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Program Studi Teknik Pertanian Universitas Hasanuddin. Alamat : Fakultas Pertanian Universitas Hasanuddin, Kampus Unhas Tamalanrea KM 10 Makassar 90245.
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INDONESIA
Salaga Journal
Published by Universitas Hasanuddin
ISSN : -     EISSN : 30322677     DOI : https://doi.org/10.70124/salaga
Core Subject : Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Control & Systems Engineering Electrical & Electronics Engineering Engineering
SALAGA journal is an academic journal for the publication of original articles and reviews in the field of appropriate technology for agriculture production and processing. The aim of this journal is to provide a forum for academia, researchers, and practitioners in discussing, reviewing, analyzing, and reporting research findings related to appropriate technologies in agriculture production and processing. Using tools, equipment, and practices that are well-matched to the regional contexts, resources, and farmer needs is referred to as using appropriate technology in agriculture. It seeks to be reasonably priced, long-lasting, and simple to maintain while enhancing production and livelihoods by taking into account environmental and social factors. Editor in Chief: Haerani ISSN (online): 3032-2677 Frequency: Biannual
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 41 Documents
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Mapping Cacao Plantations Using Random Forest Classification and Sentinel-2A Imagery in Batulappa District, Pinrang Regency, Indonesia Putri Ayu Andirah; Haerani; Suhardi; Husnul Mubarak
Salaga Journal Volume 04, No. 1, June 2026
Publisher : Program Studi Teknik Pertanian Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70124/salaga.v4i1.2298

Abstract

Remote sensing and Geographic Information System (GIS) technologies provide effective tools for mapping plantation crops and supporting sustainable land management. Cacao is an important plantation commodity in Indonesia, particularly in Pinrang Regency, South Sulawesi. This study aimed to map cacao and non-cacao land cover in Batulappa District using Sentinel-2A imagery and the Random Forest algorithm. Three input approaches were evaluated: an RGB band composite, the Normalized Difference Vegetation Index (NDVI), and Gray-Level Co-occurrence Matrix (GLCM) texture features. Ground-truth data were divided into training and validation datasets, and classification accuracy was assessed using a confusion matrix, including overall accuracy, user accuracy, and producer accuracy. The RGB band composite produced the highest overall accuracy of 85.38%, followed by GLCM with 75.47% and NDVI with 74.06%. For the cacao class, the RGB approach achieved a user accuracy of 80.00% and a producer accuracy of 86.96%, with an estimated cacao area of 4,516.80 ha, or 46.90% of the study area. These results indicate that the Sentinel-2A RGB band composite combined with Random Forest classification outperformed NDVI and GLCM for mapping cacao plantations in Batulappa District.

Page 5 of 5 | Total Record : 41

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