IAES International Journal of Artificial Intelligence (IJ-AI)
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.
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<![CDATA[FaceSynth: text-to-face generation using CLIP and its variants with generative adversarial networks ]]>
<![CDATA[Ravisankar, Priyadharsini]]>;
<![CDATA[Dhanvanth, Shruthi]]>;
<![CDATA[Jenane Padmanabhan, Vaishnave]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3588-3598
<![CDATA[In recent years, there have been massive developments in the field of generative AI, especially in generative adversarial networks (GANs). GANs generate original images that haven't been seen during training and have had several advancements like StyleGAN, StyleGAN2, and StyleGAN2-adaptive discriminator augmentation (ADA). Contrastive language-image pre-training (CLIP), by OpenAI, is a visual linguistic model that has been trained to associate texts with images. Recently, new CLIP variants were developed, such as metadata-curated language-image pre-training (MetaCLIP), released by Facebook and trained on a larger dataset, and Multilinigual-CLIP, which adapts CLIP to multiple languages. We compare CLIP and its variants in text-to-face synthesis with a custom StyleGAN2-ADA model and a pre-trained StyleGAN2 model. Our training-free algorithm starts with an initial image latent code that is iteratively manipulated to match a given text description. It achieves this by minimizing the distance between the text and image embedding in the multi-modal embedding space of the CLIP models. An examination of CLIP and its variants showed that MetaCLIP outperformed its competitors in LPIPS similarity and closeness of the synthesized image to the actual prompt. CLIP produced the most realistic images with the best FID score and multilingual-CLIP presented a choice of input text language and generated decent images.]]>
<![CDATA[Data-driven clustering and prediction of high school graduation rates in Indonesia (2015-2023) using machine learning ]]>
<![CDATA[Salman Arrosyid, Muhammad]]>;
<![CDATA[Marzuki, Marzuki]]>;
<![CDATA[Widihastuti, Widihastuti]]>;
<![CDATA[Haryanto, Haryanto]]>;
<![CDATA[Fransiska Mbari, Maria Angelina]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3771-3780
<![CDATA[This study aims to analyze the graduation rate of senior high school education in 34 Indonesian provinces during the period 2015-2023 and identify patterns of educational disparities between regions. To achieve the objectives, this study applies a neural network to predict education completion patterns based on historical data, then the prediction results are analyzed using K-means clustering technique utilizing the elbow method to select the ideal number of clusters. The clustering results show three categories of provinces based on education completion rates: high, medium, and low. The provinces with high completion rates, generally, supported with good education infrastructure and effective policies, while the medium category faces challenges in resource distribution, but still potentially improve. In contrast, the low category suffers from limited access, geographical constraints, and socio-economic disparities. This research contributes to education policy-making by offering a machine learning-based approach to understanding education disparities between regions. The new insight offered by this study lies in the integration of neural network and K-means clustering in mapping education completion rates to support strategies for improving access and quality of education in Indonesia.]]>
<![CDATA[Transformation of Islamic values in the era of artificial intelligence ]]>
<![CDATA[Faizin, Nur]]>;
<![CDATA[Ma`ali, Abul]]>;
<![CDATA[Hidayatullah, Muhammad Fahmi]]>;
<![CDATA[Nasih, Ahmad Munjin]]>;
<![CDATA[Faizah, Rohmatul]]>;
<![CDATA[Fauzan, Moh.]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4353-4362
<![CDATA[The emergence of artificial intelligence (AI) such as ChatGPT has brought significant changes in the way humans’ access and understand information, including in the religious field. This research aims to examine how the transformation of Islamic values occurs through ChatGPT responses in the aspects of educational ethics, Islamic law, da'wah, and Qur'anic interpretation. This study applied a qualitative case study method and data was collected from indexed scientific articles from academic databases, ChatGPT responses, and online news articles. The study findings show that the use of ChatGPT in the context of Islam requires caution. While technology can answer a variety of questions, there are fundamental flaws related to the accuracy of citations, unverified sources of information, and a lack of understanding of the sharia context. In fact, there are errors in the mention of Qur'anic verses that have the potential to cause confusion. This emphasizes the importance of the sanad principle in Islamic scholarship as a valid reference. The paper proposes the need to develop more ethical and contextual AI systems in understanding religious questions, as well as the involvement of scholars and academics in training machines to conform to Islamic values.]]>
<![CDATA[Recommendation system for football player recruitment using k-nearest neighbor ]]>
<![CDATA[Maukar, Maukar]]>;
<![CDATA[Rodiah, Rodiah]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3847-3857
<![CDATA[In modern professional football, achieving a competitive edge depends not only on on-field performance but also on effective off-field strategies, particularly in player recruitment. This study proposes a machine learning-based recommendation system to support talent identification and optimal player placement using statistical performance data. The model analyzes a wide range of features, including shots, expected goals, expected assists, pass types, offensive contributions, and defensive actions across field zones. The dataset undergoes preprocessing steps such as normalization (per 90 minutes) and dimensionality reduction. A key innovation of this research is the use of principal component analysis (PCA) to reduce feature dimensionality, minimizing redundancy while retaining essential information, which improves model efficiency and scalability. The refined data is then processed using the k-nearest neighbors (KNN) algorithm with cosine similarity, allowing the system to identify players with similar performance profiles based on directional similarity in a high-dimensional space. This combination enhances recommendation accuracy by focusing on performance structure rather than raw values. The resulting system provides actionable insights into player suitability and potential, offering clubs a data-driven tool for informed scouting and recruitment decisions. The approach demonstrates the effectiveness of combining PCA and KNN in optimizing football player recommendation systems.]]>
<![CDATA[A hybrid model for handling the imbalanced multiclass classification problem ]]>
<![CDATA[Alshdaifat, Esra'a]]>;
<![CDATA[Hussein, Fairouz]]>;
<![CDATA[Al-shdaifat, Ala'a]]>;
<![CDATA[Al-Hassan, Malak]]>;
<![CDATA[Altarawneh, Enshirah]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3982-3993
<![CDATA[Data in many application domains is imbalanced. In machine learning, addressing imbalanced data is crucial to prevent bias towards the dominant class label and ensure that prediction models can learn and predict the minority class proficiently. This paper proposes a hybrid imbalanced classification model (HICD) to address the multiclass imbalanced data problem. The primary idea is to combine effective methods to construct a classification model that can handle multiclass imbalanced data effectively. Four methods are employed: an oversampling method to balance the data, a decomposition method to convert the multiclass problem into a set of binary problems, ensemble classification to integrate base classifiers to improve prediction, and a boosting method to encourage the classifier to pay more attention to misclassified samples. To evaluate the proposed model, seventeen imbalanced datasets from various application domains, featuring different numbers of classes, instances, features, and imbalance ratios, are assessed. The experimental results and statistical significance tests demonstrate that the proposed hybrid model significantly outperforms the standard one-vs-one (OVO) approach and the OVO combined with oversampling technique (SMOTE), both considered state-of-the-art for addressing imbalanced multiclass datasets, in terms of F1-score.]]>
<![CDATA[Anisa: artificial intelligence companion for elderly care with empathetic conversations and health management ]]>
<![CDATA[Karegoudra, Shilpa]]>;
<![CDATA[Hegde, Pawan]]>;
<![CDATA[Poojary, Sinchana C.]]>;
<![CDATA[Shetty, Pranitha P.]]>;
<![CDATA[Kotian, Sahana M.]]>;
<![CDATA[Kallianpur, Saanvi]]>;
<![CDATA[Koti, Veeresha R.]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4260-4270
<![CDATA[This study introduces Anisa, an advanced artificial intelligence (AI) companion designed to enhance elderly care by addressing the multifaceted needs and challenges of older adults. The system integrates the Llama 3.2 model, powered by Groq, to facilitate context-aware dialogues and empathetic interactions. This capability helps alleviate loneliness and provides essential companionship. Agenda.js is used for scheduling and managing reminders, ensuring timely notifications for medications and appointments. Additionally, Twilio enables emergency alerts when distress signals are detected. Anisa promotes physical activity, tracks daily routines, and generates activity reports shared with caregivers and healthcare providers. Expo CLI implements step-tracking and document-sharing features. By integrating these functionalities, Anisa improves the quality of life for seniors, eases caregiver responsibilities, and fosters a safer, more supportive environment.]]>
<![CDATA[Accuracy of long short-term memory model in predicting YoY inflation of cities in Indonesia ]]>
<![CDATA[Leipary, Harfely]]>;
<![CDATA[Setiawan, Adi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3887-3896
<![CDATA[Our research evaluates the effectiveness of the long short-term memory (LSTM) model in forecasting annual year-on-year (YoY) inflation across 82 cities in Indonesia based on time series data from BPS economic reports for 2014-2024. This study tests the accuracy of the model in reconstructing past inflation patterns, then evaluates the capabilities and limitations of the model in various urban area contexts with the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination(R2) metrics. The findings show that LSTM performs well in metropolitan areas such as Jakarta, Bandung, and Surabaya with R2values >0.8 and the lowest MAPE of 10.91% in Jakarta. However, in small cities with higher economic volatility such as Tanjung Pandan, the model shows significant prediction errors (R²<0.50 and MAPE up to 283.11%). Moderate performance (0.50≤ R²≤0.80) was found in cities such as Palembang, Semarang, and Makassar, reflecting the model's adaptive ability to moderate inflation patterns. These results emphasize the important role of structured economic data in improving the reliability of predictions, so that the policy implications of this study include the use of the LSTM model as an early warning system by fiscal and monetary authorities, as well as the need for a data-based inflation control strategy to strengthen regional and national economic resilience in supporting sustainable development towards Indonesia Emas 2045.]]>
<![CDATA[Enhancing challenge-based immersion in cultural game using appreciative fuzzy logic ]]>
<![CDATA[Muljono, Muljono]]>;
<![CDATA[Haryanto, Hanny]]>;
<![CDATA[Andono, Pulung Nurtantio]]>;
<![CDATA[Nugroho, Raden Arief]]>;
<![CDATA[Yakub, Fitri]]>;
<![CDATA[Sukmono, Indriyo K.]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp3702-3714
<![CDATA[Many traditional games in Indonesia are considered cultural heritage and are in serious decline; young generations no longer know about them. Serious games have been considered a potential educational tool for cultural heritage preservation. Lack of immersive experience due to over-focus on the learning content is a common problem in those games. Very little research also discusses cultural heritage serious game design frameworks. This study uses the appreciative fuzzy logic system (AFLS) to enhance the challenge-based immersive experience (CBIE) in the Joglosemar cultural heritage game. The AFLS provides autonomous challenges, such as enemy numbers and aggressive behavior, and the frequency of item appearances in the games using fuzzy logic with respect to the appreciative serious games (ASG) concepts. The ASG is the design guide for serious games that divides the game activities into 4-D: discovery, dream, design, and destiny. We use three ASG-based serious games to evaluate the CBIE produced by AFLS. The game experience questionnaire (GEQ) is used to measure the player experience, while the cross-validation is used to measure the AFLS performance. Results show that the AFLS enhances the CBIE. The study contributes mainly to provide reliable intelligent system for automated serious game design.]]>
<![CDATA[Grid graph convolutional network-cyclical learning rate EfficientNet for liver tumor segmentation classification ]]>
<![CDATA[Narasimhulu, Sangi]]>;
<![CDATA[Rao, Ch D V Subba]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4235-4249
<![CDATA[Liver tumors are identified in computed tomography (CT) images, which are crucial for accurate disease diagnosis and treatment planning as they enable clear delineation of tumors. Hence, it is vital in the field of medical radiology to segment and classify CT images of liver tumors effectively. However, liver tumor locations are not captured accurately at the boundaries in terms of size and depth within the liver due to downsampled images, leading to reduced segmentation and classification results. This research proposes a grid-graph convolutional network-based cyclical learning rate EfficientNet (GGCN-CLREN) to accurately segment and classify liver tumors. GGCN addresses inaccurate liver tumor segmentation due to downsampled images, which capture spatial relationships effectively and preserve tumor boundaries as well as depth information. For classification, CLREN optimizes classification by adjusting the learning rate, which enhances convergence and accuracy. Therefore, GGCN-CLREN ensures enhanced segmentation and classification by addressing size and depth inaccuracies. Golden sine gray wolf optimization (GSGWO) selects the most appropriate features effectively. The GGCN-CLREN achieves commendable accuracies of 99.80% and 99.96%, respectively, for the LiTS17 and CHAOS datasets when compared to the existing techniques: enhanced swim transformer network with adversarial propagation (APESTNet) and adding inception module-UNet (AIM-UNet).]]>
<![CDATA[Educational data mining approach for predicting student performance and behavior using deep learning techniques ]]>
<![CDATA[Ramaraj, Muniappan]]>;
<![CDATA[Dhendapani, Sabareeswaran]]>;
<![CDATA[Chembath, Jothish]]>;
<![CDATA[Srividhya, Selvaraj]]>;
<![CDATA[Thangarasu, Nainan]]>;
<![CDATA[Ilango, Bhaarathi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v14.i5.pp4113-4122
<![CDATA[Educational Data Mining (EDM) uncovers insights from large datasets collected from various educational platforms, such as online learning systems, student information databases, and classroom tools. EDM helps educators identify hidden patterns that improve teaching strategies, personalize learning experiences, and predict student performance. Predicting student success has become a key focus of EDM, allowing institutions to implement targeted interventions and personalized support. The dataset included academic achievement grades from 1,001 students enrolled in various courses during the fall semester across multiple years, to demonstrate how proposed models provide more accurate predictions compared to traditional machine learning methods. Models such as YOLO, Fast R-CNN, Artificial Neural Networks (ANNs), and Long Short-Term Memory (LSTM) networks are used to capture complex, non-linear relationships within the data. The comparative analysis shows that these deep learning models significantly outperform traditional techniques, such as decision trees and support vector machines (SVMs). The results indicate that proposed method offers improved predictive accuracy, enabling educational institutions to identify at-risk students and deliver tailored interventions. This study highlights the potential of enhanced method to transform personalized education and enhance student success by better understanding individual learning needs and behaviors.]]>
