Crude Palm Oil Quality Classification Using the K-Nearest Neighbor Method

Andiny Andiny, Muhammad Rizka, Musta’inul Abdi

Abstract


The quality of Crude Palm Oil (CPO) is a critical factor in the palm oil processing industry, particularly for PT Perkebunan Nusantara IV Regional VI, which is required to maintain consistent quality standards. However, the manually conducted CPO quality classification process is potentially prone to inaccuracies, which can impact production and distribution efficiency. This research aims to develop a CPO quality classification model using the K-Nearest Neighbor (KNN) algorithm based on laboratory test data. The process involved data preprocessing and classification model training. Based on evaluation using a confusion matrix, the model achieved an accuracy of 96.35%, with an F1-score of 97.18% for Grade 1 and 94.81% for Grade 2. The high precision and recall values for both classes indicate that the KNN method is capable of classifying CPO quality consistently and accurately. This model is expected to serve as a foundation for further research in implementing data-driven CPO quality classification.

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