IMPLEMENTASI ALGORITMA NAÏVE BAYES UNTUK DIAGNOSA PENYAKIT PADA BUAH MENTIMUN BERBASIS WEB - Vivi Amalia; Mochamad Adhari Adiguna (JUPIK : Jurnal Penelitian Ilmu komputer, 2025)
Cucumber is an agricultural commodity that has many benefits, both in the culinary and health fields. However, cucumber plants are often damaged by various diseases, which are difficult for farmers to diagnose. To overcome this problem, this research developed a web-based disease diagnosis system for cucumber plants using the Naïve Bayes algorithm. The Naïve Bayes algorithm was chosen because of its efficiency in handling cucumber plant symptom data and calculating the probability of disease based on the symptoms input by the user. This system is designed to help farmers identify cucumber diseases quickly and accurately, and provide recommendations for appropriate action. The results of this research show that the system developed is successful in diagnosing cucumber diseases with high accuracy, helping farmers make the right decisions to maintain plant health. The Naïve Bayes algorithm has proven effective in calculating disease probabilities based on observed symptoms, and the system can be easily accessed via a user-friendly web interface. With this system, it is hoped that it can help farmers maintain the health of cucumber plants and increase agricultural productivity. Keywords: Naïve Bayes, disease diagnosis, cucumber, expert system, web-based.