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 1,722 Documents
 155   156   157   158   159 
Comparison among search algorithms for hyperparameter of support vector machine optimization Nghien, Nguyen Ba; Cong, Cuong Nguyen; Thi, Nhung Nguyen; Dung, Vuong Quoc
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3802-3815

Abstract

Support vector machine (SVM) is widely used in machine learning for classification and regression tasks, but its performance is highly dependent on hyperparameter tuning. Therefore, fine-tuning these parameters is key to improving accuracy and generality. Recently, many researchers have focused only on applying different algorithms to optimize these parameters. There is a shortage of studies that compare the performance of these methods. Hence, research is needed to compare the performance of these algorithms for the hyperparameters of the SVM optimization problem. This paper compares five optimization algorithms for tuning SVM hyperparameters: grid search (GS), random search (RS), Bayesian optimization (BO), genetic algorithm (GA), and the novel chemical reaction optimization (CRO) algorithm. Experimental results on benchmark datasets such as iris, digits, wine, breast cancer Wisconsin, and credit card fraud demonstrate that CRO consistently outperforms other methods in terms of classification scoring metrics and computational time. It achieves improvements in accuracy, precision, recall, and F1-score of up to 1% on balanced datasets and up to 10% on highly imbalanced datasets such as credit card fraud. It also reduces computation time by up to 50% compared to GS, BO, and RS. These findings suggest that CRO is a promising approach for hyperparameter optimization (HPO) of SVM models.
Artificial intelligence applications in agriculture: a systematic review of literature Cabanillas-Carbonell, Michael; Zapata-Paulini, Joselyn
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3503-3519

Abstract

Artificial intelligence (AI) is transforming agriculture by offering innovative solutions to persistent challenges. This systematic literature review explores the most studied AI applications in agriculture, emphasizing crop management, agronomic decision-making, early detection of diseases and pests, and climate change adaptation. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 700 publications were retrieved from databases such as Scopus, ScienceDirect, and IEEE Xplore, with 104 relevant articles selected after applying strict inclusion and exclusion criteria. The findings underscore the importance of machine learning and image processing in tailoring agronomic practices to specific plot conditions and microclimates. These tools enable early identification and control of plant diseases and pests, reducing crop losses and dependence on chemicals. Nonetheless, challenges remain, particularly regarding accessibility for smallholder farmers, high implementation costs, and limited data infrastructure. While AI offers significant potential to enhance agricultural productivity, sustainability, and resilience, addressing these limitations is crucial. A balanced, inclusive approach is essential to ensure AI’s benefits are widely distributed and contribute to long-term food security and environmental sustainability.
A competitive learning approach to enhancing teacher effectiveness and student outcomes Tammouch, Ilyas; Nouna, Soumaya; Elouafi, Abdelamine; Nouna, Assia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3647-3655

Abstract

Machine learning has found extensive application and improvement in the field of education. Nevertheless, there remains a lack of research studies focusing on unsupervised learning within this domain. To address this gap, our study aims to investigate the relationship between teacher attributes and student achievement in Morocco while identifying regions requiring attention and intervention, using a novel clustering approach based on unsupervised competitive learning, specifically the 'Centroid neural network', to cluster Moroccan teachers based on their qualities and qualifications. Teacher qualities and qualifications are operationalized as initial teaching qualifications, completion of training programs, and employment status. To achieve our objective, we utilize the program for international student assessment (PISA) dataset, which provides comprehensive responses from individual students, including information on parental backgrounds, socio-economic positions, and school conditions. Additionally, we incorporate data from the teacher questionnaire, which encompasses background information, initial education, professional development, teaching practice, and teacher beliefs and attitudes. Consistent with previous research, our findings suggest that teachers' qualities and qualifications significantly influence student performance. Furthermore, our clustering approach identifies regions where there is a pronounced prevalence of attributes negatively impacting student achievement. Urging academicians to incorporate resilience-building measures into the design of policies in these regions to improve students' educational outcomes.
Comparative analysis of gender classification methods using convolutional neural networks Pamungkasari, Panca Dewi; Asfandima, Ilhan Alim; Rifai, Achmad Pratama; Huu Tho, Nguyen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3634-3646

Abstract

Gender classification has become an important application in the fields of system automation and artificial intelligence, having important implications across various fields. The main challenge in this classification task is the variation in illumination that affects the quality of facial images. This study presents a method for identifying genders with Convolutional Neural Networks (CNNs). To address this issue, various preprocessing methods are applied, including Self Quotient Image (SQI), Histogram Equalization, Locally Tuned Inverse Sine Nonlinear (LTISN), Gamma Intensity Correction (GIC), and Difference of Gaussian (DoG), to stabilize the effects of illumination variations before the images are processed by the CNN. The CNN architecture used consists of 5 convolutional blocks and 2 fully connected blocks, which have proven effective in image recognition. The results of the study show that the model trained with the DoG method achieved an accuracy of 91.07%, making it the best preprocessing technique compared to other methods such as SQI and HE, which achieved accuracies of 90.39% and 88.76%, respectively. These findings demonstrate that the application of SQI in CNN can improve the accuracy of gender classification on facial images, providing better performance than previous methods. These findings are expected to serve as a foundation for further developments in facial image classification and its applications in various fields.
Automatic essay scoring: leveraging Jaccard coefficient and Cosine similarity with n-gram variation in vector space model approach Dwi Cahyani, Andharini; Fathoni, Moh. Wildan; Rachman, Fika Hastarita; Basuki, Ari; Amin, Salman; Khotimah, Bain Khusnul
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3599-3612

Abstract

Automated essay scoring (AES) is a vital area of research aiming to provide efficient and accurate assessment tools for evaluating written content. This study investigates the effectiveness of two popular similarity metrics, Jaccard coefficient, and Cosine similarity, within the context of vector space models (VSM) employing unigram, bigram, and trigram representations. The data used in this research was obtained from the formative essay of the citizenship education subject in a junior high school. Each essay undergoes preprocessing to extract features using n-gram models, followed by vectorization to transform text data into numerical representations. Then, similarity scores are computed between essays using both Jaccard coefficient and Cosine similarity. The performance of the system is evaluated by analyzing the root mean square error (RMSE), which measures the difference between the scores given by human graders and those generated by the system. The result shows that the Cosine similarity outperformed the Jaccard coefficient. In terms of n-gram, unigrams have lower RMSE compared to bigrams and trigrams.
Voting classifier in pain points identification Miftahuddin, Yusup; Firdaus, Muhammad Alif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3915-3925

Abstract

A successful app understands and addresses the needs of its users. Pain points-specific difficulties and frustrations that users experience while using an application-are crucial for understanding user expectations and improving user experience. Google Play Store reviews can be a valuable source for identifying these pain points, but this raw data requires processing to be useful for developers. This study develops a model to automatically classify reviews as either containing pain points or not. We chose the voting classifier as our primary algorithm because of its proven ability to produce models with high accuracy through combining the strengths of multiple classifiers. After evaluating 5 different classifier methods, our research shows that the optimal model combines XGradient boosting, multinomial naïve Bayes, and logistic regression-with each contributing unique strengths in text classification. This combination achieves 90% accuracy and a 90% F1-Score, outperforming previous studies that used neural networks (which achieved 80% accuracy). The model successfully identifies user frustrations from app reviews, providing developers with actionable insights to improve their applications. 
Enhancing energy efficiency and accuracy in IoT-based wireless sensor networks using machine learning Shankar Sollapure, Naganna; Govindaswamy, Poornima
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3869-3878

Abstract

This study presents a novel sensor data fusion framework designed to improve accuracy and energy efficiency in internet of things (IoT)-driven wireless sensor networks (WSNs). The proposed approach combines machine learning techniques with the Kalman filter, addressing the limitations of traditional methods, such as high computational overhead and limited precision. By utilizing machine learning algorithms for pattern recognition and the Kalman filter for precise state estimation, the framework optimizes data processing while minimizing energy consumption. MATLAB-based simulations validate the model’s effectiveness, demonstrating a significant improvement in key performance metrics, including F1-score, recall, and precision, with an overall accuracy of 98.36%. The results highlight the framework’s ability to enhance fault tolerance, accelerate convergence rates, extend network lifespan, and optimize energy utilization, making it highly suitable for real-time data fusion applications in complex sensor environments. Furthermore, the proposed hybrid model is scalable and adaptable, allowing it to be implemented across various fields, including environmental surveillance, industrial automation, and healthcare monitoring. With integration of intelligent data processing techniques, this research contributes to the development of sustainable and efficient IoT-based monitoring systems capable of handling dynamic and resource-constrained environments.
An improved real time detection transformer method for retail product detection Wahyu Maulana, Andi; Adhi Wibowo, Suryo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4090-4100

Abstract

The main problem in retail product detection is intra-class variation, as some products have similar but distinct characteristics. The primary goal of this study is to address the problem of object detection on intra-class variation in retail environments. As a result, a new approach for object detection of retail products was developed by modifying the Real Time Detection Transformer (RT-DETR) model. To manage intra-class variation more successfully, the RT-DETR model is updated by modifying its architecture. There are two convolutions in the Contextual Cross-Feature Module (CCFM) fusion block section, which is adjusted by adding one convolution layer to each CCFM fusion block. A customized dataset was meticulously constructed to reflect the wide range of products frequently seen in retail outlets. For the constructed datasets, tests were run using the mean Average Precision (mAP) metric, which had a mAP0.5 of 99.5% and a mAP0.5:0.95 of 88.2%. The updated model is superior compared to original model. The difference in mAP0.5:0.95 was 2.5%, while precision increased by 1.3% and recall increased by 0.1%. Although the mAP0.5 results stay unchanged, the gains in the other metrics suggest that the RT-DETR model modifications can improve object detection skills, particularly when dealing with intra-class variation in retail merchandise.
Facial features extraction using active shape model and constrained local model: a comprehensive analysis study Iqtait, Musab; Alqaryouti, Marwan Harb; Sadeq, Ala Eddin; Abuowaida, Suhaila; Issa, Abedalhakeem; Almatarneh, Sattam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4299-4307

Abstract

Human facial feature extraction plays a critical role in various applications, including biorobotics, polygraph testing, and driver fatigue monitoring. However, many existing algorithms rely on end-to-end models that construct complex classifiers directly from face images, leading to poor interpretability. Additionally, these models often fail to capture dynamic information effectively due to insufficient consideration of respondents' personal characteristics. To address these limitations, this paper evaluates two prominent approaches: the constrained local model (CLM), which accurately extracts facial features depending on patch experts, and the active shape model (ASM), designed to simultaneously extract the appearance and shape of an object. We assess the performance of these models on the MORPH dataset using point to point error as evaluation metrics. Our experimental results demonstrate that the CLM achieves higher accuracy, while the ASM exhibits better efficiency. These findings provide valuable insights for selecting the appropriate model based on specific application requirements.
Multilabel classification sentiment analysis on Indonesian mobile app reviews Riccosan, Riccosan; Saputra, Karen Etania
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4226-4234

Abstract

Mobile applications continue to evolve to satisfy the users. For that, the developers need to understand user feedback for improvements. Indonesia, one of the countries with the most mobile app users, has many textual mobile app reviews that may be processed and analyzed. Understanding the value of mobile app reviews requires understanding the value of sentiments and emotions to create more appropriate features to satisfy the users. To acquire a more accurate analysis of user reviews, it is important to detect sentiments that are closely associated with human emotion values due to the nature of multilabeled data. This research classifies the sentiments and emotions in Indonesian textual mobile app reviews, which are multilabel and multiclass in the form of 3 sentiments, namely positive, negative, and neutral, paired with 6 emotions, namely anger, sad, fear, happy, love, and neutral. We employ the Transformers architecture model, which includes two monolingual (a generic English and an Indonesian) and a multilingual pre-trained models with the results: bidirectional encoder representations from transformers (BERT) base uncased (micro avg. F1-score=0.69, precision=0.68, recall=0.70, receiver operating characteristic-area under the curve (ROC-AUC)=0.78), IndoBERT base uncased as best result (micro avg. F1-score=0.77, precision=0.78, recall=0.76, ROC-AUC=0.85), and multilingual BERT (M-BERT) base uncased (micro avg. F1-score=0.72, precision=0.73, recall=0.71, ROC-AUC=0.82).

Page 157 of 173 | Total Record : 1722

 155   156   157   158   159 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue 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