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Maulana, Aga
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Maulana, Aga; Hardi, Irsan; Ringga, Edi Saputra; Idroes, Rinaldi
Indatu Journal of Management and Accounting Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v1i1.78

Abstract

The rise of digital transactions and electronic payment systems in modern financial management has brought convenience but also the challenge of credit card fraud. Traditional fraud detection methods are struggling to cope with the complexities of contemporary fraud strategies. This study explores the potential of machine learning, specifically the XGBoost (eXtreme Gradient Boosting) algorithm, combined with data augmentation techniques, to enhance credit card fraud detection. The research demonstrates the effectiveness of these techniques in addressing imbalanced datasets and improving fraud detection accuracy. The study showcases a balanced approach to precision and recall in fraud detection by leveraging historical transaction data and employing techniques like Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN). The implications of these findings for contemporary financial management are profound, offering the potential to bolster financial integrity, allocate resources effectively, and strengthen customer trust in the face of evolving fraud tactics.
Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Zahriah, Zahriah; Paristiowati, Maria; Emran, Talha Bin; Ilyas, Mukhlisuddin; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i2.233

Abstract

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As generative AI tools become more popular, there is growing interest in how AI can improve teaching, learning, and research. Higher education faces many challenges, such as meeting diverse learning needs and preparing students for fast-changing careers. Generative AI offers solutions by personalizing learning experiences, making education more engaging, and supporting skill development through adaptive content. It can also help researchers by automating tasks like data analysis and hypothesis generation, making research faster and more efficient. Moreover, generative AI can streamline administrative tasks, improving efficiency across institutions. However, using AI also raises concerns about privacy, bias, academic integrity, and equal access. To address these issues, institutions must establish clear ethical guidelines, ensure data security, and promote fairness in AI use. Training for faculty and AI literacy for students are essential to maximize benefits while minimizing risks. The paper suggests a strategic framework for integrating AI in higher education, focusing on infrastructure, ethical practices, and continuous learning. By adopting AI responsibly, higher education can become more inclusive, engaging, and practical, preparing students for the demands of a technology-driven world.
Artificial Intelligence in Islamic Finance: Forecasting Stock Indices with Neural Prophet Muksalmina, Muksalmina; Idroes, Ghadamfar Muflih; Maulana, Aga
Indatu Journal of Management and Accounting Vol. 2 No. 2 (2024): December 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijma.v2i2.232

Abstract

Ensuring financial system stability is paramount, especially in markets guided by Sharia principles, where investor confidence and adherence to ethical standards play critical roles. The ability to accurately forecast stock movements within this framework not only supports informed investment decisions but also strengthens the overall stability of financial markets. This research employs the innovative Neural Prophet model to predict Islamic stock indices in Indonesia with remarkable accuracy and depth. The model demonstrates its capability not only in accurately forecasting trends but also in detecting subtle fluctuations within three Islamic stock indices: the Jakarta Islamic Index (JII), the Jakarta Islamic Index 70 (JII70), and the Indonesia Sharia Stock Index (ISSI). Visual representations highlight the model's adaptability and advanced foresight, surpassing traditional models. The significance of this research lies in its potential to enhance the precision of stock index predictions, particularly for Islamic stocks, offering stakeholders deeper insights. The model's effectiveness spans both stable and volatile market conditions, making it a valuable tool for informed financial decision-making. Accurate forecasts aid in risk management and support well-informed investment decisions in fluctuating markets, thereby contributing to financial system stability.
Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Maulydia, Nur Balqis; Patwekar, Mohsina; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i2.60

Abstract

This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.
Psoriasis severity assessment: Optimizing diagnostic models with deep learning Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Prakoeswa, Cita RS.; Kairupan, Tara S.; Idroes, Ghifari M.; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 4 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i3.1512

Abstract

Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen’s Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2–93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required.
Co-Authors AA Sudharmawan, AA Abas, Abdul Hawil Abd Rahman, Sunarti Aprianto . Asep Rusyana Dharma, Dian Budi Diah, Muhammad Earlia, Nanda Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Enitan, Seyi Samson Faisal, Farassa Rani Ghazi Mauer Idroes Hardi, Irsan Hewindati, Yuni Tri Hizir Sofyan Husdayanti, Noviana Idroes, Ghadamfar Muflih Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Irvanizam, Irvanizam Kairupan, Tara S. Kemala, Pati Khairan Khairan Khairul, Mhd Khalijah Awang Kusumo, Fitranto Lala, Andi Maria Paristiowati Marwan Marwan Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Misbullah, Alim Muhammad Adam, Muhammad Muhammad Subianto Muhammad Yusuf Mukhlisuddin Ilyas Mursyida, Waliam Muslem Muslem, Muslem Musvira, Intan Niode, Nurdjannah Jane Nizamuddin Nizamuddin Noviandy, Teuku R. Nurdjannah J. Niode Nurleila, Nurleila Patwekar, Mohsina Prakoeswa, Cita RS. Rahman, Isra Farliadi Ramadeska, Siti Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Ringga, Edi Saputra Rizkia, Tatsa Sasmita, Novi Reandy Satrio, Justinus Souvia Rahimah sufriani, sufriani Sugara, Dimas Rendy Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Suryadi Suryadi Taufiq Karma Teuku Rizky Noviandy Teuku Zulfikar TRINA EKAWATI TALLEI Yandri, Erkata Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma