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All Journal Aptisi Transactions on Management International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Madani, Muchlisina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Other

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AI Adoption Barriers in SMEs Analyzing Through the Technology Organization Environment TOE Framework Syahidun; Susetyono, Eko; Adiwijaya, Alfri; Madani, Muchlisina; Mustafa Kareem, Yasir
APTISI Transactions on Management (ATM) Vol 9 No 3 (2025): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/h6qyfk21

Abstract

Artificial Intelligence (AI) has become a key driver of innovation, improving efficiency, decision-making, and competitiveness in businesses worldwide. However, the adoption of AI tools among Small and Medium Enterprises (SMEs), especially in developing countries like Indonesia, remains limited. This study aims to explore the barriers hindering AI adoption in SMEs in Indonesia using the Technology Organization Environment (TOE) framework. A survey was conducted among Indonesian SMEs across various sectors to capture their perceptions regarding AI implementation. The survey focused on technological, organizational, and environmental factors that influence AI adoption. The findings reveal that technological barriers, such as high implementation costs and system complexity, are significant challenges for SMEs. Organizational barriers, including limited digital literacy, a lack of skilled workforce, and resistance to change, also hinder AI adoption. Furthermore, environmental barriers like insufficient government support, regulatory uncertainty, and low market pressure constrain SMEs' adoption readiness. This study extends the TOE framework to the context of AI adoption in SMEs in developing economies. Addressing the identified barriers is essential for accelerating digital transformation and enabling SMEs to leverage AI for sustainable growth in the digital economy.
Optimization of Machine Learning Algorithms for Fraud Detection in E-Payment Systems Rizky, Agung; Gunawan, Ahmad; Komara, Maulana Arif; Madani, Muchlisina; Harris, Ethan
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.68

Abstract

This study explores the optimization of machine learning algorithms for fraud detection in electronic payment (e-payment) systems. The rapid growth of e-payment platforms has introduced significant challenges in ensuring the security and integrity of financial transactions. Fraud detection plays a pivotal role in mitigating these risks, and the application of machine learning (ML) has emerged as a powerful tool to identify fraudulent activities. This research examines how Data Quality (DQ), Algorithm Selection (AS), and Optimization Techniques (OT) influence Model Performance (MP) and, subsequently, Fraud Detection Effectiveness (FDE). The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 3 to analyze the relationships between these variables. The results demonstrate that high Data Quality significantly enhances Model Performance, while Algorithm Selection and Optimization Techniques also contribute positively, albeit to a lesser extent. The findings reveal that Model Performance plays a crucial mediating role between these factors and the effectiveness of fraud detection. Fraud Detection Effectiveness is found to be significantly impacted by Model Performance, suggesting that improving model accuracy and efficiency is essential for better fraud detection outcomes. Reliability and validity tests show strong internal consistency for all constructs, with Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE) all reaching satisfactory levels. The study highlights the importance of data preprocessing, the careful selection of machine learning models, and optimization techniques in achieving high-performing fraud detection systems. The results provide valuable insights for the development of more robust and scalable fraud detection mechanisms in e-payment systems, contributing to the broader field of machine learning and cybersecurity. Future research could explore advanced techniques like deep learning and blockchain integration for further enhancement of fraud detection systems.
Life Cycle Assessment of Silicon Photovoltaics and Their Environmental Impacts Aziz, Lukmanul Hakim; Callula, Brigitta; Rozi, Achmad; Madani, Muchlisina
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.941

Abstract

The rapid expansion of silicon based Photovoltaic (PV) technologies continues to drive the global shift toward sustainable energy systems. However, the environmental implications across the full life cycle of PV modules particularly those associated with upstream silicon purification routes remain insufficiently examined. This study provides a comprehensive assessment of the environmental and process level impacts of Metallurgical Grade Silicon (MGS) and Upgraded Metallurgical Grade Silicon (UMGS), covering extraction, manufacturing, operation, and end of life stages. A process oriented Life Cycle Assessment (LCA) is conducted to analyze variations in carbon intensity, hazardous material use, and energy demand, complemented by comparative evaluations of monocrystalline and polycrystalline module production pathways. To enhance analytical precision, this study incorporates an AI-assisted predictive modeling framework using supervised machine learning to estimate Global Warming Potential (GWP) and identify key factors influencing emission variability. The AI-enhanced model reveals that electricity mix and purification route exert the strongest influence on GWP, and scenario simulations demonstrate that UMGS based processes can reduce upstream emissions by up to 89% under favorable energy conditions. Additionally, the study highlights future challenges related to increasing PV waste volumes between 2025 and 2030 and the need for improved recycling infrastructures. Overall, the integration of AI-based prediction with conventional LCA offers a more dynamic and adaptive evaluation of PV sustainability performance. The findings underscore the importance of renewable powered manufacturing, early adoption of low-energy purification technologies, and policy support to achieve long-term environmental and socio-economic benefits.
Co-Authors Adiwijaya, Alfri Aziz, Lukmanul Hakim Callula, Brigitta Gunawan, Ahmad Harris, Ethan Komara, Maulana Arif Mustafa Kareem, Yasir Rizky, Agung Rozi, Achmad Susetyono, Eko Syahidun