Klasifikasi Data Mining Pada Bibit Pertanian Dengan Menggunakan Algoritma Naïve Bayes - Pandu Rahmat Aprianto; Novi Lestari; Cindi Wulandari (Resolusi : Rekayasa Teknik Informatika dan Informasi, 2024)
Choosing the right oil palm seedlings is one of the efforts to increase the productivity of oil palm plants. At the time of seedling selection, the problem that is often faced is that not all types of seeds can be as desired by farmers against the conditions of different types of oil palm fruit seeds. Therefore, a system is needed that helps in determining the type of superior seedlings according to the needs of farmers. In this research the author applies a machine learning prediction model in determining oil palm seedlings based on leaf type, stem type, seed origin and stem candidates. The purpose of this research is to produce a classification model for determining oil palm seedlings and can provide accurate training for farmers in choosing superior seeds and also can farmers know the characteristics of superior seeds. The method used in this research is naïve bayes. In the first test, both models managed to achieve an accuracy of 84% and an F1-score value of 66%. However, the best performing Naïve Bayes model is the one used in the second test scenario, which is applied as a prediction model in determining oil palm seedlings through the website developed in this study, in the form of a Data Mining Classification System on Agricultural Seedlings at the Agricultural Extension Center of Tuah Negeri District, Musi Rawas Regency Based on Website which can assist in the selection process of oil palm seedlings that will be planted and get superior seedling results.