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Journal : joiv international journal on informatics visualization

A Machine Learning Approach to Spatial Analysis of Paddy Field Conversion Using Multispectral Sentinel-2A Imagery Fauzan, Achmad; Kurnia, Anang
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3617

Abstract

The expanse of rice fields is a critical metric as it is intimately linked to agricultural productivity in a given locale. This study investigates the application of satellite imagery to quantify trice fields' acreage and temporal variations. The data utilized was acquired by the Sentinel-2A multispectral satellite. The variables employed are the image's baseband and spectral index. The research area encompasses the Sukamakmur sub-district in Bogor Regency, Indonesia. The types of machine learning models include Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and k-Nearest Neighbor (kNN). The simulation of class numbers is conducted to achieve the most stable and precise evaluation metric values. The XGBoost algorithm is used for the overall classification process of the region based on the optimal metric score. The model's accuracy, precision, recall, and F1-score are 92.37%, 92.3%, 92.38%, and 92.33%, respectively, indicating a very good performance. The model successfully captures a decline in rice field area between 2020 and 2023. Using the Modified Moran’s Index (MMI), the study reveals a positive spatial autocorrelation, indicating a clustered pattern in land-use change. Regions that experience either substantial or minor changes in land use are commonly situated near areas exhibiting similar characteristics. This study presents a spatially aware machine learning framework that enables the effective monitoring of agricultural land-use dynamics. In the future, this framework can be enhanced by integrating time-series forecasting and socio-economic data, supporting more informed decision-making in food security planning and agricultural policy development.
Co-Authors A Fahira Nur Adhar Arifuddin Adhi Pribadi Agung Dwi Ramadhan, Agung Ahmad, A. Sri Sartika Sufiina Amalina, Nur Dina Anang Kurnia Andreas Wahyu Gunawan Ariyadi, Fandy Akhmad Ayu Wulandari Bagus Sartono Bariklana, Muhammad Buana, Arya Galih Cahyani, Laras Niken Dwi Christopher Yudha Erlangga Dafrian, Rafli Danang Bagus Wibowo Dany Hilmanto Daryono Daryono, Daryono Denta, Anggeria Dewi, Sawitri Diana Apriyanti, Diana Dwitra Gusti Alriscki Efi Miftah Faridli Elsa Pudji Setiawati Evicenna Naftuchah Riani Fachry Abda El Rahman Fadliansyah, Azhimy Fauzi, Fatkhurokhman Faza Izzatul Muttaqin Fazira, Nabila Dwi Febrianti, Nur Qadri Feri Wibowo Fitri Amalia Fitri Yanti Ghefira Nurhaliza, Celine Hafidz, Syauqi Jauzza Hakim, Raden Bagus Fajriya Humairah, Nanda Lailatul Lahan Adi Purwanto LUKMAN, LUKMAN Marisi Aritonang Marisma, Murni Maulana, Ashabul Akbar Maziyah Mazza Basya Mohamad Irkham Mamungkas Munandar Munandar Nabilah, Muna Faizatun Najlah Nur Faizin Nur Kusmiyati Oktaviani, Nabila Pradana, Wahyu Aji Pratiwi, Erlina Lutfiayu Primandari, Arum Handini Purwanto, Kalis Purwanto, Muhammad Idris Rahmawati, Octavia Ramadhan, Muhammad Rizal Ramadhan, Rizki Fauzian Rantisi, Muhamad Zia Ridho Muktiadi Rinaldi Sjahril, Rinaldi Rosdiana, Siti Roza Azizah Primatika, Roza Azizah Sadat, Fauzan Safira, Nuzulia Nur Santoso, Alfian Saputra, Aditia Shenny Oktoriana Siregar, Ayu Lestari Siregar, Rania Febriyola Supriyono Supriyono Suri, Mulhamatus Latifatus Suria, Muh. Yunus Sutisna, Irwan Syahdony, Farrel Nolan Wahyudin, M. Abyan Widiyanto, Rhendy K P Widyastuti, Galuh Widyawati, Dwi Kartika Wilis Dwi Pangesti Wiranti, Ridha Yanasari, Herlinda Yuliana Watiningrum, Rahayu Yuliana, Alfa Zahidah, RA Ghina