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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

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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.
Sosialisasi Tempat Sampah Otomatis Berbasis IoT dengan Arduino Uno Rahmawati, Cut; Anisah, Anisah; Setiawan, Ryan; Zardi, Muhammad; Noviandy, Teuku Rizky; Sufri, Rahmat; Amalia, Amalia; Herlina, Eva; Sriana, Tety; Abdullah, Rusli; Kamarudin, Syafila; Fath, Teuku Nadhif Al; Nazirah, Nurul
Jurnal Medika: Medika Vol. 5 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/51305g35

Abstract

Pengelolaan sampah yang kurang efektif serta rendahnya kesadaran masyarakat dalam membuang sampah pada tempatnya masih menjadi permasalahan lingkungan yang nyata. Menjawab tantangan tersebut, tim pengabdian masyarakat melakukan inovasi dengan merancang tempat sampah otomatis berbasis Internet of Things (IoT) menggunakan Arduino Uno. Tujuan kegiatan ini adalah meningkatkan pemahaman santri dayah mengenai pentingnya pengelolaan sampah yang modern, efisien, dan ramah lingkungan, sekaligus memperkenalkan teknologi sederhana yang dapat diaplikasikan dalam kehidupan sehari-hari. Metode pelaksanaan meliputi sosialisasi yang dilakukan di Dayah Masyraf, Gampong Keuneu Eu, Kecamatan Peukan Bada, Kabupaten Aceh Besar. Selanjutnya dilakukan demonstrasi prototipe serta pendampingan kepada santri dalam memahami cara kerja dan manfaat teknologi IoT pada sistem pengelolaan sampah. Perangkat yang digunakan adalah arduino IDE, sensor, servo, modul IoT, breadboard, kabel jumper, dan power supply. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan dan antusiasme santri terhadap penerapan teknologi dalam mendukung kebersihan lingkungan. Dengan demikian, kegiatan ini diharapkan dapat mendorong terciptanya budaya membuang sampah pada tempatnya serta membuka peluang pengembangan inovasi serupa di masa depan.
QSAR Modeling of Beta-2 Adrenergic Receptor Ligands Using Molecular Descriptor–Based Machine Learning Noviandy, Teuku Rizky; Patwekar, Mohsina; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v4i1.394

Abstract

The Beta-2 Adrenergic Receptor (ADRB2) is a well-characterized G protein–coupled receptor widely studied in pharmacology and drug discovery. In this study, quantitative structure–activity relationship (QSAR) models were developed using molecular descriptor–based machine learning approaches to predict the activity of ADRB2 ligands. A curated dataset of 745 compounds with experimentally determined IC₅₀ values was obtained from the ChEMBL database. Two-dimensional molecular descriptors were calculated and preprocessed to remove low-variance and highly correlated features, resulting in a refined feature set for model development. The dataset was categorized into active and inactive compounds and divided into training and testing subsets. Four machine learning algorithms. Logistic Regression, Support Vector Machine, Gradient Boosting, and Random Forest were implemented and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Among the models, Random Forest achieved the best performance, with an accuracy of 89.26%, F1-score of 89.87%, and AUC of 0.926, followed by Gradient Boosting with an accuracy of 87.92% and AUC of 0.922. Analysis of physicochemical descriptors indicated that hydrogen-bond donor capacity (nHD) shows a statistically significant association with variations in compound activity toward ADRB2, while lipophilicity (LogP) and hydrogen-bond acceptor count (nHA) do not exhibit statistically significant differences between activity classes. Overall, the results demonstrate that molecular descriptor–based machine learning models, particularly ensemble methods, provide an effective framework for predicting ADRB2-related compound activity and support the prioritization of candidate molecules in computational drug discovery.
Comparative Analysis of Ensemble Machine Learning Models for QSAR-Based Prediction of Anticoagulant Activity in Thrombotic Disorders Noviandy, Teuku Rizky; Sufri, Rahmat; Setiawan, Ryan; Anisah, Anisah
Heca Journal of Applied Sciences Vol. 4 No. 1 (2026): March 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v4i1.393

Abstract

Thrombotic disorders remain a major cause of global morbidity and mortality, with dysregulation of blood coagulation pathways playing a central role in disease progression. In particular, Thrombin is a key therapeutic target for anticoagulant drug development, making accurate prediction of inhibitory activity highly relevant for accelerating discovery efforts. Despite advances in computational drug discovery, there is still a need for systematic evaluation of machine learning approaches for QSAR-based prediction of anticoagulant activity. Many existing studies focus on single models or lack consistent comparison frameworks, limiting insights into the relative performance of different ensemble techniques. To address this gap, this study explores the application of multiple ensemble machine learning methods, including Random Forest, XGBoost, Gradient Boosting, and Extra Trees, combined with hyperparameter optimization using random search. The main objective of this work is to conduct a comparative analysis of these ensemble models to predict pIC50 values for thrombin inhibitors using molecular descriptors derived from chemical structures. The results show that the Extra Trees model achieved the best overall performance, with an R2 of 0.697, RMSE of 0.851, and MAE of 0.615 after tuning. Additionally, Gradient Boosting and XGBoost demonstrated significant improvement following hyperparameter optimization, highlighting the importance of model tuning in QSAR tasks. Overall, the study confirms that ensemble learning methods yield reliable, accurate predictions of anticoagulant activity, with Extra Trees emerging as the most effective approach for this dataset.
Co-Authors Abas, Abdul Hawil Abd Rahman, Sunarti Abdullah, Rusli Abrar , Tajul Adi Purnawarman, Adi Afidh, Razief Perucha Fauzie Afjal, Mohd Ahmad Watsiq Maula Ahmad, Noor Atinah Ahsya, Yahdina Alfharijy, Muhammad Daffa Amalia Amalia Amalina, Faizah Amirah, Kelsy Amri Amin Anisah Aprianto . Apriliansyah, Feby Asep Rusyana Azhar, Fauzul Azzuhry , Haikal Baehaqi 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 Erkata Yandri Essy Harnelly Eva Herlina Faisal, Farassa Rani Fajri, Irfan Fatani, Muhammad Fath, Teuku Nadhif Al 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 Kamarudin, Syafila Khairan Khairan Khairul, Mhd Khairul, Moh Khairun Nisa Kruba, Rumaisa Kurniadinur, Kurniadinur Kusumo, Fitranto Lala, Andi Lindawati Lindawati 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 Nazirah, Nurul 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 Tety Sriana Teuku Zulfikar TRINA EKAWATI TALLEI Utami, Resty Tamara Zahriah, Zahriah Zardi, Muhammad Zhilalmuhana, Teuku Zuchra Helwani, Zuchra Zulkarnain Jalil Zurnila Marli Kesuma