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All Journal Jurnal Fisika Unand
Muhammad Ario Eko Rahadianto
Teknik Geomatika, Institut Teknologi Sumatera
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Analisis Deformasi di Lampung dan Selat Sunda Berdasarkan Data GNSS tahun 2018 hingga 2021 Ongky Anggara; Muhammad Ario Eko Rahadianto; Satrio Muhammad Alif; Een Lujainatul Isnaini
Jurnal Fisika Unand Vol 13 No 5 (2024)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.13.5.637-643.2024

Abstract

The Lampung Province and Sunda Strait have a seismic gap zone with the potential for major earthquakes in the future. This study analyzes the deformation occurring in this region using continuous Global Navigation Satellite System (GNSS) station data from Indonesia Continuously Operating Reference Station (InaCORS) and Sumatran GPS Array (SuGAr) from 2018 to 2021.5. The GNSS data was processed using the Bernese 5.2 scientific software, applying least squares for velocity changes and statistical tests to analyze significance. The data processing was carried out in two schemes: the first scheme covering 2018-2020, and the second covering 2019-2021. The results of the deformation analysis from 2018 to 2021, using two continuous GNSS data processing schemes, showed velocity changes relative to the Sundaland Plate ranging from ~2 mm/year to ~20 mm/year. In the eastern region of the Sumatra fault, the velocity changes were smaller, around ~5 mm/year, due to the minimal influence of tectonic activity. However, in the Sunda Strait region, the deformation was influenced by volcanic activity. The deformation occurring in Lampung Province and the Sunda Strait, based on GNSS velocity changes, significantly contributes to tectonic and volcanic activities.
Klasifikasi Tanaman Menggunakan Metode Deep Learning Residual Network (ResNet) Berbasis Data Time Series Penginderaan Jauh di Desa Girimulyo, Lampung Timur Muhammad Ario Eko Rahadianto; Putri Wahyu Sejati; Adam Irwansyah Fauzi; Aulia Try Atmojo; Tika Widayanti; Rizky Ahmad Yudanegara
Jurnal Fisika Unand Vol 15 No 1 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.1.70-78.2026

Abstract

Most Indonesians work in agriculture, making crop-type maps essential for food security. This study evaluates time-series classification using Residual Network (ResNet) for crop mapping. Sentinel-2A imagery from May 2021 to May 2022 was used with 120 samples across five classes: Corn, Coconut, Non-crop, Banana, and Other Crops. The data were processed into a regularized Earth Observation (EO) data cube and trained using samples filtered with Self-Organizing Map (SOM) under two schemes: single clustering (SC) and double clustering (DC). The ResNet model was trained with filtered data and tested with varying epochs. The study produced a crop-type map of Girimulyo, East Lampung, smoothed with the Bayesian method. Accuracy assessment showed that SC at 100 epochs achieved 87%, exceeding the 85% threshold, while DC yielded lower accuracy due to reduced training data. These results confirm that ResNet-based time-series classification is effective for crop-type mapping in the study area.
Co-Authors Adam Irwansyah Fauzi Aulia Try Atmojo Een Lujainatul Isnaini Ongky Anggara Putri Wahyu Sejati Rizky Ahmad Yudanegara Satrio Muhammad Alif Tika Widayanti