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31,456 Article Results

Correlation of physical activity with cognition and mental health in medical students

10.11591/ijphs.v13i4.23150
Yhusi Karina Riskawati , Faresa Aulia Rastramadhani , Henny Mulyani , Ridha Saniyyah Rindradi , Muhammad Isa Mahendra , Christyaji Indradmojo , Aswaty Nur
Low physical activity increases the number of non-communicable diseases. This phenomenon occurs in medical students. This study aimed to determine the correlation between sedentary behavior and physical activity on cognition, mental health, cortisol, and brain derived neurotropic factor (BDNF). Using a cross-sectional design of observational study, eighty-six medical students were involved in this study. The subjects were interviewed to assess cognitive function and depression levels. Saliva was taken to measure cortisol and BDNF level. Bivariate analysis was performed using the Spearman test. Depression is the only variable that correlates significantly with habitual physical activity (p=0.025, r=-0.214). Sedentary behavior has a weak correlation with cognitive failure, anxiety, and depression ([p=0.046, r=0.216]; [p=0.039, r=0.223]; [p=0.011, r=0.273]). The results found that high physical activity improves mental health and cognition. This study suggested that physical activity can alleviate symptoms of depression and anxiety among medical students.
Volume: 13
Issue: 4
Page: 1617-1625
Publish at: 2024-12-01

Scalar consistency of collaborative learning: bifactor structural equation modeling

10.11591/ijere.v13i6.30216
Nilton David Vilchez Galarza , Jose Francisco Via y Rada Vittes , Luis Angel Huaynate Espejo
The way of learning in higher education needs a revaluation, which should consider cooperation as one of the principles to improve the cognitive, procedural, and attitudinal learning of students whose training will allow them to integrate effectively into the work environment. The objective of the study was to validate the consistency of the collaborative learning scale (CLS) using the bifactor structural equation model (SEM) in university students from Junín. It presented a quantitative approach, through a deductive method, of demonstrative and inferential scope and a non-experimental design. The sample was of the probabilistic type, composed of 361 students of the Faculty of Health Sciences from cycles I to VIII at Huancayo University-2023. The results verified that the use of an original bifactor SEM allows the identification of multidimensional sources present in complex psychological measures, such as the evaluation instrument under study. In conclusion, thanks to the analysis of the Chi-square differences between the second-order model and the bifactor model, it can be verified that the bifactor model has a better fit than the second-order model because it has a lower X², a lower root mean square error of approximation (RMSEA), a lower weighted root mean residual (WRMR), and a higher ω.
Volume: 13
Issue: 6
Page: 3893-3905
Publish at: 2024-12-01

Efficient smart distributed face identification using the MixMaxSim decision function

10.11591/ijece.v14i6.pp7145-7157
Sayed Mohammad Ahmadi , Rouhollah Dianat
Recognizing a large number of people is a common challenge in face identification applications, involving decreased accuracy, increased memory and time complexities. To address these issues, this study introduces a three-module approach: “toilers,” “affinity-meter,” and “decision-maker.” Unlike the random distribution methods used in previous solutions, this method employs clustering to distribute the problem into subnetworks called “toilers.” The toiler’s module calculates the likelihood of test data belonging to each class of each toiler, using the last layer outputs of deep learning models. Meanwhile, the affinity-meter module determines the similarity between the test data and the average of each class, employing a similarity measure. The decision-maker module combines the reports from the previous two modules and selects the final class, utilizing a mix of the max-max criterion and the similarity criterion. The proposed method outperforms existing solutions, achieving improved recall, precision, and F1-score. It effectively addresses memory, speed, and accuracy issues in face identification, surpassing both no-distribution and random methods on Glint360K, VGGFace2, and MS-Celeb-1M datasets. Overall, this method offers a more efficient and accurate approach by distributing the problem into subnetworks, demonstrating superior performance and scalability for large-scale face recognition applications.
Volume: 14
Issue: 6
Page: 7145-7157
Publish at: 2024-12-01

The impact of social studies teachers’ use of e-learning techniques to develop student’s perceived self-efficacy

10.11591/ijere.v13i6.29797
Basma Mohammad Al-Hawamdeh , Israa Omar Abu Alkishek , Haitham Mohammad Ali Zureigat
Private universities in Jordan have witnessed a major shift in teaching methods and strategies, as e-learning techniques have become an integral part of the educational policies followed. Therefore, the study aimed to identify the effect of social studies teachers’ use of e-learning techniques in developing the perceived self-competencies of university students, depending on gender and field of specialization. To achieve this, the descriptive analytical method was used in conducting the study. The perceived self-efficacy test was applied to a sample of 292 university students who were randomly selected. The study found that e-learning techniques contributed to raising the level of perceived self-efficacy among males more than among females. It did not have a significant impact on its interaction with the student’s field of specialization. The results also indicate the importance of the role played by social studies teachers with technological educational techniques in improving the level of students’ self-efficacy. Moreover, social relations are among the most important factors contributing to building a conscious generation that preserves human dignity. Therefore, we must research the negatives of e learning to find out the reason behind the lack of improvement in the level of perceived self-efficacy among females.
Volume: 13
Issue: 6
Page: 3725-3731
Publish at: 2024-12-01

The potential of virtual representations to help students in learning mathematics

10.11591/ijece.v14i6.pp6411-6422
Yurizka Melia Sari , Janu Arlinwibowo , Gupita Nadindra Fatima , Dwi Purwoko , Suprapto Suprapto
The study aims to conclude the benefits of virtual representation in helping students learn mathematics. To answer this goal, this research utilizes the meta-analysis method of two-group difference design. The data collection process used inclusion criteria. The data collection process was carried out with the PRISMA. The results of the effect analysis of the p-value < 5% (95% confidence interval) and the total effect of 1.1761 so that virtual representation has a significant influence. The analysis showed that i) implementation in each continent showed identical positive effects, ii) the number of students subjected to the treatment did not make a difference, iii) themes in mathematics were equally well affected with the help of virtual representation, iv) the effect of virtual representation in junior high school, high school, and university was identical, v) the development of competencies in attitude, knowledge, and skills was equally good, vi) among the many applications, GeoGebra was the application that had the greatest impact in helping students understand mathematics subject matter, and vii) the use of smartphones had a greater effect than other devices such as computers and calculators. To produce the maximum effect in understanding students, it is recommended to use mobile devices and GeoGebra software.
Volume: 14
Issue: 6
Page: 6411-6422
Publish at: 2024-12-01

Closing the digital divide in education: an innovative review of demand and supply side policies in four ASEAN member states

10.11591/ijere.v13i6.28011
Daniel McFarlane , Yannik Mieruch , Nang Lao Wann Si
The rapid adoption of digital technologies in education highlights the importance of closing the digital divide in education. However, research on school connectivity policy regimes to bridge the digital divide in education in the Association of Southeast Asian Nations (ASEAN) region is lacking. The authors conducted a systematic review of policies in Cambodia, Malaysia, Thailand, and the Philippines following a framework synthesis approach. Through desk research, data was identified based on a supply and demand side framework and included policy documents from relevant ministries and departments, reports by international development organizations and non-governmental organizations, and academic literature. The analysis reveals an imbalance between supply and demand-side policies and regulatory approaches with governments in the analyzed countries favoring demand side policies.
Volume: 13
Issue: 6
Page: 3827-3837
Publish at: 2024-12-01

Network intrusion detection in big datasets using Spark environment and incremental learning

10.11591/ijai.v13.i4.pp4414-4421
Abdelwahed Elmoutaoukkil , Mohamed Hamlich , Amine Khatib , Marouane Chriss
Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.
Volume: 13
Issue: 4
Page: 4414-4421
Publish at: 2024-12-01

A fuzzy logic-genetic algorithm for full truckload transportation problem

10.11591/ijai.v13.i4.pp4195-4205
Karim EL Bouyahyiouy , Zahira EL Hariz , Adil Bellabdaoui
This work addresses a full truckload commodity selection and multiple depot vehicle routing problem with time windows (FTSMDVRPTW). The goal of the problem is to design a set of selective truck routes that maximize overall profit subject to time window constraints. Each truck route is an arrangement of full truckload transportation commodities that begins at a departure point and ends at an arrival point. It is unnecessary to serve all commodities; only those that provide a higher profit are chosen. We introduce a meta-heuristic based on a combination of fuzzy logic controller (FLC) and genetic algorithm (GA) to solve the FTSMDVRPTW, where the crossover and mutation rates are adjusted during the GA’s evolutionary process using an FLC. We demonstrate the effectiveness and efficiency of the proposed FLC+GA through experimental results on randomly generated instances for the considered problem.
Volume: 13
Issue: 4
Page: 4195-4205
Publish at: 2024-12-01

Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning

10.11591/ijai.v13.i4.pp4572-4582
Thomas Oka Pratama , Sunarno Sunarno , Agus Budhie Wijatna , Eko Haryono
The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.
Volume: 13
Issue: 4
Page: 4572-4582
Publish at: 2024-12-01

Systematic review: State-of-the-art in sensor-based abnormality respiration classification approaches

10.11591/ijece.v14i6.pp6929-6943
Nur Fatin Shazwani Nor Razman , Haslinah Mohd Nasir , Suraya Zainuddin , Noor Mohd Ariff Brahin , Idnin Pasya Ibrahim , Mohd Syafiq Mispan
Respiration-related disease refers to a wide range of conditions, including influenza, pneumonia, asthma, sudden infant death syndrome (SIDS) and the latest outbreak, coronavirus disease 2019 (COVID-19), and many other respiration issues. However, real-time monitoring for the detection of respiratory disorders is currently lacking and needs to be improved. Real-time respiratory measures are necessary since unsupervised treatment of respiratory problems is the main contributor to the rising death rate. Thus, this paper reviewed the classification of the respiratory signal using two different approaches for real-time monitoring applications. This research explores machine learning and deep learning approaches to forecasting respiration conditions. Every consumption of these approaches has been discussed and reviewed. In addition, the current study is reviewed to identify critical directions for developing respiration real-time applications.
Volume: 14
Issue: 6
Page: 6929-6943
Publish at: 2024-12-01

Aspect-based sentiment-analysis using topic modelling and machine-learning

10.11591/ijece.v14i6.pp6689-6698
Radhika Jinendra Dhanal , Vijay Ram Ghorpade
This study addresses the critical need for an accurate aspect-based sentiment-analysis (ABSA) model to understand sentiments effectively. The existing ABSA models often face challenges in accurately extracting aspects and determining sentiment polarity from textual data. Therefore, we propose a novel approach leveraging latent-Dirichlet-allocation (LDA) for aspect extraction and transformer-based bidirectional-encoder-representations from transformers (TF-BERT) for sentiment-polarity evaluation. The experiments were carried out on SemEval 2014 laptop and restaurant datasets. Also, a multi-domain dataset was generated by combining SemEval 2014, Amazon, and hospital reviews. The results demonstrate the superiority of the LDA-TF-BERT model, achieving 82.19% accuracy and 79.52% Macro-F1 score for the laptop task and 86.26% accuracy of 87.26% and 81.27% for Macro-F1 score for the restaurant task. This showcases the model's robustness and effectiveness in accurately analyzing textual data and extracting meaningful insights. The novelty of our work lies in combining LDA and TF-BERT, providing a comprehensive and accurate ABSA solution for various industries, thereby contributing significantly to the advancement of sentiment analysis techniques.
Volume: 14
Issue: 6
Page: 6689-6698
Publish at: 2024-12-01

Ethnocultural wisdom and development of e-comic

10.11591/ijere.v13i6.29176
Ida Ermiana , Ni Nyoman Parwati , I Gusti Putu Sudiarta , I Gde Wawan Sudatha
Cultivating local cultural values by adapting to technological advances, found in digital comic learning media oriented towards local Sasak wisdom, provides ease of learning and communicative language and is easy to access but still needs to be improved. The research aims to develop digital comic media that is feasible, practical and effective, oriented towards Sasak local wisdom (e-kodisa). The development stage uses the stages of analysis, design, development, implementation and evaluation (ADDIE). The research results show that digital comic media oriented towards Sasak local wisdom from material experts in category 1 is feasible, and material experts in category 2 are possible. The assessment of digital comic media from Media expert 1 is in the attainable category, and media expert 2 is very doable. The evaluation of learning design experts and language experts is in the very decent category. Trials on students show that digital comic media is practical and can be used in learning. The results of the effectiveness test showed sig. (2-tailed)<0.05 Ho is rejected, and Ha is accepted, meaning a significant difference exists between the pretest and posttest averages. So, developing digital comic media oriented towards Sasak local wisdom is feasible, practical and effective.
Volume: 13
Issue: 6
Page: 3618-3627
Publish at: 2024-12-01

Design and validation of research capability test for educators

10.11591/ijere.v13i6.29443
Jay-cen Tamayo Amanonce , Rudolf Tamangen Vecaldo , Maribel Fontiveros Malana , Conchita Malenab Temporal , Antonio Ingaran Tamayao , Jhoanna Battung Calubaquib , Marie Claudette Maramag Calanoga , Ria Arellano Tamayo
Assessing the research capability of educators yields implications for bridging the gap and addressing concerns along research. Accordingly, the Commission on Higher Education (CHED) highlighted substantive research concerns which need to be given attention specifically in enhancing research capabilities of graduate students, research staff, and faculty which calls for a well-designed and validated research capability test (RCT). Thus, this study established the RCT’s validity and reliability to gauge the knowledge of educators in conceptualizing, implementing, and publishing research. The developed RCT underwent series of critiquing, content validation from experts, and internal and external pilot testing. The study has generated 80 items with desirable difficulty index (50.76), reasonably good discrimination index (0.28), and large functioning distractors (78.97% distractor efficiency). Remarkably, there is a significant association between distractor efficiency and difficulty index of the items with an inter-item consistency of α=0.904. Thus, the RCT is a valid and reliable instrument to gauge the research capability of educators.
Volume: 13
Issue: 6
Page: 3741-3751
Publish at: 2024-12-01

Indonesian sentiment towards global economic recession in 2023 using optimized hyperparameters of support vector machine kernels

10.11591/ijai.v13.i4.pp4948-4956
Dairatul Maarif , Adinda Aulia Hafizha , Andi Kurniawan
The potential for the 2023 global recession has troubled people worldwide, particularly in light of the COVID-19 pandemic. This study employs a sentiment analysis approach to examine how the Indonesian internet community, particularly on Twitter, perceives the topics related to the global economic recession. We collected 11,017 uploaded tweets that were analyzed using support vector machine classifier with linear, radial basis function (RBF), sigmoid, and polynomial kernel schemes. Furthermore, we optimized the classifiers with C, Gamma, and degree hyperparameters. Empirical evidence indicates a lack of preparedness to face a global recession, evidenced by most responses towards 2023 global recession exhibiting concerns about high inflation and economic instability. The finding also suggests that the optimized RBF is a superior modeling kernel relative to others. Collectively, these results provide insights with significant implications for sentiment analysis, natural language processing, and the study of behavioural economics.
Volume: 13
Issue: 4
Page: 4948-4956
Publish at: 2024-12-01

The evolution of smart sprayer system for agricultural sector in Malaysia

10.11591/ijece.v14i6.pp6122-6128
Nur Hazahsha Shamsudin , Norman Koliah Anak Noheng , Siti Amaniah Mohd Chachuli , Nur Asmiza Selamat , Hrithik Tawai , Nurliyana Abdul Raof
This study presents the development of a smart sprayer system featuring a microcontroller, ultrasonic sensors, and a Wi-Fi module for agriculture. This system enables 360° movement capabilities and facilitates the activation and deactivation of the sprayer pump remotely. The system offers remote control functionality through smartphone integration, effectively mitigating the need for direct physical contact with hazardous chemicals during the spraying operation. The results demonstrate the efficient operation of the smart sprayer system. The average spraying efficacy is estimated to be 95%, surpassing that of conventional spraying methods, as evidenced by prior research studies. The system is accessible for remote operation via a user-friendly interface, facilitated by the integrated internet of things (IoT) and microcontroller. As anticipated, it successfully executed 360° movements, obstacle detection, water level indication, and remote control of the sprayer pump.
Volume: 14
Issue: 6
Page: 6122-6128
Publish at: 2024-12-01
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