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, 2026)
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.