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INDONESIA
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 2 Documents
 1 
Search results for , issue "Vol. 14 No. 3 (2025): Optimization and Artificial Intelligence" : 2 Documents clear
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.

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