Sistem Rekomendasi Tanaman Berdasarkan Kondisi Tanah Menggunakan Sensor Tanah 8-in-1 dan Algoritma Decision Tree

Putra Eka Praja; Ade Silvia Handayani; Nasron Nasron; Nyayu Latifah Husni; Asmawati Asmawati; Jaksen Jaksen

doi:10.51901/jiifkom.v5i02.739

Putra Eka Praja Politeknik Negeri Sriwijaya
Ade Silvia Handayani Politeknik Negeri Sriwijaya
Nasron Nasron Politeknik Negeri Sriwijaya
Nyayu Latifah Husni Politeknik Negeri Sriwijaya
Asmawati Asmawati Universitas Palembang
Jaksen Jaksen Politeknik Negeri Sriwijaya

DOI: https://doi.org/10.51901/jiifkom.v5i02.739

Keywords: rekomendasi tanaman, sensor tanah 8-in-1, machine learning, rekomendasi tanaman kesesuaian lahan, klasifikasi

Abstract

Selecting crops that are suitable for specific soil conditions is an important factor in improving agricultural productivity. This study aims to develop a soil-based crop recommendation system using data collected from an 8-in-1 soil sensor. The analyzed parameters include electrical conductivity, soil temperature, soil moisture, pH, nitrogen (N), phosphorus (P), potassium (K), and soil fertility. The dataset consists of 500 observations categorized into five crop types: oil palm, coconut, rice, corn, and banana. To identify the most effective classification model, several machine learning algorithms were evaluated, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), XGBoost, Gaussian Naive Bayes, and Logistic Regression. Model performance was assessed using accuracy, precision, recall, and F1-score metrics. The experimental results show that the Decision Tree algorithm achieved the best performance, with an accuracy of 95.6%, a precision of 0.952, a recall of 0.957, and an F1-score of 0.958. The confusion matrix analysis indicates that most samples were correctly classified into their respective crop categories. These findings demonstrate that the Decision Tree algorithm is highly effective in identifying crop suitability based on soil characteristics and has strong potential as a decision-support tool for crop recommendation in agricultural applications.

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