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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Teknosains: Media Informasi Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Applied Data Sciences Kawanua International Journal of Multicultural Studies Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Edukasi dan Pengabdian kepada Masyarakat (JEPKM) International Journal of Health and Information System (IJHIS) Jurnal Imu Pendidikan dan Keagamaan Ruang Komunitas : Jurnal Pengabdian Masyarakat
Tajuddin, Muhammad
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Journal of Applied Data Sciences

 1 
Hybrid CNN Approach for Post-Disaster Building Damage Classification Using Satellite Imagery Sonang, Sahat; Yuhandri, Y; Tajuddin, Muhammad
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.931

Abstract

Accurate post-disaster building damage assessment is critical for timely response and effective reconstruction planning. This study proposes a hybrid deep learning architecture that integrates Inception-ResNet-v2 and EfficientNetV2B0, designed to enhance post-disaster damage classification from high-resolution satellite imagery. The model leverages dual-stream feature extraction, followed by concatenated fully connected layers optimized with dropout and batch normalization to improve generalization and reduce overfitting. The objective is to outperform standard Convolutional Neural Network (CNN) models in terms of classification and segmentation performance across multiple damage categories: no damage, minor damage, major damage, destroyed, and unclassified. The model was trained and validated on the publicly available xView dataset, covering over 12,000 annotated images from various natural disasters. Comparative evaluation against ResNet, GoogleNet, DenseNet, and EfficientNet demonstrates that the proposed model achieves the highest accuracy (86%), precision (85%), recall (86%), and F1-score (84%). Furthermore, it outperforms all baseline models in segmentation metrics, achieving an Intersection over Union (IoU) score of 0.7749 and a Dice Similarity Coefficient (DSC) of 0.8726. The model also significantly reduces misclassification rates in critical categories such as “major damage” and “destroyed.” A Wilcoxon signed-rank test confirmed that these improvements are statistically significant (p 0.05) across all major performance indicators. The novelty of this study lies in the fusion of two state-of-the-art CNN backbones with tailored architectural modifications, yielding a robust and generalizable model suitable for automated disaster damage assessment. This research contributes a scalable deep learning approach that can be integrated into real-time or semi-automated disaster response systems, offering improved decision-making support in emergency contexts. The results affirm the model’s potential as a reliable tool in post-disaster scenarios and set a foundation for future work in multi-modal and real-time AI-based disaster management.
Adaptive Integration of Optuna Optimization and Stacking Ensemble Learning for Automated Work Competency Classification Pratiwi, Mutiana; Defit, Sarjon; Tajuddin, Muhammad
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1228

Abstract

Artificial intelligence and machine learning are increasingly used to automate analytical and decision processes, including the evaluation of human competencies. However, traditional models often face challenges in accuracy and generalization when applied to linguistic data from interviews. This study aims to develop a model that integrates Optuna optimization and stacking ensemble learning to enhance the accuracy and interpretability of competency classification. Interview transcript data were processed using natural language processing techniques such as cleaning, tokenization, case folding, stopword removal, and stemming to ensure textual consistency. The text was then transformed into numerical representations using term frequency inverse document frequency weighting. To handle class imbalance, the synthetic minority oversampling technique was employed. Optuna was applied to optimize the hyperparameters of base models, including support vector classifier, Naïve Bayes, random forest, gradient boosting, and XGBoost. These optimized models were combined through a stacking ensemble to form the final classifier. The proposed model achieved an accuracy of 94 percent and a precision of 95 percent with macro and weighted F1 scores of 0.94. The results demonstrate stable and balanced performance across all competency categories, including analytical thinking, initiating action, problem solving, and work standards. Comparative analysis with previous studies in sentiment analysis, medical diagnosis, and financial forecasting confirmed that the integration of Optuna and stacking produces more robust and generalizable outcomes. The integration of Optuna optimization and stacking ensemble learning effectively improves classification performance while maintaining interpretability. The model demonstrates strong potential for automated competency evaluation in recruitment and human resource analytics. This framework can be extended to other linguistic datasets to support transparent and data-driven decision-making in artificial intelligence applications.
Co-Authors Abdul Aziz Abdul Muhid, Abdul Abu Bakar Agus Pribadi ahmad yani Ahmat Adil Akbar Juliansyah Aksa Aksa, Aksa Aldi, Febri Amra, Muhammad Fikri Ana Tsalitsatun Ni'mah Andi Hildayanti Andi Sofyan Anas Ardi, R Fanny Printi Ardi, Raden Fany Printi Asno Azzawagama Firdaus Awaluddin A., Muhammad Fajerin Biabdillah Fathin, Alfidhah Hammad, Rifqi I Made Yadi Dharma Irfan, Ahmad Asrul Azwar Machrizzandi, M Sya'Rani Maesaroh, Dwi Titi Maknuniah, Jauharatul Masrifah, Rofiah Mildawati, Titi Muchtar, Azmy Ali Nadiroh, Nurlaila Nursalimah, Putri Pratiwi, Mutiana Printi Ardi, R. Fany Putra, Ondra Eka Rivai, Aspin Nur Arifin Santoso, Heroe Sarjon Defit Sa’adatai, Yuan Septiana, Vina Tri Setiawan, Adil Sonang, Sahat Sumijan Sumijan Syahroni Hidayat Syam, Febrianto Syuhada, Fahmi Triwijoyo, Bambang Krismono Umar, Kusnadi Yanto, Musli Yuhandri Yuhandri, Yuhandri Yuhandri, Y Yusuf, Siti Agrippina Alodia Zainal Abidin