cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Much Ibnu Subroto
Contact Email
imam@unissula.ac.id
Phone
-
Journal Mail Official
ijai@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 1,974 Documents
 193   194   195   196   197 
Hybrid deep learning for sentiment analysis of online student experiences Raja Ouadad; Hicham Mouncif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2736-2749

Abstract

The COVID-19 pandemic disrupted millions of lives worldwide, and social media platforms became a significant outlet for people to share their emotions and experiences, providing valuable insights into the challenges and opportunities of remote education. This paper analyzes student sentiments about online learning during the pandemic using Twitter data. An experimental approach is developed to analyze public comments, focusing on the sentiment expressed in tweets related to online education. A hybrid deep learning model, based on the logistic regression (LR) sentiment model, is used to predict sentiment from a large dataset of online learning-related tweets. After performing n-gram analysis to extract key topics, tweets are classified into sentiment classes. The proposed convolutional long short term memory (Conv-LSTM) and convolutional bidirectional long short-term memory (Conv-BiLSTM) models are trained on tweets annotated with granular sentiment classifications, achieving validation accuracies of 93% and 95%, respectively. This work provides meaningful insights into the emotional effects of online learning during the pandemic, contributing to the understanding of students' experiences and challenges in remote education.
Personalized learning with learning style using fuzzy for university students performance Endina Putri Purwandari; Endang Widi Winarni; Siti Soraya Abdul Rahman; Jafar Nashrudin Al Azam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2216-2228

Abstract

The main challenges of traditional learning systems are time-space constraints and teacher-centeredness. The emergence of information technology has given rise to e-learning systems characterized by teacher centred strategy components and one-size-fits-all strategies. Furthermore, the concept of personalization is presented through learning technology that provides educational content to the students learning style. This research develops a personalized system that aligns teaching strategies with students' learning styles using the Myers-Briggs Type Indicator (MBTI). The emphasis is on adaptive and revising teaching strategies to improve student learning performance. The system is developed to create student profiles to determine their learning styles based on the MBTI and fuzzy. The system was tested with undergraduate students at the information systems department in University of Bengkulu. Research shows that students in the experimental group have higher post-test scores, greater learning achievement and performance than the control group. Fuzzy clustering based personalized e-learning could improve university student performance. The use of personalized online learning significantly affects learning management system (LMS) integration, lecturers, and curriculum development.
Explainable deep learning framework for advanced deepfake video manipulation detection Shahrin Islam; Bibhas Roy Chowdhury Piyas; Fatama Jannat Tisha; Abu Saleh Musa Miah; Sadia Rahman; Shazzad Hossen; Md Abdus Samad Kamal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2674-2684

Abstract

The growing sophistication of deepfake technologies has emerged as a critical threat to the credibility of digital media by generating highly realistic yet fabricated visual content. This erodes public trust, elevates security vulnerabilities, and challenges information integrity across online platforms. Despite notable advancements, existing research still suffers from limited data diversity, insufficient model explainability, and inadequate model evaluation. To overcome this limitation, a framework for detecting deepfake video manipulation by using a transfer learning approach was introduced. Each extracted frame was processed by a convolutional neural network (CNN)-based model to obtain frame-level predictions, which were subsequently aggregated to produce the final video-level prediction using a predefined threshold. The publicly available, widely adopted FaceForensics++ dataset was used, which contains high-quality videos generated using advanced manipulation techniques. Various CNN architectures, including Xception, Densenet121, InceptionResNetV2, ResNet50, and EfficientNetB3, were explored along with rigorous hyperparameter tuning. Among these, the Xception architecture outperformed others by achieving a test accuracy of 94.5%. Gradient-weighted class activation mapping (Grad-CAM), generalized gradient-based visual explanations (Grad-CAM++), and Shapley additive explanations (SHAP) were employed to enhance model explainability by visualizing the key regions that influence deepfake detection. The research offers an effective approach to address deepfake threats and safeguard information integrity in contemporary industry 4.0.
Tomato leaf disease classification using DenseNet-121 with data augmentation and fine-tuning Sufajar Butsianto; Anggi Muhammad Rifa’i
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2521-2532

Abstract

In recent years, accurate classification of agricultural images has become increasingly important to support precision farming and crop disease monitoring. However, achieving reliable performance remains challenging due to visual similarity between disease classes and dataset variability. This study presents an applied evaluation of DenseNet-121 combined with data augmentation and fine-tuning for multi-class tomato leaf disease classification. Experiments were conducted using a publicly available tomato leaf image dataset consisting of 5,000 images across 10 classes. All images were resized to 64×64 pixels and split into 80% training and 20% testing sets using a stratified strategy. Data augmentation was applied exclusively to the training data to improve generalization. The experimental results show a progressive performance improvement across training stages, achieving a final classification accuracy of 98.44% with a loss of 4.72% after fine-tuning. Per-class evaluation indicates strong performance across most disease categories, with minor confusion observed among visually similar classes. While the results demonstrate the effectiveness of the proposed training strategy under controlled experimental conditions, further validation using real-field images is required. Overall, this study shows the potential of DenseNet-121 with transfer learning to support tomato leaf disease classification in precision agriculture applications.
ValveHealthNet: a light deep learning model for accurate valvular heart disorder detection Ausilah Alfraihat; Wafaa Al-Sharu; Ali Mohammad Alqudah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2643-2654

Abstract

Valvular heart disease (VHD) is a significant global health issue, contributing to increased morbidity and mortality rates, particularly in aging populations. Current diagnostic methods, such as echocardiography and manual auscultation, face limitations in accessibility and accuracy, particularly in resource-constrained environments. This study introduces ValveHealthNet, a lightweight deep learning model designed to classify various VHDs using heart sound recordings. Leveraging a dataset of over 10,000 heart sounds, minimal preprocessing was applied by converting the audio signals into power spectra before feeding them into a convolutional neural network (CNN) combined with a bidirectional long short-term memory (BiLSTM) network. This model achieved impressive results, with an accuracy of 98% in training and testing and 98.4% through 10-fold cross-validation. This highly efficient model can be used in embedded systems, providing a cost-effective, AI-driven solution for early detection of VHD in settings where advanced diagnostic tools may be unavailable.
Optimized classification of student performance outcomes using LEE feature selection in the context of educational data mining Kishore Kumar Kamarajugadda; Movva Pavani; Rani Vanathi Gurusamy; Nagarajan Karthikeyan; Pavan Kumar Nidumolu; Desidi Narsimha Reddy; Muniappan Ramaraj; Rajasekaran Nithya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2459-2470

Abstract

Student speculative victory is a vital area that needs to be predicted to improve the quality of education and aid the institutional decision making. This research work has to planned to use learning based enhanced evaluation (LEE) feature selection method with real world educational datasets for optimized data mining approach to predict student performance. High dimensionality and irrelevant features are common problems with enhanced models, affecting classification accuracy and efficiency. LEE feature algorithm is used to extract important features, that enhance the performance of the model, reduce the calculation quantity of the model. The methodology consists of pre-processing of the dataset, feature selection using LEE algorithm, and testing four classifiers namely support vector machine (SVM), k-nearest neighbor (KNN), adaptive learning, and naïve Bayes. The incorporation of LEE improves the model’s ability by reducing noise and highlighting the influential features. Experimental results show that optimized techniques are better in terms of accuracy and robustness than others. The models are evaluated based on important performance metrics such as accuracy, precision, recall, F1-score, and training time. The enhanced approach will help to add to the literature of the field of educational data mining (EDM), providing a practical and effective way of predicting student performance in real academic settings.
Detection of autism spectrum disorder using multilayer perceptron classifier Ahmed Q. Hadi; Saif H. Alrubaee; Fahad Taha Al-Dhief; Ammar AbdRaba Sakran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2103-2112

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by distinctive challenges in both verbal and nonverbal communication, social interaction, and repetitive behaviors. However, the diagnosis of ASD usually occurs within a clinical setting, conducted by licensed professionals, and often involves lengthy and costly procedures. On the other hand, machine learning holds significant promise for improving diagnostic and intervention research within the behavioral sciences, particularly in research concerning ASD disease. Hence, a deep investigation of a machine learning algorithm for ASD detection is crucial. Therefore, this paper presented a new system for differentiating the ASD samples from non-ASD (i.e., healthy) samples. The samples of ASD have been compiled from toddlers. The multilayer perceptron (MLP) algorithm is used to classify ASD samples from non-ASD samples. The proposed MLP classifier is implemented based on different numbers of neurons (i.e., nodes). In other words, the proposed MLP classifier started with 10 neurons and finished with 50 neurons with an increment step of 5 neurons. The outcomes demonstrate that the MLP classifier acquired different results concerning the number of neurons. The MLP obtained the best performance, reaching an accuracy rate of 100% in identifying ASD cases.
Dynamic optimization using long short-term memory and genetic algorithms for predicting marine data Mukhlis Mukhlis; Indra Jaya; Sri Nurdiati; Karlisa Priandana; Irman Hermadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2826-2837

Abstract

This study aims to develop an accurate and efficient ocean data prediction model to tackle the challenges posed by climate change and complex oceanographic dynamics. The main goal is to use long short-term memory (LSTM) networks along with genetic algorithms (GA) to predict four key ocean factors at once: sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a (Chl-a). An experimental quantitative approach is employed, utilizing satellite data from the Banda Sea region. This approach involves time series modeling using LSTM, which is optimized by GA for hyperparameters such as the number of neurons and batch size. The results show that the combined LSTM-GA model greatly improves prediction accuracy and successfully identifies seasonal trends and irregular changes in all variables, even when there is a lot of noise. Tests reveal that the optimal configuration varies for each variable, and the GA optimization process can expedite model convergence by as little as 10 epochs. These findings underscore the effectiveness of integrating evolutionary techniques in training deep learning (DL) models for ocean data. The implications of this research include potential applications in adaptive ocean monitoring systems, early warning initiatives, and data-driven planning in marine resource management.
Comparing global and variety-specific ensemble models for avocado maturity prediction with near-infrared Christian Ovalle; Jhon Garcia Jimenez; Jose Briones Zuñiga
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2300-2313

Abstract

Ensuring accurate, non-destructive maturity classification of avocados is critical to supply chain optimization in agro-industrial systems. This study presents a predictive framework that integrates near-infrared (NIR) spectroscopy with ensemble stacking machine learning (ML) to enhance the precision of avocado ripeness assessment. The proposed methodology compares global versus variety-specific models for 'Hass' and 'Fuerte' avocado types, leveraging spectral data (900–1,700 nm) and multiple base classifiers, including random forest (RF), gradient boosting (GB), support vector machines (SVMs), decision trees (DT), k-nearest neighbors (KNN), and categorical boosting (CatBoost), combined via linear regression as a meta-learner. Experimental results revealed that the stacking models outperformed individual learners, with variety-specific GB model achieving the highest performance (Matthews correlation coefficient (MCC) =0.679, area under the curve (AUC) =0.931). These findings highlight the critical importance of varietal specificity in model calibration and demonstrate how ensemble strategies can improve robustness, scalability, and interpretability in intelligent agricultural systems. The proposed model provides a computationally efficient solution for real-time quality control and supports the deployment of AI-powered systems within agricultural supply chains in developing regions.
Enhancing fake news detection: a hybrid BERT-XGBoost model for improved performance and interpretability Nishant Vasantkumar Hegde; Suneesh Bare; Namruth Reddy; Rajat Gondkar Aravinda; Minal Moharir; Aamir Ibrahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2385-2397

Abstract

The widespread spread of fake news poses a serious threat to the integrity of information. The dominant approach to detection involves end-to-end fine-tuning of large transformer models like bidirectional encoder representations from transformers (BERT), which, despite achieving high accuracy, often function as opaque “black boxes” with limited interpretability. This paper proposes and validates a hybrid, decoupled architecture that proves to be a more practical and powerful alternative. We first fine-tune a DistilBERT model on the full WELFake dataset of 71,537 articles after cleaning to create domain-specific embeddings. These high-dimensional vectors are then used as input features to train a robust extreme gradient boosting (XGBoost) classifier. The results demonstrate that the hybrid model achieves a state-of-the-art accuracy of 99.76%, slightly surpassing the already high performance of a standard end-to-end fine-tuned model. Crucially, this approach provides this top-tier performance while offering significant advantages in model interpretability through feature importance analysis. This work establishes that a decoupled architecture is not just a viable alternative but a superior practical strategy for combating misinformation, successfully balancing state-of-the-art accuracy with essential model transparency.

Page 195 of 198 | Total Record : 1974

 193   194   195   196   197 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 15, No 3: June 2026 Vol 15, No 2: April 2026 Vol 15, No 1: February 2026 Vol 14, No 6: December 2025 Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue