Klasifikasi Varietas Kopi Berdasarkan Kondisi Tanah dan Suhu Menggunakan Algoritma Gaussian Naïve Bayes

Riza, Fahrur
Safwandi, Safwandi
Fadlan, Said

Jurnal Algoritma
Vol 22 No 1 (2025): Jurnal Algoritma

This research aims to classify the most suitable coffee varieties grown in the Bener Meriah region based on environmental factors such as soil pH, altitude from sea level (masl), and temperature. The method used is Gaussian Naïve Bayes, a probability-based classification technique that assumes that the input features are normally distributed and mutually independent. This method is relevant because it is able to handle numerical data efficiently. The results showed that the classification accuracy reached 45.7%, with a high precision value in the Gayo 3 class of 0.60%. Although the results are not yet optimal, the method shows potential in predicting the suitability of coffee varieties based on the analyzed environmental parameters.