cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hengki Tamando Sihotang
Contact Email
hengkitamando26@gmail.com
Phone
+6281381251442
Journal Mail Official
hengkitamando26@gmail.com
Editorial Address
Romeby Lestari Housing Complex Blok C, No C14 Deliserdang, North Sumatra, Indonesia
Location
Unknown,
Unknown
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 3 Documents
 1 
Search results for , issue "Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence" : 3 Documents clear
Advancing Decision-Making: AI-Driven Optimization Models for Complex Systems Sihotang, Hengki Tamando; Sihotang, Jonhariono; Simbolon, Agata Putri Handayani; Panjaitan, Firta Sari; Simbolon, Roma Sinta
International Journal of Basic and Applied Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i3.581

Abstract

Effective decision-making in complex systems requires optimization models that balance multiple competing objectives, such as cost efficiency, time constraints, and adaptability to dynamic environments. This research proposes an AI-driven optimization model utilizing the Pareto optimization algorithm to enhance decision-making accuracy and system resilience. The model was tested in a logistics scenario, demonstrating a 10% reduction in operational costs and a 36% decrease in time deviations while improving adaptability to real-time disruptions. Unlike traditional static models, the proposed framework dynamically adjusts to external factors, optimizing resource allocation and route planning in real-world conditions. The findings highlight the model’s capability to bridge the gap between theoretical AI advancements and practical applications in industries such as supply chain management, urban transportation, and disaster response logistics. While computational requirements and data availability pose challenges, future research should explore computational efficiency enhancements, broader industry applications, and sustainability integration. This study contributes to the advancement of AI-based multi-objective optimization, providing a scalable and adaptable solution for complex decision-making in dynamic environments
Performance Comparison of Naive Bayes and Support Vector Machine Algorithms in Spambot Classification in Emails Manurung, Jonson; Saragih, Hondor
International Journal of Basic and Applied Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i3.522

Abstract

In the ever-growing digital era, email spam is a serious threat that affects user productivity and information security. This study aims to analyze the comparative effectiveness of Naive Bayes and SVM algorithms with radial basis function (RBF) kernels in classifying spambots in emails. The methodology used includes collecting email datasets, applying both algorithms for classification, and evaluating performance using accuracy, precision, recall, and f1-score metrics. The results showed that SVM RBF performed better than Gaussian Naive Bayes, with significant improvements in all evaluation metrics. These findings provide important insights for the development of more accurate and efficient spam detection systems, and highlight the importance of selecting appropriate algorithms in the face of complex data classification challenges.
Advancing optimization algorithms with fixed point theory in generalized metric vector spaces Vinsensia, Desi; Utami, Yulia; Awawdeh, Benavides Khan; Bausch, Nocedals Bertesh
International Journal of Basic and Applied Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i3.621

Abstract

This research develops and evaluates an adaptive parameter-based fixed point iterative algorithm within generalized metric vector spaces to improve stability and convergence speed in optimization problems. The study extends fixed point theory beyond classical metric spaces by incorporating a more flexible structure that accommodates non-Euclidean systems, commonly found in machine learning, data analysis, and dynamic systems optimization. The proposed adaptive fixed point algorithm modifies the conventional iterative method: where the adaptive parameter dynamically adjusts based on the previous iterations: with as a control constant. A numerical case study demonstrates the algorithm’s effectiveness, comparing it with the classical Banach Fixed Point Theorem. Results show that the adaptive method requires fewer iterations to achieve convergence while maintaining higher stability, significantly outperforming the standard approach. The findings suggest that incorporating adaptive parameters in fixed point iterations enhances computational efficiency, particularly in non-convex optimization and deep learning training models. Future research will explore the algorithm’s robustness in high-dimensional spaces, its integration with hybrid optimization techniques, and applications in uncertain and noisy environments.

Page 1 of 1 | Total Record : 3

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 14 No. 2 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 14 No. 1 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 13 No. 4 (2025): Computer Science, Engineering, Basic and Applied mathematics Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence Vol. 13 No. 2 (2024): Sep: Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science Vol. 12 No. 4 (2024): March: Basic and Applied Science Vol. 12 No. 3 (2023): December: Basic and Applied Science Vol. 12 No. 2 (2023): Sep: Basic and Applied Science Vol. 12 No. 1 (2023): June: Basic and Applied Science Vol. 11 No. 4 (2023): March: Basic and Applied Science Vol. 11 No. 3 (2022): December: Basic and Applied Science Vol. 11 No. 2 (2022): Sep: Basic and Applied Science Vol. 11 No. 1 (2022): June: Basic and Applied Science Vol. 10 No. 4 (2022): March: Basic and Applied Science Vol. 10 No. 3 (2021): December: Basic and Applied Science Vol. 10 No. 2 (2021): September: Basic and Applied Science Vol. 10 No. 1 (2021): June: Basic and Applied Science Vol. 9 No. 4 (2021): March: Basic and Applied Science Vol. 9 No. 3 (2020): December: Basic and Applied Science Vol. 9 No. 2 (2020): September: Basic and Applied Science Vol. 9 No. 1 (2020): June: Basic and Applied Science Vol. 8 No. 4 (2020): March: Basic and Applied Science Vol. 8 No. 3 (2019): December: Basic and Applied Science Vol. 8 No. 2 (2019): September: Basic and Applied Science More Issue