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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Dian Agaventa, Chrissandro
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Computer Science & IT

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PEMANFAATAN GOOGLE EARTH ENGINE DAN ALGORITMA RANDOM FOREST UNTUK PEMETAAN LAHAN PERKEBUNAN JERUK Dian Agaventa, Chrissandro; Rumapea, Humuntal; Indra Kelana Jaya
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4078

Abstract

This study employed Google Earth Engine (GEE) and the Random Forest algorithm to map citrus plantations in Silimakuta District, Simalungun Regency,  North Sumatra. As a major citrus production center—reaching 840,000 quintals in 2023—the region faces challenges in producing accurate and efficient maps of plantation distribution. By processing Sentinel-2 and Sentinel-1 satellite imagery in GEE, this study provides a more detailed and reliable mapping solution. The Random Forest model achieved a land-cover classification accuracy of 97% and a Kappa coefficient of 96.3%, demonstrating the method’s effectiveness for land mapping. This approach can overcome existing limitations in land data and deliver visual information useful for increasing citrus plantation productivity in the region. Therefore, the combined use of Google Earth Engine and the Random Forest algorithm shows strong potential to support more optimal and sustainable land management.
PEMANFAATAN GOOGLE EARTH ENGINE DAN ALGORITMA RANDOM FOREST UNTUK PEMETAAN LAHAN PERKEBUNAN JERUK Dian Agaventa, Chrissandro; Rumapea, Humuntal; Indra Kelana Jaya
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study employed Google Earth Engine (GEE) and the Random Forest algorithm to map citrus plantations in Silimakuta District, Simalungun Regency, North Sumatra. As a major citrus production center—reaching 840,000 quintals in 2023—the region faces challenges in producing accurate and efficient maps of plantation distribution. By processing Sentinel-2 and Sentinel-1 satellite imagery in GEE, this study provides a more detailed and reliable mapping solution. The Random Forest model achieved a land-cover classification accuracy of 97% and a Kappa coefficient of 96.3%, demonstrating the method’s effectiveness for land mapping. This approach can overcome existing limitations in land data and deliver visual information useful for increasing citrus plantation productivity in the region. Therefore, the combined use of Google Earth Engine and the Random Forest algorithm shows strong potential to support more optimal and sustainable land management.
Co-Authors Humuntal Rumapea Indra Kelana Jaya