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 85 Documents
 2   3   4   5   6 
Search results for , issue "Vol 15, No 2: April 2026" : 85 Documents clear
A comparative study of Arabic morphological analyzers Saadiyeh, Omar; Ramadan, Alaaeddine; Zaki, Chamseddine; Hajjar, Mohamad; Bernard, Gilles
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1876-1890

Abstract

The field of Arabic natural language processing (NLP) has witnessed significant advancements, driven by the development of various morphological analyzers. This paper compares several major Arabic morphological analyzers and examines their ability to handle word ambiguities, process dialects, operate efficiently, and support downstream NLP tasks. By reviewing previous studies, we identify key gaps, including the limited resources for dialects, the shortage of annotated corpora, and challenges related to system scalability. The study also highlights future directions, such as building larger and more diverse corpora, adapting neural models for dialects, and developing analyzers that are more interpretable and trustworthy. Overall, this comparative overview aims to provide a clearer understanding of the current state of Arabic morphological analyzers, synthesize existing research, and offer practical recommendations for future work in this area.
A deep learning-based approach for hearing loss detection Deepa, Deepa; Rao, Manjula Gururaj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1701-1708

Abstract

Millions of people across the world are affected by hearing loss and early detection is very important for effective intervention. The traditional hearing screening methods are effective but they often rely on specialized equipment and clinical resources, making them less accessible to common people. Hearing loss is a state that affects the ability to communicate, socially interact and overall quality of life. The advancements in recent years have aimed to enhance the accessibility and efficiency of hearing tests, mainly in remote areas. The accurate classification of hearing loss is essential for effective detection and treatment in audiology. This study presents a deep learning (DL)-based approach based on a feedforward neural network (FNN). This paper focuses on common causes like cerumen impaction, otitis media, and otosclerosis. The study tries to explore ways to improve the diagnosis of hearing loss. The goal is to develop solutions that make hearing screenings more accessible and cost-effective for populations with limited access to healthcare resources. The results show the advantages of DL models in supporting automated accurate classification of hearing loss for intelligent diagnostic systems in audiological healthcare.
Double-hop of reconfigurable intelligent surfaces-aided for wireless optical link under log-normal fading channels Ai, Duong Huu; Loi Nguyen, Van; Ty Luong, Khanh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1174-1180

Abstract

In optical wireless communication (OWC), the reconfigurable intelligent surfaces (RIS) are used to manipulate optical signals by controlling the phase shifts or amplitude of reflected beams, which helps improve signal quality. RIS units can be tailored to increase the strength and reliability of the communication link, especially in challenging fading conditions. The double-hop scenario involves two RIS-assisted segments, such as transmitter to RIS-1 and RIS-1 to RIS-2 or a receiver. Each hop encounters log-normal fading, which impacts the overall link performance. Log-normal fading models the irradiance fluctuation caused by turbulence, which is significant in free-space optical (FSO) systems, this fading model assumes that the received optical signal’s amplitude varies with a log-normal distribution, making it more suited for weak to moderate turbulence. Numerical results are obtained under different of link distance, subcarrier quadrature amplitude modulation (QAM) is displayed quantitatively illustrate the average symbol error rate in the absence of RIS and with double-hop of RIS.
AI-induced fatigue among students in higher education: a latent profile analysis Soriano, Dynah D.; Salenga, Jordan L.; Miranda, John Paul P.; Grume, Juvy C.; Fernando, Emerson Q.; Martinez, Jr., Amado B.; Cabrera, Raymond A.; Yambao, Jaymark A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1963-1971

Abstract

The integration of artificial intelligence (AI) tools in education offers significant benefits but also introduces challenges, including AI-induced fatigue among students. This study aimed to classify students’ experiences with AI tools using latent profile analysis (LPA). A quantitative cross sectional design and referral approach were used to collect survey data from 388 college students who actively used AI tools for academic purposes from November to December 2024. The survey measured AI usage intensity, AI literacy, self-efficacy, perceived usefulness, cognitive load, technostress, sleep quality, general fatigue levels, and attitude toward AI. Descriptive results indicated moderate levels of AI usage intensity, AI literacy, perceived usefulness, cognitive load, sleep quality, and general fatigue, with technostress and attitude toward AI also at moderate levels. Model selection considered Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and profile size adequacy, and expert review supported the retained six-profile structure. The LPA identified six interpretable user groups: competent but sleep-deprived users, overwhelmed and high-strain users, stable moderate users, strained moderate users, high intensity strained users, and low-strain selective users. The findings show differences in patterns of competence, strain, fatigue, and sleep outcomes associated with AI tool use, which supports the development of profile specific strategies to manage technostress, cognitive load, fatigue, and sleep disruption among higher education students.
Exponential long short-term memory with Levy flight optimization for lung nodule classification Gowthami, Kaliba; Jayaseelan, Kamalakannan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1451-1463

Abstract

Lung cancer, which commonly appears as lung nodules is a deadly type of cancer that develops in a lung. Early detection of lung cancer is critical and challenging task due to presence of overlapping structures, which make it challenging to differentiate the benign and malignant regions. This research proposes long short-term memory (LSTM) with exponential linear unit (ELU) method for the classification of different classes of lung nodules. The hyperparameters of the LSTM network are optimized using the developed dynamic Levy flight – Archimedes optimization algorithm (DLF-AOA), which effectively identifies the optimal parameters for classification. The ResNet-18 method is used for the extraction of high-level features to differentiate various classes of lung nodules. Furthermore, Bayesian active contour (BAC) is employed for the segmentation of images as containing cancerous and non-cancerous regions of lung nodules. The LSTM with ELU method achieves 98.56% accuracy, 97.54% sensitivity, 98.22% specificity, 96.93% precision, 96.33% F1-score, and 1.44 error rate in IQ-OTH/NCCD lung cancer dataset.
Usability analysis of the individual creativity assessment tool using the adjusted system usability scale Mohamad Rosman, Mohamad Rahimi; Md Arifin, Noor Arina; Mokhtar, Siti Aishah; Mat Nawi, Nur Ainatul Mardiah; Hamidon, Huda; Md Radzi, Salliza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1955-1962

Abstract

Creativity is a critical element in the learning environment, which leads to innovation and research advancement in higher education. However, assessing creativity is challenging due to its diverse nature and the lack of standardized tools. The existing assessment tools often overlook the critical role of organizational culture in shaping individual creativity within academic settings. To address this gap, the individual creativity assessment tool (i-CAT) was developed based on the framework of organizational culture to assess its contribution to creativity among Malaysian academicians. This study aimed to i) assess the usability of i-CAT and ii) determine the significant effect of demographic factors on its usability assessment. A quantitative methodology, utilizing expert sampling and the system usability scale (SUS), was employed as the primary evaluation method. 20 experts with relevant professional and academic experience were selected for the validation. The results showed excellent usability, with 95% of experts rating the information system as functionally acceptable. A one way analysis of variance (ANOVA) found no significant difference in usability based on profession or education levels, but a significant difference was observed for experience levels. These findings confirm that i-CAT is a functional, user-friendly, and culturally relevant tool for creativity assessment within Malaysia’s higher education institutions.
Generative artificial intelligence as powered writing tools in academic writing Gonzaga, Exequiel B.; Manguda, Nasrah A.; Tado, Rodelina B.; Amante, Ivy F.; Banguis, Rovy M.; Cedeño, Shem A.; Montaña, Joveth Jay D.; Apilar, Jai Rondo S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1121-1131

Abstract

Generative Artificial Intelligence (GAI) as a writing tool is rampantly developing and attracting attention in academic writing. This study aimed to analyze the use of GAI as an AI-powered writing tool in academic writing among college students. By using a mixed method design with criterion purposive sampling, the researchers gathered the data from eighty students through a survey and selected individuals from all year levels underwent interviews. Descriptive statistics and thematic analysis were used to analyze their perceptions and integration of GAI tools. The result reveals mainly high levels of perception: knowledge perception, “High”; frequency and extent of use, “Average”; impact on academic writing, “High”; and integration with human writers, “High”. The study further identified that the students integrate GAI writing tools to improve writing quality, efficiency, and productivity. On the other hand, their disadvantages include over-reliance on GAI tools and inaccuracy issues. The findings suggest that GAI tools integration improves academic writing, but negatively impacts the students’ character. This study stresses the importance of moderation in using GAI writing tools and recommends looking further into the different ways of effective integration.
A novel approach to detect tomato leaf disease using vision transformer Sagar, Sanjeela; Singh, Jaswinder
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1548-1565

Abstract

Tomatoes are one of the most widely consumed vegetables across the world. However, tomatoes are prone to diseases. Recognizing and classifying tomato leaf diseases is crucial task. Various deep learning (DL) methods have been developed by several researchers, but they have some complex issues like noise in images, high computational complexity, poor accuracy, and limited feature selection. The main goal of this research is to present novel DL based tomato leaf disease classification framework with neural network based gated vision transformer (G-ViT) model assisted attention mechanism. The proposed framework uses dilated convolution with bidirectional long short-term memory (Bi-DLSTM) used for efficient feature extraction to enhance the classification. An effective chaotic spider wasp optimization (CSWO) is used for feature selection. Further, novel attention based gated vision transformer (A-GVT) is used to classify tomato leaf diseases which integrates strengths of attention mechanism and G-ViT models. Further, to improve the generalizability of classification model, its parameters are tuned with black widow optimization (BWO) algorithm. The experimental findings shows that proposed framework outperformed previous studies on tomato leaf disease identification and classification models in terms of accuracy, precision, recall, F1-score, specificity, mean absolute error (MAE), and root mean square error (RMSE) with 99.7%, 98.29%, 98.22%, 98.25%, 99.19%, 0.03, and 0.25 respectively. The proposed study can pave a way for new agricultural revolution.
ResNet based deep learning approach for chronic obstructive pulmonary disease prediction using lung sound analysis Ullal, Babitha Sudhakar; Narasimhaiah, Veena Kalludi; Kamesh, Rithul
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1733-1745

Abstract

Chronic obstructive pulmonary disease (COPD) affects around 300-400 million people worldwide representing a critical healthcare challenge that requires early detection for effective intervention. This work introduces chronic lung analysis via audio signal prediction (CLASP), a novel framework achieving 97.90% accuracy in predicting COPD automatically through respiratory audio signal analysis. This method integrates advanced signal processing and deep learning architectures, comparing long short-term memory (LSTM), convolutional neural networks (CNN), and residual networks (ResNet) models for optimal performance. The ResNet architecture exhibits superior diagnostic capability with precision of 98.72%, recall of 96.86%, and 0.9937 area under the curve (AUC), as compared to existing methods by significant margins. These results establish a new benchmark for noninvasive COPD detection, thus enabling practical deployment in clinical settings thereby dramatically improving the patient outcomes by early detection and also reduce healthcare costs.
IndoBERT for educational assessment: comparative analysis of transformer models in Indonesian question generation Jati, Handaru; Indrihapsari, Yuniar; Setialana, Pradana; Wijaya, Danang; Ardy, Satya Adhiyaksa; Dwi Nur Ardiansyah, Dhista
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1804-1813

Abstract

This study asks whether a monolingual encoder can realistically outperform multilingual and larger transformer models for Indonesian automatic question generation (AQG) when all models share the same training budget. We compare Indonesian bidirectional encoder representations from transformers (IndoBERT), multilingual BERT (mBERT), and BERT-large using a single fine-tuning pipeline with answer highlighting, applied to an Indonesian version of TyDiQA-GoldP and a 20,000 translated subset of SQuAD 2.0. We evaluate model quality using bilingual evaluation understudy score n-gram 4 (BLEU-4), metric for evaluation of translation with explicit ordering (METEOR), and ROUGE-Lincoln (ROUGE-L). IndoBERT consistently achieves the best scores on both datasets (e.g., BLEU-4 of 19.69 on TyDiQA-GoldP and 3.79 on the SQuAD 2.0 subset) while requiring less computation than mBERT and BERT-large. Our results show that language-specific pretraining gives clear advantages for Indonesian AQG, yielding higher accuracy at lower computational cost than multilingual or larger encoders. The work closes a gap in Indonesian AQG benchmarking by providing the first head-to-head comparison of IndoBERT, mBERT, and BERT-large under a shared fine-tuning and evaluation protocol. For educational assessment, the findings offer a practical recipe for building deployable AQG systems on mid-range GPUs that generate higher quality questions without prohibitive training or inference budgets.

Page 4 of 9 | Total Record : 85

 2   3   4   5   6 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue 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