Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences Indatu Journal of Management and Accounting
Hidayatullah, Ferdy
Unknown Affiliation
Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimizing renewable energy potential in island areas based on analytical hierarchy process Hasibuan, Arnawan; Sayuti, Muhammad; Siregar, Widyana Verawaty; Hidayatullah, Ferdy; Daud, Muhammad; Azmi, Azmi; Azman, Azman; Almunadiansyah, Rizky; Roid, Fahrian
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp218-226

Abstract

The decreasing availability of fossil fuels and their contribution to environmental degradation and emissions of harmful gases has encouraged a shift to renewable energy sources. This change aims to produce electricity without using fossil fuels. Banyak Islands, a new tourist destination in Aceh Singkil, Indonesia, is experiencing an increase in visitors, increasing electricity demand currently supplied by diesel power plants. Therefore, this research aims to examine the possibilities of wind and solar power plants and assess their energy output. The four factors used to determine the potential of renewable energy are technology, economics, environment, and social politics, with 13 sub-criteria. Analytical hierarchy process techniques prioritize potential alternative energy sources such as wind and solar power facilities. Research findings show that environmental criteria have a higher value (0.3455) than financial criteria (0.2861), next is technological criteria (0.1734), then socio-political criteria (0.1603). Based on the results of the requirements and sub-requirements, wind power generation is the most effective and efficient alternative energy used in the Banyak Islands region (0.549), followed by solar power generation (0.451). This data shows that the area is prone to medium to high wind speeds, specifically in geographic locations facing the Indian Ocean which is located offshore.
Credit Card Fraud Detection Through Explainable Artificial Intelligence for Managerial Oversight Muksalmina, Muksalmina; Syahyana, Ahmad; Hidayatullah, Ferdy; Idroes, Ghalieb Mutig; Noviandy, Teuku Rizky
Indatu Journal of Management and Accounting Vol. 3 No. 1 (2025): June 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v3i1.301

Abstract

As digital payment systems grow in volume and complexity, credit card fraud continues to be a significant threat to financial institutions. While machine learning (ML) has emerged as a powerful tool for detecting fraudulent activity, its adoption in managerial settings is hindered by a lack of transparency and interpretability. This study examines how explainable artificial intelligence (XAI) can enhance managerial oversight in the deployment of ML based fraud detection systems. Using a publicly available, simulated dataset of credit card transactions, we developed and evaluated four ML models: Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest. Performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score. The Random Forest model demonstrated superior classification performance but also presented significant interpretability challenges due to its complexity. To fill this gap, we applied SHAP (SHapley Additive exPlanations), a leading method for explaining the outputs of the Random Forest model. SHAP analysis revealed that transaction amount and merchant category were the most influential features in determining the risk of fraud. SHAP plots were used to make these insights accessible to non-technical stakeholders. The findings underscore the importance of XAI in promoting transparency, facilitating regulatory compliance, and fostering trust in AI-driven decisions. This study offers practical guidance for managers, auditors, and policymakers seeking to integrate explainable ML tools into financial risk management processes, ensuring that technological advancements are balanced with accountability and informed human oversight.
Co-Authors Arnawan Hasibuan Azman Azman AZMI, AZMI Idroes, Ghalieb Mutig M Sayuti Muhammad Daud Muksalmina Muksalmina Rizky Almunadiansyah Roid, Fahrian Siregar, Widyana Verawaty Syahyana, Ahmad Teuku Rizky Noviandy