Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Bulletin of Intelligent Machines and Algorithms
Nayla Nurul Azkiya
Universitas Informatika dan Bisnis Indonesia
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PREDICTION OF INHIBITOR BINDING AFFINITY AND MOLECULAR INTERACTIONS IN MPRO DENGUE USING MACHINE LEARNING Venia Restreva Danestiara; Marwondo Marwondo; Nayla Nurul Azkiya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5994

Abstract

The dengue virus experiences rapid mutation and genetic variability, posing challenges in developing effective antiviral therapies. This study explores the prediction of binding affinities between potential antiviral drug inhibitors and the NS2B-NS3 protease of the dengue virus using machine learning models. Molecular docking simulations were conducted with AutoDock Vina to generate interaction data between viral proteins and ligands. The generated datasets were used to train several machine learning models, including Random Forest Regressor (RF Regressor), Support Vector Regression (SVR), and Extreme Gradient Boosting Regressor (XGBoost Regressor). The RF Regressor model demonstrated the highest accuracy in predicting binding affinities, measured through Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient (R). However, the XGBoost Regressor and SVR models showed better generalization in practical scenarios. This study highlights the potential of machine learning to optimize the drug discovery process and provides significant insights into antiviral drug development for dengue fever.
Explainable Machine Learning For Early HIV Detection Using Extra Trees and SHAP Algorithms Anggi Dewi Nurcahyani; Ratu Dika Ratu Anisa; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 2 (2026): BIMA January 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i2.8

Abstract

Human Immunodeficiency Virus (HIV) remains a global health challenge that requires accurate and reliable early detection approaches. The use of machine learning offers potential in classifying HIV status based on clinical, demographic, and behavioral data. However, the limitations of interpretability in black-box models are an obstacle to clinical application. This study proposes an Explainable Machine Learning approach for early HIV detection by integrating the Extra Trees algorithm and the Shapley Additive exPlanations (SHAP) method. The model was developed using an HIV dataset obtained from the Kaggle platform and processed through standard data preprocessing stages without class balancing. Performance evaluation was conducted using classification metrics, confusion matrices, and learning curves to assess accuracy and learning stability. The results of the experiment show that the Extra Trees model achieved 88% accuracy with strong generalization. SHAP and mean absolute SHAP analyses revealed the dominant features that contributed to the prediction of HIV status consistently at the global and local levels. These findings show that integrating Extra Trees and SHAP produces an HIV early-detection model that is not only competitive in performance but also transparent and clinically relevant, potentially supporting the development of reliable artificial intelligence-based medical decision support systems.
Enhancing Breast Cancer Diagnosis with Ensemble Learning: Leveraging Convolutional Neural Networks and Pretrained Models through Averaged Predictions Elia Setiana; Reni Nursyanti; Nur Alamsyah; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.26

Abstract

Breast cancer continues to be a serious health issue at the global level, and early detection can significantly improve patient outcomes. This research uses imaging techniques to examine the design of an improved classification model in breast cancer detection. This project uses deep learning approaches through Convolutional Neural Networks (CNN) and ensemble learning models to potentially improve classification accuracy. To further enhance performance while controlling for class imbalance and overfitting, we leverage several models, such as ResNet18 and VGG16, with data augmentation and pre-trained models. Our methods included standard preprocessing of medical images, splitting datasets into training, testing and validation sets, and training each model with the Adam optimizer. Performance measurement included accuracy, precision, recall, and F1 score metrics. Overall, prototypes recently created displayed clear advantages based on finding results achieved through an ensemble method, which demonstrated improved model stability and reduced significant misclassification errors, and model accuracy reached 0.96. This research is crucial while developing strong deep-learning models to aid in breast cancer detection, ultimately allowing us to set a base for developing better diagnostic inference systems in medical-based applications. These systems may help improve early detection and overall patient care.
Co-Authors Anggi Dewi Nurcahyani Marwondo Nur Alamsyah Nursyanti, Reni Ratu Dika Ratu Anisa Setiana, Elia Venia Restreva Danestiara