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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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Inductive Biases in Feature Reduction for QSAR: SHAP vs. Autoencoders Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Lala, Andi; Helwani, Zuchra; 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.v3i1.306

Abstract

Machine learning models in drug discovery often depend on high-dimensional molecular descriptors, many of which may be redundant or irrelevant. Reducing these descriptors is essential for improving model performance, interpretability, and computational efficiency. This study compares two widely used reduction strategies: SHAP-based feature selection and autoencoder-based compression, within the context of Quantitative Structure-Activity Relationship (QSAR) classification. LightGBM is used as a consistent modeling framework to evaluate models trained on all descriptors, the top 50 and 100 SHAP-ranked descriptors, and a 64-dimensional autoencoder embedding. The results show that SHAP-based selection produces interpretable and stable models with minimal performance loss, particularly when using the top 100 descriptors. In contrast, the autoencoder achieves the highest test performance by capturing nonlinear patterns in a compact, low-dimensional representation, although this comes at the cost of interpretability and consistency across data splits. These findings reflect the differing inductive biases of each method. SHAP prioritizes sparsity and attribution, while autoencoders focus on reconstruction and continuity. The analysis emphasizes that descriptor reduction strategies are not interchangeable. SHAP-based selection is suitable for applications where interpretability and reliability are essential, such as in hypothesis-driven or regulatory settings. Autoencoders are more appropriate for performance-driven tasks, including virtual screening. The choice of reduction strategy should be guided not only by performance metrics but also by the specific modeling requirements and assumptions relevant to cheminformatics workflows.
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.
Pemasangan Panel Surya Sebagai Energi Alternatif di Pesantren Darul Hikmah, Kabupaten Aceh Besar Rahmawati, Cut; Muhtadin, Muhtadin; Mahyuddin, Mahyuddin; Lindawati, Lindawati; Effendy, Amalia; Noviandy, Teuku Rizky; Sufri, Rahmat; Anisah, Anisah; Faisal, Muhammad; Mutaqin, Raihan; Fatani, Muhammad; Alfharijy, Muhammad Daffa
ABDIMASKU : Jurnal Pengabdian Masyarakat UTND Vol 4 No 1 (2025): Edisi Januari 2025 - Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Tjut Nyak Dhien

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36490/jpmtnd.v4i1.1601

Abstract

This activity aims to provide students with an understanding of the importance of renewable energy usage and to raise awareness about sustainability. It took place at the Darul Hikmah Islamic Boarding School located in Geundring Village, Darul Imarah District, Aceh Besar Regency. The methodology employed included socialization, discussions, and practical training on solar panel installation. Participants comprised a team from the Faculty of Engineering at Abulyatama University and students from Darul Hikmah Islamic Boarding School.The outcomes of this initiative include the successful dissemination of information regarding the benefits of solar panels as an alternative energy source, as well as the installation of one solar-powered lamp that can provide lighting at night and enhance the quality of the boarding school’s facilities. The students gained a better understanding of renewable energy through socialization and discussions. They learned about the benefits and operation of solar panels and the significance of environmental sustainability. This activity contributed to raising the students' awareness of the need to transition to more environmentally friendly energy sources, thereby potentially stimulating further renewable energy initiatives.
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.
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.
Application of Machine Learning with XGBoost for Classifying Chemical Compound Activity as Potential Alzheimer’s Drug Candidates Muhibbul Tibri; Rahmat Sufri; Teuku Rizky Noviandy
Artificial Intelligence Systems and Its Applications Vol. 1 No. 2 (2025): Vol. 1, No. 2, December 2025
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v1i2.44

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive and memory decline, with acetylcholinesterase (AChE) as one of the most important therapeutic targets. Conventional experimental screening of AChE inhibitors is time-consuming, costly, and prone to high failure rates. Therefore, computational approaches based on machine learning are increasingly adopted to accelerate early-stage drug discovery. This study aims to classify the bioactivity of chemical compounds against AChE as potential Alzheimer’s drug candidates using the Extreme Gradient Boosting (XGBoost) algorithm. Bioactivity data were obtained from the ChEMBL database, where IC50 values were converted into pIC50 and classified into active and inactive compounds. Molecular descriptors were calculated using the Mordred library, and the dataset was divided into training and testing sets with an 80:20 ratio. Hyperparameter optimization was performed using Random Search to improve model performance. The experimental results show that the baseline XGBoost model achieved an accuracy of 84.39%, while the optimized model improved accuracy to 86.90% with an AUC of 0.9343. SHAP analysis revealed that descriptors related to electronic properties and lipophilicity, such as SssCH2, PEOE_VSA7, and SlogP_VSA, contributed most significantly to compound activity classification. These findings demonstrate that XGBoost combined with explainable AI techniques is effective for in silico identification of potential Alzheimer’s drug candidates and provides meaningful insights into relevant molecular features
PENGEMBANGAN WEB SERVICE UNTUK PENDAFTARAN DAN HASIL TES TOEFL MAHASISWA Rahmat Sufri; Ryan Setiawan; Teuku Rizky Noviandy; Anisah Anisah
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 3 No. 2 (2025): April : Jurnal Ilmiah Sains Teknologi dan Informasi
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v3i2.1175

Abstract

Sistem informasi yang terpusat akan terlalu membebani server hal ini dikarena banyaknya sistem informasi yang didistribusikan ke masing-masing fakultas atau ke masing-masing jurusan diantaranya dari berbagai macam sistem informasi tersebut adalah sistem informasi pendaftaran toefl, pada saat transaksi pendaftaran sistem ini akan berinteraksi dengan sistem informasi akademik Universitas Abulyatana untuk mencari data pokok mahasiswa yang jumlahnya pasti banyak maka pada kasus ini tentu akan membutuhkan waktu yang lama sehingga akan mengakibatkan server terbebani. Oleh karena itu untuk mengatasi masalah ini perlu dikembangkan suatu web service yang dapat memudahkan dalam komunikasi antar berbagai sistem informasi. Hasil dari penelitian ini menunjukan interaksi sistem informasi pendaftaran toefl dan sistem informasi akademik pada Universitas Abulyatama dapat berjalan dengan baik menggunakan web service. Pada penelitian ini penerapan web service dikembang dengan menggunakan Framework CodeIgniter mengunakan standar librabri yang disediakan oleh Framework sehingga dalam berinteraksi dari berbagai sistem informasi menjadi cepat dan efektif.
Fine-Tuning ChemBERTa for Predicting Activity of AXL Kinase Inhibitors in Oncogenic Target Modeling Teuku Rizky Noviandy; Ghazi Mauer Idroes; Mohsina Patwekar; Rinaldi Idroes
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.
Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM Teuku Rizky Noviandy; Khairun Nisa; Ghalieb Mutig Idroes; Irsan Hardi; Novi Reandy Sasmita
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10129

Abstract

This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.
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