IMPLEMENTASI MACHINE LEARNING UNTUK SISTEM IRIGASI CERDAS
DOI:
https://doi.org/10.32497/orbith.v21i2.7038Keywords:
irrigation, machine learning, random forestAbstract
Abstrak
Penggunaan air yang tidak efisien dalam pertanian seringkali menjadi salah satu penyebab utama menurunnya produktivitas lahan. Di era pertanian modern, implementasi Machine Learning (ML) dapat menjadi solusi untuk memantau kondisi tanah dan menentukan kebutuhan irigasi secara otomatis. Penelitian ini mengembangkan sistem berbasis sensor tanah dengan parameter kelembaban, suhu, cahaya, dan curah hujan yang diproses menggunakan algoritma Random Forest. Data dikembangkan melalui simulasi sebanyak 10.000 sampel tanpa penambahan noise untuk menjaga representasi kondisi nyata. Model Random Forest dibandingkan dengan Decision Tree, SVM, dan Neural Network. Hasil pengujian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi di atas 96,4%. Selain itu dilakukan juga perbandingan penggunaan antara baseline data dengan jumlah data tiap kelas yang berbeda jauh dengan metode penyeimbang data Synthetic Minority Oversampling Technique (SMOTE). Hasil pengujian menunjukkan meskipun penggunaan metode SMOTE menurunkan akurasi secara umum, tetapi dapat meningkatkan Recall yang menunjukkan bahwa model lebih sensitif pada data minoritas.
Kata kunci: irigasi, machine learning, random forest
Abstract
Inefficient water use in agriculture is often a major cause of declining land productivity. In the modern agricultural era, the implementation of Machine Learning (ML) can be a solution for monitoring soil conditions and automatically determining irrigation needs. This study developed a soil sensor-based system with parameters of humidity, temperature, light, and rainfall processed using the Random Forest algorithm. Data was developed through simulations of 10,000 samples without the addition of noise to maintain the representation of real conditions. The Random Forest model was compared with Decision Tree, SVM, and Neural Network. The test results showed that Random Forest provided the best performance with an accuracy above 96.4%. Furthermore, a comparison was conducted between the use of baseline data with a significantly different number of data per class using the Synthetic Minority Oversampling Technique (SMOTE) data balancing method. The test results showed that although the use of the SMOTE method decreased accuracy in general, it could increase Recall, making the model more sensitive to minority data.
Keywords: irigation, machine learning, random forest
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