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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Mila Kusuma
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Computer Science & IT

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Perbandingan Kinerja Machine Learning Perekomendasi Tanaman Berdasarkan Data Iklim dan Kondisi Tanah Zidan Fahreza; Arwin Datumaya Wahyudi Sumari; Mila Kusuma
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The selection of appropriate crop types according to agroclimatic conditions is a determining factor in the success of agricultural productivity. This study develops a machine learning-based crop recommendation system to classify 22 crop types based on seven agroclimatic parameters (N, P, K, temperature, humidity, pH, and rainfall). Four machine learning algorithms were compared for performance: K-Nearest Neighbors (KNN), Logistic Regression, Artificial Neural Network (ANN), and Decision Tree using a dataset of 2200 samples with an 80:20 split ratio for training and testing. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The research results show that KNN with k=13 achieved optimal performance with 98.18% accuracy, 98.28% precision, 98.18% recall, and 98.17% F1-score. This algorithm outperformed Logistic Regression (97.27%), ANN (96.59%), and Decision Tree (95.23%). Confusion matrix analysis identified that classification errors primarily occurred in crop pairs with similar agroclimatic characteristics such as lentil-chickpea and pigeonpeas-kidneybeans. KNN proved to be the most suitable model for implementing precision agriculture decision support systems in the Indonesian agricultural context by providing high accuracy and good generalization capability.
The selection of appropriate crop types according to agroclimatic conditions is a determining factor in the success of agricultural productivity. This study develops a machine learning-based crop recommendation system to classify 22 crop types based on se Zidan Fahreza; Arwin Datumaya Wahyudi Sumari; Mila Kusuma
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The selection of appropriate crop types according to agroclimatic conditions is a determining factor in the success of agricultural productivity. This study develops a machine learning-based crop recommendation system to classify 22 crop types based on seven agroclimatic parameters (N, P, K, temperature, humidity, pH, and rainfall). Four machine learning algorithms were compared for performance: K-Nearest Neighbors (KNN), Logistic Regression, Artificial Neural Network (ANN), and Decision Tree using a dataset of 2200 samples with an 80:20 split ratio for training and testing. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The research results show that KNN with k=13 achieved optimal performance with 98.18% accuracy, 98.28% precision, 98.18% recall, and 98.17% F1-score. This algorithm outperformed Logistic Regression (97.27%), ANN (96.59%), and Decision Tree (95.23%). Confusion matrix analysis identified that classification errors primarily occurred in crop pairs with similar agroclimatic characteristics such as lentil-chickpea and pigeonpeas-kidneybeans. KNN proved to be the most suitable model for implementing precision agriculture decision support systems in the Indonesian agricultural context by providing high accuracy and good generalization capability.
Co-Authors Arwin Datumaya Wahyudi Sumari Zidan Fahreza