results highlight the potential of our approach for clinical applications.
Volume: 37
Issue: 2
Page: 1328-1338
Publish at: 2025-02-01

Evaluating low-cost internet of things and artificial intelligence in agriculture

10.11591/ijeecs.v37.i2.pp968-975
Kamal Elhattab , Said Elatar
This article investigates the transformative impact of low-cost internet of things (IoT) solutions on the agricultural sector, with a particular emphasis on integrating artificial intelligence (AI) and machine learning (ML) technologies. The study aims to illustrate how affordable IoT technologies, when combined with advanced AI and ML capabilities, can serve as a significant asset for small and medium-sized farms. It addresses the economic and technical barriers these farms face in adopting such technologies, including high initial costs and the complexity of implementation. By conducting a comprehensive evaluation of existing IoT hardware and software, the research identifies and highlights innovative, cost-effective solutions that have the potential to drive significant advancements in agricultural practices. The findings underscore how these integrated technologies can enhance operational efficiency, increase productivity, and support sustainable agricultural development. Additionally, the paper explores the potential challenges and limitations of adopting these technologies, offering insights into how they can be mitigated. Overall, the study demonstrates that the convergence of low-cost IoT with AI and ML presents a valuable opportunity for modernizing agriculture and improving farm management.
Volume: 37
Issue: 2
Page: 968-975
Publish at: 2025-02-01

A deep learning model with an inductive transfer learning for forgery image detection

10.11591/ijeecs.v37.i2.pp801-810
Prabhu Bevinamarad , Prakash H. Unki , Venkatesh Bhandage
ue to the availability of affordable electronic devices and several advanced online and offline multimedia content editing applications, the frequency of image manipulation has increased. In addition, the manipulated images are presented as evidence in courtrooms, circulated on social media and uploaded upon authentication to deceive the situation. This study implements a deep learning (DL) framework with inductive transfer learning (ITL) by using a pre-trained network to benefit from the discovered feature maps rather than starting from scratch and fine-tuning the process to check and classify whether the suspected image is authenticated or forged effectively. To experiment with the proposed model, we used both Columbian uncompressed image splicing detection (CUISD) and the CoMoFoD dataset for training and testing. We measured the model’s performance by changing hyperparameters and confirmed the better selection of values for the hyperparameter to yield compromised results. As per the evaluation results, our model showed improved results by classifying new instances of images with an average precision of 89.00%, recall of 86.43%, F1-score of 87.32, and accuracy of 87.72% and consistently performed better compared to other methods currently in use.
Volume: 37
Issue: 2
Page: 801-810
Publish at: 2025-02-01

Modified-LSTM and feed forward neural network enabled resource allocation for 6G wireless networks

10.11591/ijeecs.v37.i2.pp811-818
Pradnya Kamble , Alam N. Shaikh
The 6G wireless networks utilize terahertz (THz) frequency and intended to tremendously dynamic and diverse applications with deep learning enabled network, harvested significant attention and able to solve complex problems. Efficient resource allocation is a key requirement of next generation wireless networks. This research focuses on the resource allocation optimization challenge which includes storage, computing power, bandwidth and memory in the milieu of 6G wireless networks with device-to-device (D2D) communication enabled. The proposed model uses modified long short-term memory (mLSTM) and feed forward neural network to allocate resources to various tasks as per requirement such as information access, audio/video streaming, information access and productivity activity applications. The proposed work focuses on network parameters like channel noise, signal to noise ratio (SNR), distance from base station and includes D2D communication decisions to improve network performance. This research gives a novelty learning based solution for resource allocation for 6G wireless networks which contributes to the enhancement of next generation wireless communication networks. The lowest computing power utilized is 1%, Bandwidth utilized is 3% of total bandwidth and 2% storage.
Volume: 37
Issue: 2
Page: 811-818
Publish at: 2025-02-01

Improved Bi-GRU for parkinson’s disease severity analysis

10.11591/ijeecs.v37.i2.pp1140-1149
Malathi Arunachalam , Ramalakshmi Ramar , Vaibhav Gandhi , Bhuvanesh Ananthan
Parkinson’s disease (PD) is a common neuro-degenerative issue, evaluated via the continuous deterioration of motor functions over time. This condition leads to a gradual decline in movement capabilities. For diagnosing clinical set of PDs, medical experts utilize medical observations. These observations are highly based on the expert’s experience and can vary among clinicians due to its subjective nature, leading to differences in evaluation. The gait patterns of individuals with PD typically exhibit distinctions from those of adults. Evaluating these gait malformations not only aids in diagnosing PD but can also enable the categorization of severity stages with respect to symptoms of motor movement. Therefore, this paper introduces a classification of gait model based on the optimized deep learning (DL) model bidirectional gated recurrent unit-artificial hummingbird optimizer (BI-GRU-AHO). The training and testing involved the sequential segmentation of the right and left instances from the signals of vertical ground reaction force (VGRF) based on the identified gait cycle. The outcomes of the proposed BI-GRU-AHO exhibits reliable and accurate assessment of PD and achieved better accuracy of 98.7 %. The proposed model is trained and tested satisfactorily; hence it can be implemented in a real-time environment by integrating the model into a software application or system capable of receiving real-time data from PD patients.
Volume: 37
Issue: 2
Page: 1140-1149
Publish at: 2025-02-01
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