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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding KOMMIT Bulletin of Electrical Engineering and Informatics Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization Indonesian Journal of Artificial Intelligence and Data Mining Journal of Information Technology and Computer Engineering Jurnal Teknologi Jurnal Sistim Informasi dan Teknologi Sainstek : Jurnal Sains dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) SmartComp CSRID
Sumijan Sumijan
Gunadarma University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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Image-Based Classification of Rice Field Conversion: A Comparison Between MLP and SVM Using Multispectral Features Anisya, Anisya; Sumijan, Sumijan; Syahrani, Anna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5179

Abstract

The conversion of farmland into non-agricultural purposes has emerged as a pressing concern in many urban regions, including Koto Tangah District, Padang City. In this area, agricultural land experienced a 4% shift in land use between 2022 and 2024. If this trend continues, it could lead to a notable decline in rice production and ultimately threaten food security. This research focuses on examining spatial transformations of rice fields from 2022 to 2024 by utilizing Sentinel-2 satellite imagery along with advanced classification techniques. Vegetation and moisture features were extracted using NDVI, NDWI, texture analysis through GLCM, and Principal Component Analysis (PCA). To classify land cover changes and assess model accuracy, two machine learning approaches were applied: Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The findings reveal a considerable reduction in dense vegetation, indicated by the downward shift of NDVI values in 2024. MLP achieved an accuracy of 82%, outperforming SVM, which reached 71%. Furthermore, MLP obtained a higher F1-score for non-rice field detection (0.75 vs. 0.74) and produced more realistic delineations of rice field boundaries during spatial validation. These outcomes highlight the potential of MLP in monitoring land use conversion, supporting agricultural land conservation, and guiding sustainable urban planning. Moreover, the study contributes to computer science by advancing the use of machine learning for spatio-temporal analysis and reinforcing the role of non-linear models in satellite image classification.
Co-Authors Achmad Noerkhaerin Putra Adi Gunawan, Adi Afriyadi, Iqbal Agasi, Andre Ahmad Zaki Am, Andri Nofiar Anam, M Khairul Anisya Anisya Ardiyan, Destio Esa Kurniawan Fajrul Islami Fauzana, Rahmi Febriani, Widya Firdaus, Muhammad Bambang Gunadi Widi Nurcahyo Hidayad, Asri Ilham Roni Yansyah Iskandar Fitri, Iskandar Jaya, Budi Juansen, Monsya Julius Santony Kadrahman, Kadrahman Karfindo, Karfindo Mardison Mardison Negoro, Wahyu Saptha Pohan, Yosua Ade Putra, Ondra Eka Putra, Surya Dwi Putra, Yoga Ananda Putri, Adek Putri, Dhena Marichy Rahmad Dian Rahman, Surya Aulia Rahmi, Mifthahul Renaldi, Encik Yoega Saiful Bukhori Sarjon Defit Setiawan, Adil Silfia Andini, Silfia Siska, Ayu Prima Soeheri, Soeheri Sovia, Rini Suhaidir, Lc Granadi Sunaryo, Nandra Surmayanti, Surmayanti Tajuddin, Muhammad Tamaza, Muhammad Abyanda Tukino, Tukino Yamin, Abdul Yamin Yendi Putra Yuhandri Yuhandri, Yuhandri Yunus, Yuhandri