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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 129 Documents
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Longitudinal Alzheimer’s Disease Progression Modelling Using Adaptive Spline Regression Muhammad Khoiruddin Harahap; Surya Hendraputra
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 Erly Krisnanik; Muhammad Adrezob; Kraugusteeliana Kraugusteeliana; Bambang Saras Yulistiawan; I Gede Susramae
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 Mohammad Andri Budiman; Jonson Manurung
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 Muhammad Ilhamdi Rusydi; Andre Paskah Gultom; Adam Jordan; Rahmad Novan Nurhadi; 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 Victor Marudut Mulia Siregar; Andi Setiadi Manalu; Roy Sahputra Saragih
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 Vandana Malik; A. J Singh
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.
Scenario based two stage production planning for cassava SMEs under demand uncertainty Dedy Juliandry Panjaitan; Rima Aprilia; Firmansyah Firmansyah
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.v14i4.850

Abstract

Production planning in small and medium sized enterprises (SMEs) is commonly based on deterministic assumptions that do not fully reflect uncertain market demand. This study develops a scenario-based production planning approach to support feasible and cost efficient decisions under demand uncertainty. Two stage stochastic programming model with demand scenarios is applied to a real multi-product SME (small medium enterprises) case, where three demand scenarios pessimistic, most likely, and optimistic are constructed from historical data. The model incorporates production costs, raw material availability, labor capacity, and machine capacity constraints and is solved using a standard linear programming solver with actual operational data. The results indicate that optimal production quantities and total production costs vary across demand scenarios due to differences in demand limits and resource availability. While deterministic planning becomes infeasible under extreme demand conditions, the proposed Two stage stochastic programming model consistently produces feasible and cost efficient production plans, resulting in consistently feasible solutions across all demand scenarios, and highlighting its usefulness as a practical decision support tool  for SMEs facing demand uncertainty.
Hybrid ABSA–C5.0 framework for interpretable classification of tourist perceptions in digital destination services Fristi Riandari; Ramadhanu Ginting; Indri Sulistianingsih; Virdyra Tasril; Ade Rizka
International Journal of Basic and Applied Science Vol. 15 No. 1 (2026): Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v15i1.856

Abstract

This study proposes an interpretable ABSA–C5.0 hybrid framework for analyzing tourists’ perceptions of digital destination information services. This framework integrates Aspect-Based Sentiment Analysis (ABSA) for aspect extraction and sentiment assessment with C5.0 decision trees for classification and rule generation. This study follows the Knowledge Discovery in Databases (KDD) process, including preprocessing, feature engineering, modeling, and evaluation. The experiment uses a statistically synthesized dataset containing 72 labeled reviews, designed to reflect real-world online review patterns. With a 70:30 validation split, the model achieved 97.22% accuracy and a Cohen’s Kappa value of 0.947 in this controlled setting. However, these results are not intended for generalization, given the limited dataset size, synthetic data construction, and the absence of baseline comparisons and statistical significance tests. The extracted rules indicate that interactivity, clarity, and response speed are the primary factors driving positive perceptions. This framework is suitable for exploratory analysis, while further validation with real-world data and comparative models is required.
Metaheuristic Optimized Fuzzy Ensemble for Maize Seed Quality Prediction Using Vis/NIR Spectroscopy Ridwan Raafiudin; Ali Khumaidi; Indra Permana Solihin; Erik Mulyana
International Journal of Basic and Applied Science Vol. 15 No. 1 (2026): Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v15i1.886

Abstract

Maize (Zea mays) seed quality assessment is essential for supporting agricultural productivity and sustainable seed management. This study proposes a non-destructive machine learning framework for predicting maize seed quality using portable Visible/Near-Infrared (Vis/NIR) spectroscopy. The framework integrates NIPPY-based spectral preprocessing, metaheuristic wavelength selection, and fuzzy ensemble learning to handle spectral noise, multicollinearity, and nonlinear relationships in small-sample spectral data. Informative wavelengths were selected using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Two fuzzy ensemble models were developed: a Fuzzy Residual-Corrected Ensemble that refines predictions through residual-based fuzzy correction, and an RF+XGB Fuzzy Ensemble that combines Random Forest and XGBoost outputs using confidence-based fuzzy weighting. The models were evaluated for Moisture Content (MC), Germination Rate (GR), and Electrical Conductivity (EC) using repeated cross-validation, variability measures, and statistical validation. The proposed fuzzy ensemble models achieved R² values ranging from 0.8249 to 0.8689 and showed performance comparable to the strongest Random Forest baseline. Statistical comparison indicated that the main contribution of the fuzzy ensemble framework lies not in large gains in mean accuracy, but in prediction stability, residual correction, and uncertainty-aware modeling. SHAP-based explainability further identified physiologically meaningful wavelength regions, including visible pigment-related bands and near-infrared moisture-related bands. The dataset consists of 800 maize seed samples from four varieties under laboratory conditions, which limits generalization to field environments. Future work will focus on multi-location validation, domain adaptation, and real-time implementation. Overall, the proposed framework provides a statistically validated and interpretable approach for portable Vis/NIR-based maize seed quality prediction.

Page 13 of 13 | Total Record : 129

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