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All Journal G-Tech : Jurnal Teknologi Terapan Journal of Information Technology
Mokhamad Amin Hariyadi
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks Revaldi Rahmatmulya; Agung Teguh Wibowo Almais; Mokhamad Amin Hariyadi
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1919

Abstract

Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. The data utilized consists of aerial images sourced from xView2: Assess Building Damage, comprising 50 aerial images with 5 classes: no-damage, minor-damage, major-damage, destroyed, and unlabeled. The steps undertaken in this research include data preprocessing using patchify and data augmentation. Subsequently, feature extraction is performed using convolution, followed by the training process using a neural network with the proposed architecture. This study proposes an architecture with 27 hidden layers, with feature extraction utilizing average pooling. The model evaluation process will employ Mean Intersection over Union (MIoU) to assess how closely the segmentation prediction results resemble the original data. The proposed architecture demonstrates the best MIoU result with a value of 0.31 and an accuracy of 0.9577.
Analytic Predictive of Crescent Sighting Using Astronomical Data-Based Multinomial Logistic Regression in Indonesia Tomy Ivan Sugiharto; Mokhamad Amin Hariyadi; Totok Chamidy; Irwan Budi Santoso; Cahyo Crysdian; Ahmad Zarkoni; Ma'muri Ma'muri; Syahreni Syahreni
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8246

Abstract

This research aims to develop and validate a sophisticated crescent visibility classification model in Indonesia. Multinomial Logistic Regression (MLR) was chosen for its capability to provide clear model interpretation through coefficient analysis. Utilizing comprehensive observational data (2021-2025) from Indonesia's Meteorology, Climatology, and Geophysics Agency (BMKG), the study comprised 2210 data points. The model classifies visibility into three categories (Dark, Faint, and Bright) based on defined elongation thresholds. The final predictor variables used were azimuth difference, moon altitude, and elongation. Analysis of the optimal model's (Model A3) coefficients revealed azimuth difference and elongation as the most dominant predictors, marked by exceptionally large positive coefficients (12.050 and 12.018, respectively) for classifying the 'Faint' category. After data preprocessing and systematic optimization ('saga' solver, L2 penalty), the optimal model (A3, C=100) demonstrated exceptional performance with an outstanding F1-Score of 99.10%. These findings strongly validate MLR's effectiveness for elongation-based crescent visibility classification and highlight its substantial potential as a reliable foundation for objective decision-making.
Co-Authors Agung Teguh Wibowo Almais Ahmad Zarkoni Cahyo Crysdian Irwan Budi Santoso Ma'muri Ma'muri Revaldi Rahmatmulya Syahreni Syahreni Tomy Ivan Sugiharto Totok Chamidy