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All Journal bit-Tech Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Elektronika dan Telekomunikasi
Wahyu Caesarendra
Curtin University Malaysia
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Comparative Analysis Of Random Forest and Naive Bayes for Flood Classification Using Sentinel-1 SAR Clara Silvia Rotua Aritonang; Ade Silvia Handayani; Suroso Suroso; Wahyu Caesarendra; Asriyadi Asriyadi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2852

Abstract

This research introduces a framework for classifying flood inundation utilising Sentinel-1 Ground Range Detected (GRD) radar imagery alongside machine learning algorithms.  Radar backscatter values from pre- and post-event Sentinel-1 images were processed with SNAP and QGIS to extract spatial features and change indicators in decibel (dB) format.  The tabular dataset, comprising 500,000 samples that equally represent flooded and non-flooded areas, was utilised for model training. Two models, Random Forest and Naive Bayes, were assessed for their classification efficacy.  The Random Forest model demonstrated exceptional performance, attaining an accuracy of 99.81%, precision of 99.75%, recall of 99.67%, and an F1-score of 99.71%.  Naive Bayes achieved an accuracy of 52.63%, with precision and F1-score notably impacted by elevated false positive rates, although recall was 86.36%.  Analysis of confidence distribution indicated that Random Forest exhibited low-confidence errors at the decision boundary, whereas Naive Bayes demonstrated confident misclassifications. Analysis of computation time indicated that Naive Bayes required less than 0.1 seconds per run, whereas Random Forest completed training in under 3 minutes.  The trade-off between speed and reliability underscores the appropriateness of Random Forest for operational flood mapping applications.  This research provides a practical comparison of classification models utilising open-access radar data and establishes a dependable pipeline for pixel-level flood identification.
Feature Selection and Class Imbalance Machine Learning for Early Detection of Thyroid Cancer Recurrence: A Performance-Based Analysis Agus Wantoro; Wahyu Caesarendra; Admi Syarif; Hari Soetanto
Jurnal Elektronika dan Telekomunikasi Vol. 25 No. 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.758

Abstract

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
Integrated Sustainable Manufacturing and Waste Management Framework for Medium-Density Fiberboard (MDF): Finite Element Methods-Based Structural Optimization for Bookshelf Applications Sri Handyani; Rizki Setiadi; Listiyono Budi; Wahyu Caesarendra
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.3440

Abstract

Medium-density fiberboard (MDF) is commonly used in furniture manufacture because of its consistent qualities, low cost, and ease of processing. However, its relatively short lifespan and rising market demand have resulted in substantial waste generation, posing serious environmental and disposal concerns. This study provides an integrated sustainable manufacturing and waste management framework for MDF that incorporates artificial intelligence technology and finite element method (FEM)-based structural optimization for bookshelf applications. The structural performance is evaluated by numerical simulations focusing on von mises stress, displacement, and safety factor. These findings indicate that combining FEM-based design optimization with intelligent waste management strategies might enhance the structural performance and sustainability of MDF products. This study emphasizes the necessity of merging advanced simulation, artificial intelligence, and life-cycle assessment methodologies to create intelligent, efficient, and ecologically responsible wood-based manufacturing systems.
Co-Authors Ade Silvia Handayani Admi Syarif Agus Wantoro Asriyadi Asriyadi Clara Silvia Rotua Aritonang Hari Soetanto Listiyono Budi Rizki Setiadi Sri Handyani Suroso Suroso