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 119 Documents
 8   9   10   11   12 
Chemometric analysis of fingerprinting derivative spectrophotometry for authentication of shallots Puteri, Adelia; Wijaya, Triyadi Hendra; Wasito, Hendri
International Journal of Basic and Applied Science Vol. 13 No. 2 (2024): Sep: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i2.406

Abstract

The Bima Brebes, variety of shallots, was in high demand, which led to mixing with other varieties. Derivative spectrophotometric fingerprinting combined with chemometrics was used to distinguish between authentic and adulterated shallot varieties. The objective of this study was to identify the original spectra and their derivative spectrophotometric fingerprinting, as well as classify and differentiate between shallot varieties using chemometrics. UV-Visible (UV-Vis) spectrophotometry was used to test essential oil samples from three shallot varieties and their mixtures, followed by spectral derivatization. The spectral data revealed distinct patterns for each sample, including individual varieties and mixtures, and was then analyzed using Principal Component Analysis (PCA) and Partial Least Square-Discriminant Analysis (PLS-DA). The original spectra and their derivatives showed similarities across the samples. PCA and PLS-DA results indicated that the second-order derivative data provided the greatest separation, with a total Principal Component 1 (PC1) and Principal Component 2 (PC2) value of 62.2%, a total component 1 and 2 value of 60.1%, and the highest Variable Importance in the Projection (VIP) score wavelength of 225 nm. The PLS-DA results were validated to ensure that the model was not overfit, as evidenced by a satisfactory cross-validation quality (Q2/R2) value of 0.693 and a significant permutation test. The combination of derivative spectrophotometry fingerprinting and a chemometric approach effectively classified different samples, allowing for the determination of the authenticity of a specific shallot variety.
Early warning systems for financial distress: A machine learning approach to corporate risk mitigation Judijanto, Loso; Sihotang, Jonhariono; Simbolon, Agata Putri Handayani
International Journal of Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i1.470

Abstract

This research explores the development of an early warning system for corporate financial distress using machine learning techniques to address key challenges in corporate risk mitigation. The main objective is to enhance predictive accuracy by integrating financial and non-financial data, addressing class imbalance, and ensuring model interpretability. The research design involves the formulation of a new machine learning model, leveraging cost-sensitive learning and feature selection, and is tested with a numerical example using logistic regression. Methodologically, the study adopts a data-driven approach that incorporates diverse financial ratios, macroeconomic variables, and market sentiment indicators to predict corporate distress. The numerical results from a basic logistic regression model demonstrate poor performance, especially in handling class imbalance, revealing limitations in traditional statistical models. However, the research suggests that machine learning methods, particularly ensemble learning with cost-sensitive algorithms, offer superior predictive accuracy and practical applicability. The study concludes that integrating advanced techniques and diverse datasets leads to more reliable early warning systems, with significant implications for corporate governance and financial risk management. Future research should explore more sophisticated machine learning models and extend real-world applications across various industries and economic conditions.
Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty Judijanto, Loso; Riandari, Fristi
International Journal of Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i1.474

Abstract

This research aims to develop an enhanced Fuzzy Logic Framework for Financial Distress Prediction to improve corporate decision-making under uncertainty. The primary objective is to address limitations in traditional fuzzy logic models, such as static rule bases and lack of adaptability to dynamic financial conditions. To achieve this, a time-dependent fuzzy logic system is proposed, incorporating real-time financial data and adaptive learning mechanisms to improve predictive accuracy over time. The research design involves creating a dynamic fuzzy rule base, assigning weights to rules based on predictive performance, and optimizing membership functions and rule weights using real-time data. The methodology applies the proposed framework to financial indicators such as liquidity, profitability, and leverage, with a numerical example demonstrating the system's effectiveness in predicting financial distress. The results show that the model can accurately predict financial distress levels, with a predicted distress value of 0.588 compared to an actual value of 0.6. The model’s ability to update rule weights and optimize predictions over time represents a significant improvement over static fuzzy logic models. This research fills a critical gap in financial distress prediction by introducing a dynamic, adaptive fuzzy logic framework that evolves with real-time data. The model offers significant implications for both academics and industry, providing a tool for more accurate risk assessment in volatile financial environments. However, further research is needed to refine the model’s computational efficiency and test its long-term predictive capabilities across different industries
Strategy and planning for beef cow development based on land carrying capacity in South Tapanuli District Hasibuan, Arif Sopiandin; Tafsin, Ma'ruf; Budi, Usman
International Journal of Basic and Applied Science Vol. 13 No. 1 (2024): June: Basic and Aplied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i1.435

Abstract

South Tapanuli District has quite potential in developing beef cattle by maximizing the potential of land with various types of plantation and agricultural plants as a source of animal feed. The objectives to be achieved in this research are to analyze the availability of feed based on plantation crops and food crops that support beef cattle feed sources, analyze variables in beef cattle development in influencing the number of beef cattle populations, and analyze strategic factors that influence the success of developing beef cattle and formulating strategies to increase beef cattle production in South Tapanuli District. Multiple linear regression analysis was used to examine the data. The study's findings demonstrate the abundance of potential carrying capacity of the region's land, with plantation area having the capacity to support 1,420 beef cattle annually and agricultural land having the capacity to support 14,508 cattle annually.
Data-driven corporate growth: A dynamic financial modelling framework for strategic agility Sihotang, Hengki Tamando; Vinsensia, Desi; Riandari, Fristi; Chandra, Suherman
International Journal of Basic and Applied Science Vol. 13 No. 2 (2024): Sep: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i2.485

Abstract

This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application
Dynamic optimization algorithms for enhancing blockchain network resilience against distributed attacks Riandari, Fristi; Afrisawati, Afrisawati; Afifa, Rizky Maulidya; Syahputra, Rian; Ginting, Ramadhanu
International Journal of Basic and Applied Science Vol. 13 No. 2 (2024): Sep: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i2.499

Abstract

This research introduces a dynamic optimization algorithm designed to enhance blockchain network resilience against distributed attacks such as Distributed Denial of Service (DDoS), Sybil, and eclipse attacks. The primary objective is to develop a real-time, adaptive control strategy that minimizes network performance degradation while dynamically responding to evolving threats. The research design integrates multi-objective optimization, game theory, and reinforcement learning to formulate a defense strategy that adapts to adversarial conditions. The methodology is based on a modified state-space model, where the blockchain's performance is represented by a system of dynamic equations influenced by both control actions (defensive measures) and attack vectors. The optimization problem is formulated to minimize a cost function that balances network resilience and resource usage. A numerical example is presented to validate the model, demonstrating the algorithm’s effectiveness in maintaining network performance under attack by adjusting defense mechanisms in real-time. The main results indicate that the proposed method significantly reduces the impact of distributed attacks while ensuring efficient resource allocation. In conclusion, this research offers a novel framework for enhancing blockchain security, with implications for real-world applications in decentralized systems, financial services, and critical infrastructure. Future work will address the scalability of the algorithm and explore more advanced reinforcement learning techniques to handle more complex and unpredictable attack patterns.
Fixed Point Theory in Generalized Metric Vector Spaces and their applications in Machine Learning and Optimization Algorithms Vinsensia, Desi; Utami, Yulia
International Journal of Basic and Applied Science Vol. 13 No. 2 (2024): Sep: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i2.504

Abstract

This study introduces a novel formulation of fixed-point theory within Generalized metric spaces, with an emphasis on applications in machine learning optimization and high-dimensional data analysis. Recall on the concept of complete G-metric spaces, we define a generalized contraction condition tailored for operators representing iterative updates in machine learning algorithms. The proposed framework is exemplified through gradient descent with regularization, demonstrating convergence within a non-Euclidean, high-dimensional setting. Results reveal that our approach not only strengthens convergence properties in iterative algorithms but also complements modern regularization techniques, supporting sparsity and robustness in high-dimensional spaces. These findings underscore the relevance of G-metric spaces and auxiliary functions within fixed-point theory, highlighting their potential to advance adaptive optimization methods. Future work will explore further applications across machine learning paradigms, addressing challenges such as sparse data representation and scalability in complex data environments.
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 11 of 12 | Total Record : 119

 8   9   10   11   12 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol. 14 No. 2 (2025): Sep (In Progress) 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