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All Journal Jurnal Fisika Unand
Sejati, Putri Wahyu
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Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Klasifikasi Tanaman Menggunakan Metode Deep Learning Residual Network (ResNet) Berbasis Data Time Series Penginderaan Jauh di Desa Girimulyo, Lampung Timur Rahadianto, Muhammad Ario Eko; Sejati, Putri Wahyu; Fauzi, Adam Irwansyah; Atmojo, Aulia Try; Widayanti, Tika; Yudanegara, Rizky Ahmad
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 Atmojo, Aulia Try Fauzi, Adam Irwansyah Rahadianto, Muhammad Ario Eko Tika Widayanti Yudanegara, Rizky Ahmad