Penerapan Algoritma Holt-Winters Exponential Smoothing Untuk Estimasi Dan Naïve Bayes Untuk Klasifikasi Produksi Kelapa Sawit

Hanny Handayani Sucinta (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
Tedy Setiadi (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)

Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Vol 4, No 4 (2023): Edisi Oktober

This research optimizes oil palm production data in Kuantan Singingi Regency using Holt-Winters Exponential Smoothing and Naïve Bayes methods. With parameters alpha = 0, beta = 0.1, and gamma = 0.2, the estimation model successfully increases production in the upcoming months. Evaluation using MAPE shows estimation accuracy below 18%. The Naïve Bayes classification model achieves an accuracy of around 85%, indicating a good balance between accuracy and precision. This study provides a significant contribution to oil palm production planning and assists farmers and cooperatives in more efficient management.