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International Journal of Basic and Applied Science
Published by Institute of Computer Science
ISSN : 23018038     EISSN : 27763013     DOI : https://doi.org/10.35335/ijobas
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics
International Journal of Basic and Applied Science provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
Arjuna Subject : Umum - Umum
Articles 126 Documents
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Longitudinal Alzheimer’s Disease Progression Modelling Using Adaptive Spline Regression Harahap, Muhammad Khoiruddin; Hendraputra, Surya
International Journal of Basic and Applied Science Vol. 14 No. 3 (2025): Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i3.748

Abstract

Alzheimer’s disease is one of the most prevalent neurodegenerative disorders, and modeling its longitudinal progression is essential for improving early intervention and clinical decision-making. While spline-based approaches have been widely used to capture nonlinear patterns, their application to longitudinal Alzheimer’s progression remains limited, particularly with respect to adaptive knot selection and clinical interpretability. This study addresses this gap by applying adaptive spline regression with automatic knot selection via Generalized Cross Validation (GCV) to longitudinal Alzheimer’s disease modeling. Using a simulated longitudinal dataset of 200 patients explicitly designed to reflect realistic clinical characteristics such as cognitive decline (MMSE), hippocampal volume change, and APOE ε4 genetic status we systematically evaluate the proposed method under controlled conditions. The adaptive spline model is compared against linear regression and static (fixed-knot) spline regression using 5-fold cross-validation. The results show that adaptive spline regression achieves lower RMSE (0.191) and MAE (0.152), and a higher R² (0.130) than the baseline models. Although the explained variance remains modest, the adaptive spline more effectively captures nonlinear progression patterns and yields smoother, clinically interpretable trajectories. These findings demonstrate that adaptive knot selection enhances both flexibility and interpretability in longitudinal disease modeling. From a practical perspective, the resulting progression curves have potential value for exploratory clinical analysis and hypothesis generation. Future work will focus on validating the framework using real-world datasets such as OASIS and ADNI, and extending the model to incorporate multimodal biomarkers for improved clinical relevance.
KMS for overcoming stunting in early childhood and pregnant women using the Soft System Methodology (SSM) with the Learning Lesson System (LLS) approach Krisnanik, Erly; Adrezob, Muhammad; Kraugusteeliana, Kraugusteeliana; Yulistiawan, Bambang Saras; Susramae, I Gede
International Journal of Basic and Applied Science Vol. 14 No. 3 (2025): Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i3.834

Abstract

This study addresses the concerning prevalence of stunting among early childhood and pregnant women in Indramayu Regency, which reached 18.4% in 2024, exceeding the national target of 14%. It aims to develop a Knowledge Management System (KMS) to support integrated stunting control efforts by employing Soft Systems Methodology (SSM) for comprehensive problem identification and the Learning Lesson System (LLS) to incorporate proven best practices. The KMS is designed to optimize information distribution regarding the causes, impacts, and interventions for the stunting issue, while enhancing collaboration among government, community, and families. The integration of SSM and LLS allows the system to adapt to changing local conditions and needs, providing relevant, evidence-based information. This research result suggests that the implementation of KMS can significantly improve the effectiveness of health policies and intervention programs at reducing stunting, particularly among vulnerable populations. However, questions remain regarding the specific features of the KMS, the implementation strategy within communities, and the evaluation measures for assessing its long-term effectiveness in combating stunting.
Enhancing XGBoost performance for classification tasks using particle swarm optimization and SHAP-based model interpretability Budiman, Mohammad Andri; Manurung, Jonson
International Journal of Basic and Applied Science Vol. 14 No. 4 (2026): March: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i4.771

Abstract

Phishing remains one of the most critical and rapidly evolving cyber threats, with increasing incidents that challenge conventional detection mechanisms such as blacklist-based approaches. Although machine learning models have improved phishing detection accuracy, many studies emphasize performance optimization without adequately addressing model interpretability and transparent decision-making. This study aims to develop an optimized and explainable phishing detection framework by integrating XGBoost with Particle Swarm Optimization (PSO) for hyperparameter tuning and SHAP for interpretability analysis. The proposed approach was evaluated on the UCI Phishing Websites dataset consisting of 11,055 samples and 30 features, using accuracy, precision, recall, F1-score, and ROC-AUC as performance metrics. Experimental results show that XGBoost optimized using PSO achieved the best performance with an accuracy of 0.911, precision of 0.906, recall of 0.902, F1-score of 0.904, and ROC-AUC of 0.935, outperforming Random Forest (accuracy 0.896; ROC-AUC 0.921), SVM (accuracy 0.872; ROC-AUC 0.903), and XGBoost with default hyperparameters (accuracy 0.842; ROC-AUC 0.875). Furthermore, SHAP analysis identified key influential features such as Have_IP and URL_Length, providing transparent insights into model decisions. These findings demonstrate that combining metaheuristic optimization with explainable AI significantly enhances both predictive performance and interpretability, contributing to the development of reliable and trustworthy phishing detection systems in dynamic cybersecurity environments.
Electrooculography Based Control of a Robotic Manipulator with Dual Cameras for Object Retrieval Rusydi, Muhammad Ilhamdi; Gultom, Andre Paskah; Jordan, Adam; Nurhadi, Rahmad Novan; Darwison, Darwison
International Journal of Basic and Applied Science Vol. 14 No. 4 (2026): March: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i4.798

Abstract

This study presents an assistive control system for a four-degree-of-freedom (4-DoF) robotic manipulator that integrates image-based spatial perception with electrooculography (EOG)-based human–machine interaction for three-dimensional object retrieval. The system is motivated by the need for intuitive, non-contact assistive technologies to support individuals with severe motor impairments, such as tetraplegia, in performing basic manipulation tasks. The proposed framework employs an orthogonal dual-camera vision configuration to achieve explicit 3D target localization, where planar object positions on the XY plane and depth along the Z axis are estimated using focal length–based geometric modeling. User commands are generated through an EOG interface, in which eye movements and voluntary blinks are classified using a K-Nearest Neighbor (KNN) algorithm to control manipulator motion. Compared to conventional assistive robotic systems that rely on depth sensors or high-degree-of-freedom manipulators, the proposed approach utilizes asymmetric monocular viewpoints and a minimal 4-DoF architecture to reduce system complexity. Experimental results demonstrate high performance, achieving average localization accuracies of 99.52% on the XY plane and 95.88% along the Z axis, as well as an EOG classification accuracy of 94.38%. Manipulation experiments confirmed reliable operation with a 100% task success rate, while task completion time and positional error increased gradually with target distance. These findings validate the feasibility of the proposed system as a low-complexity, high-accuracy assistive robotic solution for rehabilitation and human–machine interaction applications.
Augmented Reality Applications for Enhancing Environmental Awareness in Smart Tourism: A Systematic Literature Review Siregar, Victor Marudut Mulia; Manalu, Andi Setiadi; Saragih, Roy Sahputra
International Journal of Basic and Applied Science Vol. 14 No. 4 (2026): March: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i4.841

Abstract

Augmented Reality (AR) has been increasingly adopted in smart tourism to enhance visitor experiences and support sustainability-oriented learning. However, empirical evidence regarding how AR applications contribute to environmental awareness and sustainable tourism practices remains fragmented and insufficiently synthesized. This study conducts a systematic literature review to examine the role of AR in enhancing environmental awareness within smart tourism contexts and its potential contribution to sustainability-oriented tourism development. The review addresses three research questions concerning the implementation of AR applications for environmental learning, the comparative effectiveness of AR and non-AR approaches, and the key challenges and research opportunities associated with AR in sustainability-oriented tourism. The review follows the PICOC framework (Population, Intervention, Comparison, Outcome, Context) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A structured search of the Scopus database covering publications from 2022 to 2025 resulted in 32 empirical journal articles included in the final analysis. The findings indicate that AR applications, such as mobile AR systems, location-based interpretation, immersive environmental visualization, and gamified learning tools, are widely implemented in tourism environments including museums, heritage sites, geotourism destinations, natural parks, and wildlife attractions. Overall, AR tends to enhance environmental understanding, emotional engagement, and pro-environmental intentions more effectively than conventional interpretation media. These outcomes contribute to strengthening visitor awareness of environmental conservation and responsible tourism behavior. This review synthesizes fragmented empirical evidence and highlights key methodological and technological gaps while outlining future research directions for advancing AR-based environmental learning and sustainability practices within smart tourism ecosystems.
Explainable Mitochondrial Image Segmentation and Morphological Quantification using Deep Learning Based Framework Malik, Vandana; Singh, A. J
International Journal of Basic and Applied Science Vol. 14 No. 4 (2026): March: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i4.845

Abstract

Mitochondria is an essential cell organelle with varying shape and size. A slight change in mitochondrial morphology leads to neurodegenerative diseases. The advanced deep learning-based models like U-Net, Mark R-CNN, MitoNet, MitoStructSeg, MitoSkel perform accurate mitochondrial image analysis by performing image segmentation or morphological quantification but are devoid of the ability to interpret the results produced. This research work proposed a novel unified XM-DL framework (Explainable Mitochondrial Deep Learning Based Framework) capable of performing multiple tasks like image segmentation, morphological quantification, classification of mitochondria on the basis of their shape, and interpreting results by using explainable artificial intelligence (XAI) techniques as a single pipeline. The XM-DL framework is composed of U-Net architecture integrated with residual connections, skip connections, and attention gates for performing image segmentation, followed by a post processing module for morphological quantification and utilizing Gradient Class Activation Mapping (Grad-CAM) as explainable AI and form a unique pipeline.  The XM-DL framework was trained on the MitoEM dataset and achieved a high F1 score of 0.9322 and IoU (intersection over union) of 0.8793 for image segmentation task. The XM-DL framework provides assistance to the medical service providers by improving the interpretability and understanding about the deep learning techniques.

Page 13 of 13 | Total Record : 126

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