Article Per Year (5 Year)
p-Index From 2021 - 2026
6.749
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Masyarakat Tjut Nyak Dhien 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
Teuku Rizky Noviandy
Interdisciplinary Innovation Research Unit, Graha Primera Saintifika, Aceh Besar 23771, 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 Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

Published : 54 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 54 Documents
Search

 2   3   4   5   6 
Interpretable Machine Learning QSAR Models for Classification and Screening of VEGFR-2 Inhibitors in Anticancer Drug Discovery Noviandy, Teuku Rizky; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 3 No. 2 (2025): September 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v3i2.339

Abstract

Cancer remains a major global health burden, with angiogenesis playing a central role in tumor growth and progression. Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a key mediator of angiogenesis and an attractive therapeutic target, but existing inhibitors are limited by reduced efficacy, toxicity, and resistance, creating a need for more effective predictive models in drug discovery. In this study, an interpretable machine learning based QSAR approach was developed using a curated dataset of 10,221 VEGFR-2 inhibitors from ChEMBL represented by 164 molecular descriptors. Four algorithms, kNN, AdaBoost, Random Forest, and XGBoost, were compared, and XGBoost achieved the best results with an accuracy of 83.67 percent, sensitivity of 91.38 percent, specificity of 71.73 percent, F1-score of 87.17 percent, and AUC of 0.9009. Model interpretation with LIME identified molecular descriptors related to hydrogen bonding, electrostatics, and lipophilicity as key contributors to activity. These results indicate that interpretable ensemble models can combine strong predictive performance with mechanistic insights, supporting rational design and optimization of novel VEGFR-2 inhibitors for anticancer therapy.
Fine-Tuning ChemBERTa for Predicting Activity of AXL Kinase Inhibitors in Oncogenic Target Modeling Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Patwekar, Mohsina; Idroes, Rinaldi
Grimsa Journal of Science Engineering and Technology Vol. 3 No. 2 (2025): October 2025
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v3i2.98

Abstract

The development of selective kinase inhibitors remains a key objective in cancer drug discovery, where predictive computational models can significantly accelerate the identification of leads. In this study, we investigate the fine-tuning strategies of the transformer-based ChemBERTa model for quantitative structure–activity relationship (QSAR) modeling of AXL receptor tyrosine kinase inhibitors, an important therapeutic target implicated in tumor progression and metastasis. A dataset of AXL inhibitors was curated from the ChEMBL database. Three fine-tuning configurations, namely baseline, full fine-tune, and aggressive, were implemented to examine the influence of learning rate, weight decay, and the number of frozen transformer layers on model performance. Models were evaluated using accuracy, precision, recall, F1-score, and calibration metrics. Results showed that both the full fine-tune and aggressive configurations outperformed the baseline model, achieving higher precision and F1-scores while maintaining robust recall. The aggressive configuration achieved the most balanced performance, with improved calibration and the lowest expected calibration error, indicating reliable probabilistic predictions. Overall, this study highlights that controlled fine-tuning of ChemBERTa significantly enhances predictive performance and confidence estimation in QSAR modeling, offering valuable insights for optimizing transformer-based chemical language models in kinase-targeted drug discovery.
The Role of Study Habits, Parental Involvement, and School Environment in Predicting Student Achievement: A Machine Learning Perspective Noviandy, Teuku Rizky; Paristiowati, Maria; Isa, Illyas Md; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i2.350

Abstract

This study explores the application of machine learning techniques to predict student achievement based on study habits, parental involvement, and school environment. Using a dataset from Kaggle comprising academic, behavioral, and contextual variables, four machine learning algorithms, namely K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Random Forest, were implemented and evaluated. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curve, and Precision–Recall curves. Results show that all models effectively classified students into low- and high-achievement categories, with SVM achieving the highest accuracy (94.02%) and the strongest overall performance. The findings highlight the potential of machine learning-driven predictive analytics in educational settings, enabling early identification of at-risk students and supporting evidence-based interventions. By integrating diverse factors influencing academic performance, this study demonstrates how data-driven approaches can enhance educational management, inform policy, and promote equitable learning outcomes.
An Interpretable Machine Learning Framework for Predicting Advanced Tumor Stages Noviandy, Teuku Rizky; Patwekar, Mohsina; Patwekar, Faheem; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i2.364

Abstract

Accurate identification of advanced tumor stages is essential for timely clinical decision-making and personalized treatment planning. This study proposes an explainable ensemble learning framework for predicting advanced tumor stage using a dataset containing 10,000 samples with 18 clinical and radiological features. Four machine learning models, namely Logistic Regression, Naïve Bayes, AdaBoost, and LightGBM, were evaluated using stratified train–test splits along with standard performance metrics. LightGBM achieved the highest performance, with an accuracy of 86.05% and an F1-score of 76.61%, outperforming linear and probabilistic classifiers. ROC–AUC and precision–recall analyses further confirmed the superior discriminative ability of ensemble methods. SHAP explainability techniques highlighted mitotic count, Ki-67 index, enhancement, and necrosis as the most influential predictors of advanced stage. The proposed framework demonstrates strong predictive capability and provides clinically interpretable insights, underscoring its potential as a decision-support tool in oncological diagnostics. Future work will involve external validation and integration of additional multimodal data to enhance generalizability.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Abrar , Tajul Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Afjal, Mohd Ahmad Watsiq Maula Ahmad, Noor Atinah Ahsya, Yahdina Alfharijy, Muhammad Daffa Amalina, Faizah Amirah, Kelsy Amri Amin Anisah Aprianto . Apriliansyah, Feby Asep Rusyana Azhar, Fauzul Azzuhry , Haikal Bahri, Ridzky Aulia BAKRI, TEDY KURNIAWAN Dahlawy, Arriz Dharma, Aditia Dian Handayani Dian Lestari, Nova Dimas Chaerul Ekty Saputra Earlia, Nanda Effendy, Amalia Eko Suhartono El-Shazly, Mohamed Emran, Talha Bin Enitan, Seyi Samson Essy Harnelly Faisal, Farassa Rani Fajri, Irfan Fatani, Muhammad Fauzi, Fazlin Mohd Furqan, Nurul Ghazi Mauer Idroes Hafizah, Iffah Hardia, Natasha Athira Keisha Hewindati, Yuni Tri Hidayatullah, Ferdy Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Idroes, Ghifari Maulana Iin Shabrina Hilal Imelda, Eva Imran Imran Irma Sari Irsan Hardi Irvanizam, Irvanizam Isa, Illyas Md Isra Firmansyah, Isra Kadri, Mirzatul Khairan Khairan Khairul, Mhd Khairul, Moh Khairun Nisa Kruba, Rumaisa Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lindawati Lindawati Mahyuddin Mahyuddin Maimun Syukri, Maimun Mardalena, Selvi Maria Paristiowati Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Misbullah, Alim Mohamed Yusof, Nur Intan Saidaah Mohd Fauzi, Fazlin Muhammad Adam, Muhammad Muhammad Faisal Muhammad Subianto Muhammad Yanis Muhammad Yusuf Muhtadin Muhtadin Mukhlisuddin Ilyas Muksalmina Muksalmina Muliadi Mursyida, Waliam Muslem Muslem, Muslem Mutaqin, Raihan Nainggolan, Sarah Ika Niode, Nurdjannah Jane Nizamuddin Nizamuddin Nurleila, Nurleila Patwekar, Faheem Patwekar, Mohsina Rahmawati, Cut Raihan Raihan, Raihan Ramadeska, Siti Raudhatul Jannah Ray, Samrat Razief Perucha Fauzie Afidh Rinaldi Idroes Ringga, Edi Saputra Rizkia, Tatsa 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 Syahyana, Ahmad Taufiq Karma Teuku Zulfikar TRINA EKAWATI TALLEI Utami, Resty Tamara Yandri, Erkata Zahriah, Zahriah Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma