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All Journal Indonesian Management and Accounting Research Journal of Marine Studies
Ibrahim, Jimoh
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science

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Investigation into the influence of treatment parameters using central composite design (CCD) and characterization of chitosan extracted from marine crab shell wastes Sumaila, Abdulmumuni; Ibrahim, Jimoh; Yahaya, Muhammad Kabir; Sumaila, Ahmed Onimisi; Aniki, Samuel Adamariko
Journal of Marine Studies Volume 2, Issue 3 (November, 2025)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/joms.v2i3.22561

Abstract

The extraction and characterization of chitosan from marine crab shell waste have garnered significant attention due to the increasing demand for sustainable and biodegradable biomaterials. This study investigates the influence of critical treatment parameters on the extraction efficiency and quality of chitosan, using Response Surface Methodology (RSM) based on Central Composite Design (CCD) for optimization. Marine crab shells, an abundant seafood processing by-product, were subjected to a three-step extraction process involving demineralization, deproteinization, and deacetylation. Key variables such as acid concentration, alkali concentration, reaction temperature, and treatment duration were systematically varied to evaluate their effect on chitosan yield and properties. The optimized extraction conditions yielded high-quality chitosan with improved physicochemical properties. Characterization techniques, including Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analysis (TGA) were employed to assess the functional groups, morphology, crystallinity, and thermal stability of the extracted chitosan. The results confirmed the successful removal of calcium carbonate and proteins, and the presence of characteristic amine and hydroxyl groups indicative of chitosan. SEM analysis revealed a porous surface morphology suitable for biomedical and environmental applications. Statistical analysis of the CCD model showed a significant correlation between the treatment parameters and chitosan yield, with high predictive accuracy (R² > 0.95). This study not only demonstrates the feasibility of utilizing marine crab shell waste as a valuable resource but also highlights the effectiveness of CCD in optimizing biopolymer extraction processes. The findings contribute to the advancement of green chemistry and waste valorization strategies, promoting the circular bioeconomy and environmental sustainability.
Artificial Intelligence as a Catalyst for Enhancing Financial Reporting Quality of Listed Deposit Money Banks in Nigeria Ibrahim, Jimoh; Olowookere, Johnson Kolawole
Indonesian Management and Accounting Research Vol. 25 No. 1 (2026): Indonesian Management and Accounting Research
Publisher : Lembaga Penerbit Fakultas Ekonomi dan Bisns, Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v25i1.24021

Abstract

Factors such as inadequate information, inaccurate accounting estimates, recurring crises, and corporate financial scandals often arise from weak managerial judgment. This study therefore examines the extent to which Artificial Intelligence (AI) influences the financial reporting quality (FRQ) of listed deposit money banks (DMBs) in Osun State, Nigeria. Specifically, it investigates how expert systems, machine learning, and neural networks affect the FRQ of these banks. The study employed a cross sectional survey design, distributing questionnaires to 151 out of 243 employees across 10 selected DMBs. Partial Least Squares Structural Equation Modeling (PLS SEM) was used to analyze the data. The results reveal that AI—proxied by expert systems, machine learning, and neural networks—has a significant and positive effect on the FRQ of the selected banks. The study concludes that AI applications substantially enhance the efficiency of financial reporting processes and improve the overall financial reporting quality of listed banks in Nigeria. The study is anchored on Grand Theory, which explains how decision making processes can be enhanced through the use of AI tools. The theory supports the notion that AI enables more informed decisions by analyzing large volumes of data. Practically, the findings suggest that banks can leverage AI solutions to improve the quality and timeliness of financial reporting, thereby enhancing operational efficiency and reducing errors
Co-Authors Aniki, Samuel Adamariko Olowookere, Johnson Kolawole Sumaila, Abdulmumuni Sumaila, Ahmed Onimisi Yahaya, Muhammad Kabir