Identifikasi Penyakit Pada Daun Kelapa Sawit Dengan Pendekatan CNN AlexNet - Mandiri, Tobyanto Putra; Dharmawan, Benedictus Budhi; Ibn, Ferreyla Setara; Untoro, Meida Cahyo (Jurnal Informatika: Jurnal Pengembangan IT, 2025)
Oil palm is a plant that plays an important role in Indonesia's agricultural commodities. Cultivating oil palm is suitable for Indonesia due to its tropical climate, which greatly supports the growth of this plant. However, cultivating oil palm is not easy. The emergence of leaf diseases in oil palm can hinder growth, thereby affecting fruit production levels. This research aims to identify diseases on oil palm leaves using one of the methods of Deep Learning, namely the Convolutional Neural Network (CNN) method. This method was chosen because CNN leverages image-based datasets for classification and prediction, making it highly suitable for identifying diseases on oil palm leaves. The research begins with collecting a dataset of images of diseased oil palm leaves. The collected dataset will undergo pre-processing to enhance image quality, enabling more efficient processing by the model. The classification results will subsequently be evaluated to determine the accuracy level of the image processing performed by the model. By implementing Convolutional Neural Network, this research is expected to produce an effective and accurate system for identifying diseases on oil palm leaves, assisting farmers in cultivating oil palm, reducing losses, and ultimately increasing the productivity of oil palm plantations.