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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Indonesian Journal of Artificial Intelligence and Data Mining JURNAL EDUCATION AND DEVELOPMENT Jurnal Aplikasi Statistika & Komputasi Statistik EIGEN MATHEMATICS JOURNAL Prosiding Seminar Nasional Official Statistics Jurnal Kebijakan Pembangunan The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Rani Nooraeni
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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COMPARATIVE ACCURACIES USING MACHINE LEARNING MODELS FOR MAPPING OF SUGARCANE PLANTATION BASED ON SENTINEL-2A IMAGERY IN KEDIRI AREA, EAST JAVA Ridson Al Farizal Pulungan; Rani Nooraeni
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 1 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2024.v21.a3840

Abstract

Data collection in smallholder sugarcane plantations is still very sensitive to the subjectivity of informants and data collectors. In the meantime, the problem with data collection on sugarcane plantation companies is a low response rate. This situation can reduce the precision of the estimates that are produced. Consequently, the goal of this research is to recognize sugarcane fields using the machine learning models on Sentinel-2A satellite imagery in Kediri Area that covering Kediri Regency and Kediri Municipality, East Java. Along with developing machine learning algorithms, this research will evaluate how well LightGBM performs when compared to other algorithms, including CART, SVM, Random Forest, and XGBoost. Each model employed hyperparameter tuning with random search and stratified 10-fold cross validation to avoid overfitting. The process of labelling satellite imagery using images from Google Street View, then predictor variables used are NDVI, NDWI, NDBI, EVI, and elevation. The most accurate classification model obtained was LightGBM, with a 98% accuracy and a cohen’s kappa of 97.7%. The estimated area of sugarcane plantations in the Kediri Regency and Kediri Municipality in September 2022 is 18,897.6 ha and 571.87 ha.
Co-Authors Ahmad Firman Maulana Ahmad Safrian Fauzi Bimananda W Camelia Iltazami Ulva Esa Anindika Sari Fath Esa Prasanti Kusuma Faza Budiarti Fitri Anna Resqiah Asri Hanum Iftitah Rahma Insan Riski Karina Dwi M Ridwan Firdaus Moh. Fadli Hidayat DJ Makaraseng Muh. Rizki Muhammad Ali Irfan Muhammad Hasby Ashshiddiq Nyoman Putri Pradievy Syanthi Rendy Rendi Reza Septian Pradana Ria Nurul Ridson Al Farizal Pulungan Takdir Theresa Siahaan Tika Novitasari Uzma, Iffati Wahyu Winarto Yansi Indiyani Yosherina Dhea