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All Journal Jurnal Riset Informatika
Ilham Maulana
Universitas Nusa Mandiri
Computer Science & IT

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Prediction Of Flight Delays Using Feature Engineering, Catboost, And Bayesian Optimization To Improve Model Performance Ilham Maulana; Siti Ernawati; Risa Wati
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.346

Abstract

Flight delays have become a major issue in the aviation industry, impacting operational efficiency and customer satisfaction. This study proposes a CatBoostClassifier-based approach combined with Feature Engineering, Bayesian Optimization, and Random Over Sampling techniques to improve the accuracy of flight delay predictions. Based on model evaluation results, the use of Feature Engineering and Bayesian Optimization enhances performance compared to the baseline CatBoost model. The CatBoost+FE+Bayes combination achieves an accuracy of 83.32%, higher than the unmodified CatBoost model, which only reaches 82.95%. However, applying the Random Over Sampling technique in the CatBoost+FE+Bayes+ROS combination decreases model performance, reducing accuracy to 81.44%. Regarding other metrics, the CatBoost+FE+Bayes model demonstrates the highest F1-score of 0.62, indicating a balance between precision and recall. Additionally, the Area Under Curve (AUC) analysis reveals that CatBoost+FE+Bayes has the highest AUC value of 0.7793, followed by CatBoost+FE at 0.7768, and the unmodified CatBoost model at 0.7643. Meanwhile, the application of ROS leads to a decrease in AUC value to 0.6787. These findings suggest that utilizing Feature Engineering and Bayesian Optimization significantly enhances flight delay predictions. However, resampling techniques such as ROS do not always positively impact the tested model and can even degrade classification performance. The objective of this research is to develop a more accurate flight delay prediction model through the application of appropriate optimization techniques. The resulting model is expected to improve prediction quality and benefit the aviation industry by optimizing operational efficiency and minimizing the negative impact of delays on passengers.
IMPROVING IMAGE CLASSIFICATION ACCURACY WITH OVERSAMPLING AND DATA AUGMENTATION USING DEEP LEARNING: A CASE STUDY ON THE SIMPSONS CHARACTERS DATASET Ilham Maulana; Siti Ernawati; Muhammad Indra
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.348

Abstract

The issue of data imbalance in image classification often hinders deep learning models from making accurate predictions, especially for minority classes. This study introduces AugOS-CNN (Augmentation and Over Sampling with CNN), a novel approach that combines oversampling and data augmentation techniques to address data imbalance. The The Simpsons Characters dataset is used in this study, featuring five main character classes: Bart, Homer, Agnes, Carl, and Apu. The number of samples in each class is balanced to 2,067 using an augmentation method based on Augmentor. The proposed model integrates oversampling and augmentation steps with a Convolutional Neural Network (CNN) architecture to improve classification accuracy. Evaluation results show that the AugOS-CNN model achieves the highest accuracy of 96%, outperforming the baseline CNN approach without data balancing techniques, which only reaches 91%. These findings demonstrate that the AugOS-CNN model effectively enhances image classification performance on datasets with imbalanced class distributions, contributing to the development of more robust deep learning methods for addressing data imbalance issues.
Explainable AI-Driven TabNet Model Enhanced with Bayesian Optimization for Lung Cancer Prediction and Interpretation Ilham Maulana
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.354

Abstract

This study aims to develop an accurate and explainable lung cancer risk prediction model using a TabNet approach optimized with Bayesian Optimization and applying Explainable AI (XAI) methods through LIME (Local Interpretable Model-Agnostic Explanations). TabNet was selected for its efficiency in processing tabular data and its ability to produce high-accuracy predictions. In the initial stage, the TabNet model was tested using a dataset that was preprocessed through standardization and split into training and testing sets. The performance evaluation of the model without optimization showed an accuracy of 95.83%, precision of 95.87%, recall of 95.76%, and F1-Score of 95.81%. Subsequently, Bayesian Optimization was applied using the Optuna library to find the best hyperparameter combination for the TabNet model. The optimization results demonstrated a significant improvement, achieving an accuracy of 98.33%, precision of 98.48%, recall of 98.21%, and F1-Score of 98.32%. After optimizing the TabNet model, LIME was implemented to provide interpretability for the generated predictions. LIME was used to identify the most influential features contributing to the predictions, enhancing the model's transparency in the lung cancer risk prediction process. Through the combination of TabNet, Bayesian Optimization, and Explainable AI, this study successfully developed a lung cancer prediction model that is not only accurate but also highly interpretable. This model can assist medical professionals in identifying key risk factors and providing transparent explanations for each prediction made.
ARRHYTHMIA DETECTION FROM IMBALANCED ECG DATA USING CNN, FOCAL LOSS, AND RR INTERVAL FEATURES WITH INTER-PATIENT EVALUATION Ilham Maulana; Nita Merlina
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.514

Abstract

Detection of arrhythmias from electrocardiogram (ECG) signals is crucial for early diagnosis of heart rhythm disorders. However, the imbalanced distribution of arrhythmia classes poses challenges for classification models. This study proposes a Convolutional Neural Network (CNN) model combined with Focal Loss and RR features to address class imbalance using an inter-patient evaluation approach. The dataset includes three classes: N (Normal), SVEB (Supraventricular Ectopic Beats), and VEB (Ventricular Ectopic Beats), divided into training (DS1), validation (DS1-Val), and testing (DS2) sets. The proposed CNN + Focal Loss + RR model achieved an accuracy of 95.75% and an F1-score of 95.19%. Additionally, Explainable AI (XAI) using LIME is applied to improve model interpretability by identifying important features influencing predictions. The results demonstrate that the proposed approach provides accurate and interpretable arrhythmia detection, supporting reliable medical decision-making. However, the model's performance on the minority SVEB class remains limited, with a recall of only 34.24%, indicating that SVEB beats are frequently misclassified as Normal due to morphological similarities and severe class imbalance. This limitation highlights the need for further improvements in minority-class detection for clinical applicability.
Co-Authors Muhammad Indra Nita Merlina Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya Siti Ernawati