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All Journal Narra J Jurnal Pengabdian Masyarakat Tjut Nyak Dhien JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Narra X Journal of Computing Theories and Applications Heca Journal of Applied Sciences Malacca Pharmaceutics Leuser Journal of Environmental Studies Ekonomikalia Journal of Economics Journal of Educational Management and Learning Infolitika Journal of Data Science Indonesian Journal of Case Reports Indatu Journal of Management and Accounting Grimsa Journal of Science Engineering and Technology The International Journal of Financial Systems Jurnal Medika: Medika Grimsa Journal of Business and Economics Studies Journal of Future Artificial Intelligence and Technologies Artificial Intelligence Systems and Its Applications (AISA)
Teuku Rizky Noviandy
Department Of Information Systems, Faculty Of Engineering, Universitas Abulyatama, Aceh Besar 23372, Indonesia
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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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.
Prognostication of differentiated thyroid cancer recurrence: An explainable machine learning approach Idroes, Ghazi M.; Noviandy, Teuku R.; Idroes , Ghalieb M.; Hardi, Irsan; Duta, Teuku F.; Hamoud, Lama MA.; Al-Gunaid , Hala T.
Narra X Vol. 2 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v2i3.183

Abstract

Differentiated thyroid cancer (DTC) generally has a favorable prognosis, but recurrence remains a concern for a subset of patients, highlighting the need for accurate predictive tools. While traditional methods, such as the American Thyroid Association (ATA) guidelines, are widely used, they may not fully capture the complex patterns in clinical data. To address this, we developed a machine learning model using LightGBM and enhanced its interpretability with SHAP (SHapley Additive exPlanations). Our model, trained on data from 383 DTC patients, identified response to initial therapy as the most significant predictor of recurrence, alongside age and risk level. The model achieved an accuracy of 93.51%, with precision and sensitivity of 94.23% and 96.08%, respectively, using only five key features selected through Recursive Feature Elimination (RFE). SHAP analysis provided clear insights into how these features influenced predictions, offering a transparent and interpretable approach to risk stratification. These results highlight the potential of explainable machine learning to improve recurrence prediction, support personalized care, and build clinician trust, while laying the groundwork for further validation in diverse populations.
Forecasting Bank Stock Trends Using Artificial Intelligence: A Deep Dive into the Neural Prophet Approach Noviandy, Teuku Rizky; Hardi, Irsan; Idroes, Ghalieb Mutig
The International Journal of Financial Systems Vol. 2 No. 1 (2024)
Publisher : Otoritas Jasa Keuangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61459/ijfs.v2i1.41

Abstract

This research aims to use Neural Prophet, a deep learning tool, to predict stock prices in the banking sector with high accuracy and useful insights. The model's capability in managing intricate temporal patterns differentiates it, garnering attention from researchers. The significance of this research lies in its potential to enhance stock price prediction precision, especially in the context of banking stocks, offering stakeholders’ deeper insights. The model's efficacy spans stable and volatile market behaviours, making it a valuable tool for informed decision-making in finance. Accurate predictions benefit risk management, facilitating well-informed investment choices in dynamic markets.
Long-Term Impact of Dirty and Clean Energy on Indonesia’s Economic Growth: Before and During the COVID-19 Pandemic Ringga, Edi Saputra; Hafizah, Iffah; Idroes, Ghifari Maulana; Amalina, Faizah; Kadri, Mirzatul; Idroes, Ghalieb Mutig; Noviandy, Teuku Rizky; Hardi, Irsan
Grimsa Journal of Business and Economics Studies Vol. 2 No. 1 (2025): January 2025
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjbes.v2i1.49

Abstract

Dirty (non-renewable) energy, considered environmentally harmful due to greenhouse gas emissions, is contrasted with clean (renewable) energy, which is believed to have positive ecological impacts that can boost economic growth in the long term. This study analyzes the long-term effects of electricity generation from both dirty and clean energy sources on economic growth in Indonesia, using data from two periods: before the COVID-19 pandemic (2000–2019) and the full period including the COVID-19 pandemic (2000–2022). Empirical findings from Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) methods reveal that dirty energy significantly impacts long-term economic growth in both periods, while clean energy does not have a substantial effect. A robustness check conducted using the Canonical Cointegrating Regression (CCR) method confirms that dirty energy continues to play a crucial role in Indonesia's long-term economic growth. A key finding is that the positive impact of dirty energy generation on economic growth was stronger in the full period including the COVID-19 pandemic compared to before. This suggests that dirty energy contributed more to economic growth during the pandemic. The study recommends a balanced approach to economic growth by prioritizing the transition to clean energy while recognizing the importance of dirty energy in Indonesia's economy. This transition should be gradual, using the current role of dirty energy to support economic development while investing in clean energy alternatives for sustainable growth.
Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma Noviandy, Teuku Rizky; Imelda, Eva; Idroes, Ghazi Mauer; Suhendra, Rivansyah; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i1.271

Abstract

Glaucoma is a leading cause of irreversible blindness, primarily managed by lowering intraocular pressure (IOP). Carbonic Anhydrase-II (CA-II) inhibitors play a crucial role in this treatment by reducing aqueous humor production. However, existing CA-II inhibitors often suffer from poor selectivity, side effects, and limited bioavailability, highlighting the need for more efficient and targeted drug discovery approaches. This study uses machine learning-driven Quantitative Structure-Activity Relationship (QSAR) modeling to predict CA-II inhibition based on molecular descriptors, significantly enhancing screening efficiency over traditional experimental methods. By evaluating multiple machine learning models, including Support Vector Machine, Gradient Boosting, and Random Forest, we identify SVM as the most effective classifier, achieving the highest accuracy (83.70%) and F1-score (89.36%). Class imbalance remains challenging despite high sensitivity, necessitating further improvements through resampling and hyperparameter optimization. Our findings underscore the potential of machine learning-based virtual screening in accelerating CA-II inhibitor identification and advocate for integrating AI-driven approaches with traditional drug discovery techniques. Future directions include deep learning enhancements and hybrid machine learning-docking frameworks to improve prediction accuracy and facilitate the development of more potent and selective glaucoma treatments.
Prognostication of differentiated thyroid cancer recurrence: An explainable machine learning approach Idroes, Ghazi M.; Noviandy, Teuku R.; Idroes , Ghalieb M.; Hardi, Irsan; Duta, Teuku F.; Hamoud, Lama MA.; Al-Gunaid , Hala T.
Narra X Vol. 2 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narrax.v2i3.183

Abstract

Differentiated thyroid cancer (DTC) generally has a favorable prognosis, but recurrence remains a concern for a subset of patients, highlighting the need for accurate predictive tools. While traditional methods, such as the American Thyroid Association (ATA) guidelines, are widely used, they may not fully capture the complex patterns in clinical data. To address this, we developed a machine learning model using LightGBM and enhanced its interpretability with SHAP (SHapley Additive exPlanations). Our model, trained on data from 383 DTC patients, identified response to initial therapy as the most significant predictor of recurrence, alongside age and risk level. The model achieved an accuracy of 93.51%, with precision and sensitivity of 94.23% and 96.08%, respectively, using only five key features selected through Recursive Feature Elimination (RFE). SHAP analysis provided clear insights into how these features influenced predictions, offering a transparent and interpretable approach to risk stratification. These results highlight the potential of explainable machine learning to improve recurrence prediction, support personalized care, and build clinician trust, while laying the groundwork for further validation in diverse populations.
Predicting AXL Tyrosine Kinase Inhibitor Potency Using Machine Learning with Interpretable Insights for Cancer Drug Discovery Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Harnelly, Essy; Sari, Irma; Fauzi, Fazlin Mohd; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 3 No. 1 (2025): March 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v3i1.270

Abstract

AXL tyrosine kinase plays a critical role in cancer progression, metastasis, and therapy resistance, making it a promising target for therapeutic intervention. However, traditional drug discovery methods for developing AXL inhibitors are resource-intensive, time-consuming, and often fail to provide detailed insights into molecular determinants of potency. To address this gap, we applied machine learning techniques, including Random Forest, Gradient Boosting, Support Vector Regression, and Decision Tree models, to predict the potency (pIC50) of AXL inhibitors using a dataset of 972 compounds with 550 molecular descriptors. Our results demonstrate that the Random Forest model outperformed others with an R² of 0.703, MAE of 0.553, RMSE of 0.720, and PCC of 0.841, showcasing strong predictive accuracy. SHAP analysis identified critical molecular features, such as RNCG and TopoPSA(NO), as key contributors to inhibitor potency, providing interpretable insights into structure-activity relationships. These findings highlight the potential of machine learning to accelerate the identification and optimization of AXL inhibitors, bridging the gap between computational predictions and rational drug design and paving the way for effective cancer therapeutics.
An Explainable Machine Learning Study of Behavioral and Psychological Determinants of Depression in the Academic Environment Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Ringga, Edi Saputra; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i1.304

Abstract

Depression is a significant and growing concern within academic environments, affecting both students and staff due to factors such as academic pressure, financial stress, and lifestyle challenges. This study explores the use of machine learning, specifically a Random Forest classifier, to predict depression risk among students using behavioral, psychological, and demographic data. A dataset of 27,788 student records was analyzed after thorough preprocessing and exploratory data analysis. The model achieved strong performance, with an accuracy of 83.52% and an AUC of 0.91, indicating reliable classification of depression status. Local Interpretable Model-agnostic Explanations (LIME) were employed to enhance interpretability, revealing key predictive features such as suicidal ideation, academic pressure, sleep duration, and dietary habits. These interpretable insights align with existing psychological research and provide actionable information for mental health professionals. The findings highlight the value of explainable AI in educational settings, offering a scalable and transparent approach to early depression detection and intervention. Future work should focus on longitudinal data integration, multimodal inputs, and real-world implementation to strengthen the model’s utility and impact.
Techniques and Tools in Learning Analytics and Educational Data Mining: A Review Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Paristiowati, Maria; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i1.308

Abstract

Learning analytics and educational data mining are rapidly evolving fields that leverage data-driven methods to enhance teaching, learning, and institutional decision-making. This review provides a comprehensive overview of the key analytical techniques and tools employed in learning analytics and educational data mining, including classification, clustering, regression, association rule mining, and data visualization. It also highlights the integration of advanced methods such as deep learning and adaptive systems for personalized education. The paper examines various platforms and technologies, including learning management systems, open-source tools, and AI/ML libraries, to evaluate their capabilities, scalability, and practical adoption. Key application areas, such as dropout prediction, engagement analysis, personalized learning, and curriculum design, are examined through selected case studies spanning K–12 and higher education. The review emphasizes the growing importance of ethical considerations, interpretability, and usability in the application of educational analytics. By synthesizing current practices and trends, this work aims to inform educators, researchers, and developers seeking to harness educational data for improved learning outcomes and strategic planning.
Explainable Deep Learning with Lightweight CNNs for Tuberculosis Classification Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Zulfikar, Teuku; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.305

Abstract

Tuberculosis (TB) remains a major global health threat, particularly in low-resource settings where timely diagnosis is critical yet often limited by the lack of radiological expertise. Chest X-rays (CXRs) are widely used for TB screening, but manual interpretation is prone to errors and variability. While deep learning has shown promise in automating CXR analysis, most existing models are computationally intensive and lack interpretability, limiting their deployment in real-world clinical environments. To address this gap, we evaluated three lightweight and explainable CNN architectures, ShuffleNetV2, SqueezeNet 1.1, and MobileNetV3, for binary TB classification using a locally sourced dataset of 3,008 CXR images. Using transfer learning and Grad-CAM for visual explanation, we show that MobileNetV3 and ShuffleNetV2 achieved perfect test performance with 100% accuracy, sensitivity, specificity, precision, and F1-score, along with AUC scores of 1.00 and inference times of 94.66 and 103.63 seconds, respectively. SqueezeNet performed moderately, with a lower F1-score of 82.98% and several misclassifications. These results demonstrate that lightweight CNNs can deliver high diagnostic accuracy and transparency, supporting their use in scalable, AI-assisted TB screening systems for underserved healthcare settings.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Afjal, Mohd Ahmad Watsiq Maula Ahmad, Khairunnas Ahmad, Noor Atinah Ahsya, Yahdina Al-Gunaid , Hala T. Alfharijy, Muhammad Daffa Amalia Amalia Amalina, Faizah Amirah, Kelsy Amri Amin Anisah Anisah Anisah Anisah Anisah Anisah Anisah Aprianto . Apriliansyah, Feby Asep Rusyana Azhar, Fauzul Baehaqi Bahri, Ridzky Aulia BAKRI, TEDY KURNIAWAN Cut Rahmawati Cut Rahmawati Dahlawy, Arriz Dharma, Aditia Dian Handayani Dian Lestari, Nova Dimas Chaerul Ekty Saputra Duta, Teuku F. Earlia, Nanda Effendy, Amalia Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Enitan, Seyi Samson Erkata Yandri Essy Harnelly Eva Herlina Faisal, Farassa Rani Fajri, Irfan Fatani, Muhammad Fauzi, Fazlin Mohd Furqan, Nurul Ghalieb Mutig Idroes Ghazi Mauer Idroes Hafizah, Iffah Haikal Azzuhry Hamoud, Lama MA. Hardia, Natasha Athira Keisha Hewindati, Yuni Tri Hidayatullah, Ferdy Hizir Sofyan Hizir Sofyan Husdayanti, Noviana Idroes , Ghalieb M. Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. Idroes, Ghifari Maulana Iin Shabrina Hilal Imelda, Eva Imran Imran Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Isra Firmansyah, Isra Kadri, Mirzatul Kairupan, Tara S. Kemala, Pati Khairan Khairan Khairul, Mhd Khairul, Moh Khairun Nisa Kruba, Rumaisa Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lindawati Lindawati Lubis, Vanizra F. Maimun Syukri, Maimun Mardalena, Selvi Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Misbullah, Alim Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Mohsina Patwekar Muhammad Adam, Muhammad Muhammad Faisal Muhammad Subianto Muhammad Yanis Muhammad Yusuf Muhammad Zardi Muhibbul Tibri Muhtadin Muhtadin Mukhlisuddin Ilyas Muksalmina Muksalmina Muliadi Muliadi Ramli Mursyida, Waliam Muslem Muslem Mutaqin, Raihan Nainggolan, Sarah Ika Nizamuddin Nizamuddin Novi Reandy Sasmita Nurdjannah J. Niode Nurleila, Nurleila Nurul Nazirah Patwekar, Faheem Patwekar, Mohsina Prakoeswa, Cita RS. Rahmat Sufri Rahmat Sufri Rahmat Sufri Rahmat Sufri Rahmawati, Cut Raihan Raihan, Raihan Ramadeska, Siti Raudhatul Jannah Ray, Samrat Razief Perucha Fauzie Afidh Rinaldi Idroes Ringga, Edi Saputra Rizkia, Tatsa Rusli Abdullah Ryan Setiawan Ryan Setiawan Ryan Setiawan Safhadi, Aulia Al-Jihad Sasmita, Novi Reandy Satrio, Justinus Sofyan, Rahmi Solly Aryza Souvia Rahimah Sufri, Rahmat sufriani, sufriani Sugara, Dimas Rendy Suhendra , Rivansyah Suhendra, Rivansyah Suhendrayatna Suhendrayatna Suryadi Suryadi Syafila Kamarudin Syahyana, Ahmad Tajul Abrar Taufiq Karma Tety Sriana Teuku Nadhif Al Fath Teuku Zulfikar TRINA EKAWATI TALLEI Utami, Resty Tamara Yosza Dasril Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma