Metode Klasifikasi Kematangan Tandan Buah Segar Kelapa Sawit: Sebuah Tinjauan Sistematis - Nurita Evitarina; Kusrini Kusrini (G-Tech: Jurnal Teknologi Terapan, 2024)
Determining the maturity of palm oil fruit is very important to improve the quality and quantity of palm oil production. This research examines the use of deep learning technology to classify oil palm maturity through a Systematic Literature Review (SLR). The research method used is a Systematic Literature Review (SLR) which involves analysis of 35 journals from Scopus and Google Scholar from 2020 to 2024, with a focus on datasets, algorithms, dataset locations, and methods for measuring model performance. The results show that ANN and CNN are the most widely used algorithms, with usage of 16% and 10% respectively. Accuracy, precision, recall, and F1 score are the most common performance metrics. Future research should focus on improving model generalization and integrating data from multiple sources to improve classification accuracy, the aim of which is to contribute to palm oil maturity classification and help the industry improve production efficiency and quality