OPTIMALISASI KESUBURAN TANAH DALAM BUDIDAYA JAGUNG MENGGUNAKAN MACHINE LEARNING DENGAN METODE RANDOM FOREST BERBASIS SISTEM INFORMASI GEOGRAFIS

Mira Mira, Kusnanto Kusnanto, Janice Thea Sumar

Abstract


The growth of national corn production faces the challenge of declining soil fertility, which impacts productivity. This study aims to classify soil fertility levels in corn cultivation areas in Bengkayang District using the Random Forest algorithm integrated with a Geographic Information System (GIS). The research dataset consists of 300 samples collected through random sampling at three corn plantation locations, covering variables such as pH, temperature, humidity, rainfall, and geographic coordinates. The initially unlabeled data were processed through rule-based labeling with reference to agronomic literature to produce three fertility categories: low, medium, and high. The preprocessing stage also included outlier checking and class imbalance management. The Random Forest model was built with parameters n_estimators=200, criterion="entropy", and class_weight="balanced". The evaluation results showed an accuracy of 91% with high precision, recall, and F1-score values across all classes. Feature importance analysis identified soil pH (55.36%) as the most dominant factor, followed by humidity (38.66%), while temperature and rainfall contributed little. Integration of classification results into maps produces interactive spatial visualization, so it can be used as a decision support system in precision agriculture practices

Keywords


Optimization; Corn Plants; Random Forest; Machine Learning; GIS

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DOI: https://doi.org/10.29100/jipi.v11i3.9214

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JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
ISSN 2540-8984
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