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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 5 Documents
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Search results for , issue "Vol. 13 No. 2 (2024): Sep: Basic and Applied Science" : 5 Documents clear
Growth and biochemical responses of red chili (Capsicum annuum L) under drought conditions with 6-Benzylaminopurine application Tambun Sihotang; Luthfi Aziz Mahmud Siregar; Nini Rahmawati
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.v13i2.404

Abstract

The purpose of this research is to assess how red chili plants (Capsicum annuum L.) develop and react biochemically to drought stress, as well as how cytokinin treatment affects these plants. The study employed a factorial Randomized Block Design (RAK) with three replications and two components, namely the degree of drought with three stages, comprising: K1 has an 80% soil water content, K2 has a 60% soil water content, K3 has a 40% soil water content, and S0, S1, S2, and S3 have 0 ppm, 10 ppm, 20 ppm, and 30 ppm of 6-benzylaminopurine concentration, respectively. Plant height, leaf count, root length, flowering age, total and aqueous chlorophyll content, activity of antioxidant enzymes (e.g., superoxide dismutase and peroxide dismutase), and hydrogen peroxide as a signal for plant molecules against dehydration stress are among the parameters assessed. The findings demonstrated that red chili plants under drought stress experienced slower growth, as seen by a reduction in height and leaf count as well as earlier flowering. However, by raising plant height, leaf count, and chlorophyll levels (a, b, and total), cytokinin treatment was able to lessen the deleterious impacts of drought. When treated with 10 ppm 6-Benzylaminopurine, the enzyme activity of superoxide dismutase, peroxide dismutase, and hydrogen peroxide increased, but at other dosages, it tended to decrease, suggesting a slight but noticeable increase in plant defense mechanisms against oxidative stress. Therefore, giving red chili plants 10 parts per million of cytokinin may be a useful tactic for enhancing their resistance to drought stress.
Chemometric analysis of fingerprinting derivative spectrophotometry for authentication of shallots Adelia Puteri; Triyadi Hendra Wijaya; Hendri Wasito
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.
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.

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