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All Journal Jurnal ilmiah teknologi informasi Asia
Sugiharto , Tomy Ivan
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Computer Science & IT

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Utilizing Long Short-Term Memory (LSTM) Networks for Predicting Seismic-Induced Building Damage: A Bawean Region Case Study Zarkoni, Ahmad; Almais, Agung Teguh Wibowo; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Pagalay, Usman; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1212

Abstract

This study examines the feasibility of employing Long Short-Term Memory (LSTM) networks to estimate earthquake-induced building damage using a focused dataset derived from the continuous 8-day mainshock–aftershock sequence that occurred in March 2024. A total of 483 events were analyzed, utilizing three readily available source parameters: magnitude, depth, and epicentral distance to predict the corresponding EMS-98 damage grade. The motivation for using an LSTM architecture stems from its capacity to model temporal dependencies within sequential seismic activity, despite the limited size of the dataset. The best-performing single-split model (B4) achieved a test R^2 of 0.5738 and an RMSE of 0.2997 on the held-out set. However, to obtain a more robust assessment of the model’s generalizability, a 5-fold TimeSeriesSplit cross-validation was conducted. The cross-validation procedure yielded a mean R^2 of 0.49 with a standard deviation of 0.27, and a mean RMSE of 0.33 with a standard deviation of 0.16. These results demonstrate that the LSTM model provides a credible baseline model for exploratory damage estimation, although a substantial portion of the variance remains unexplained due to the absence of geotechnical, soil amplification, and structural fragility information. The findings highlight the potential of sequence-based modeling for rapid damage estimation and underscore the need for integrating site-specific and structural variables in future work to enhance predictive accuracy.
A hybrid GoogLeNet–GLCM feature extraction framework for textural representation of post-disaster building damage imagery Amani, Holidiyatul; Almais, Agung Teguh Wibowo; Abidin, Zainal; Nugroho, Fresy; Kurniawan, Fachrul; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1214

Abstract

Accurate representation of visual characteristics in post-disaster building imagery is crucial for downstream analytical tasks such as damage interpretation, retrieval, and automated assessment. This study presents a focused investigation of feature extraction using a hybrid approach that integrates deep semantic representations from the GoogLeNet architecture with statistical texture descriptors inspired by the Gray-Level Co-Occurrence Matrix (GLCM). The objective of this work is limited strictly to the generation and analysis of semantic–textural feature vectors rather than the development or evaluation of any classification or prediction model. High-level feature maps are obtained from a selected convolutional layer of GoogLeNet, after which statistical texture properties—contrast, energy, and homogeneity—are computed per channel. A representative set of feature channels is analyzed to demonstrate the capabilities of the proposed hybrid extraction pipeline. The results demonstrate the potential of semantic–textural descriptors to provide interpretable feature characteristics in building-damage imagery. This study provides a methodological foundation and analytical insight for future works that may incorporate these feature representations into classification, clustering, or decision-support frameworks.
Co-Authors Agung Teguh Wibowo Almais Amani, Holidiyatul Cahyo Crysdian Fachrul Kurniawan Fresy Nugroho Mokhamad Amin Hariyadi Usman Pagalay Zainal Abidin Zarkoni, Ahmad