The application of machine learning algorithms for assessing the maturity level of palm fruits as the prominent commodity in the Western-Southern Area of Aceh
DOI:
https://doi.org/10.22441/oe.2024.v16.i1.102Kata Kunci:
palm fruit, maturity detection, Self Organizing Map (SOM)Abstrak
The potential of palm oil plantations in Aceh is substantial, with the province ranking eighth in Indonesia for palm oil cultivation. Aceh boasts a vast oil palm plantation area of 470.8 thousand hectares, comprising 44% of Aceh's total plantation land. Palm fruit quality directly impacts palm oil production, emphasizing the need for consistent maturity levels. To address this, computer algorithms, especially machine learning, have been applied. This study introduces the Self-Organizing Map (SOM) Algorithm for palm fruit maturity determination. SOM's reliability in capturing dataset topology offers a diverse classification process, revolutionizing palm fruit maturity detection and optimizing palm oil production. This study uses 40 dataset consisted of 20 mature and 20 unmature palm fruit image as the basis data which then converted into RGB and HSV value with Matlab engine. The result of the study indicates that the SOM algorithm is capable of classifying the maturity detection with 100% precision result. The SOM algorithm is synthesized in a Graphical User Interface that is capable of reading and classifying the input data into the output cluster.
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