Measuring the Maturity Level of Oil Palm Fruit For CPO Production Based on Color With Using the LVQ Method
DOI:
https://doi.org/10.59934/jaiea.v3i1.303Keywords:
CPO (Crude Palm Oil), Palm Oil, LVQ (Learning Vector Qualization).Abstract
Palm oil is a very important commodity besides oil and gas which also has a fairly good export value. The palm oil produced must be supported by the quality standards set by SNI. The level of maturity when harvesting oil palm fruit greatly influences the quality of Crude Palm Oil (CPO) production, which is crude palm oil that has a reddish color obtained from extraction or from the pressing process of oil palm fruit flesh. In fact, in the field of oil palm fruit harvest, there are still many oil palm fruit that are not ripe enough and can even be said to be still raw, entering the CPO production process. Determination of the maturity level of oil palm fruit is generally determined based on the amount of loose fruit and color, so handling the harvest of oil palm fruit is an important activity in improving the quality of CPO. It is necessary to build a system capable of managing and processing palm fruit images to measure the maturity level of the palm fruit to be produced. To obtain the right level of accuracy, this research uses the Learning Vector Quantization (LVQ) method. LVQ is a method for conducting supervised competitive layer learning. From the results of trials conducted, it is proven that the system can measure very ripe oil palm fruit with HSV values (0.052209; 0.896021; 0.791114).
Downloads
References
Sanjaya, S. (2019). Application of Learning Vector Quantization in Classifying Levels of Ripeness of Tomatoes Based on Fruit Color. CoreIT Journal: Journal of Computer Science and Information Technology Research Results, 5(2), 49.
Luthfi Khalid, Jayanta, YW (2020). Implementation of Learning Vector Quantization (Lvq) for Natural Ripe Mango Identification Model. 606–621.
Jepriana, SH & IW (2020). The Concept of Algorithms and Their Applications in the C++ Programming Language. CV. Andi Offset, Yogyakarta.
Usman, W., Damanik, IS, & Hardinata, JT (2020). Artificial Neural Network with Learning Vector Quantization (LVQ) Method in Determining Classification of Types of Tickets Based on Vehicles. Proceedings of the National Seminar on Information Science Research (SENARIS), 1(September), 780.
Dlh.probolinggokab.go.id. (2023).
BW, TA, Hermanto, IGR, & D, RN (2009). Introduction to Balinese Letters Using Modified Direction Feature (MDF) and Learning Vector Quantization (LVQ) Methods. National Conference on Systems and Informatics 2009, 7–12.
Cakra Adipura Wicaksana, DETL (2021). Recognition of Digital Image Signature Vector Patterns Using Regional Division and Learning Vector Quantization (LVQ) Methods. Journal of Control and Network Systems, 5(2), 1–13.
Jepriana, SH & IW (2020). The Concept of Algorithms and Their Applications in the C++ Programming Language. CV. Andi Offset, Yogyakarta.
Nurhayati. (2022). Ensemble Learning Techniques to Increase Prediction Model Accuracy Performance (Selection of Scholarship Recipient Students). Pascal Books, Tangerang.
Rachmat Destriana, Syepry Maulana Husain, Nurdiana Handayani, ATPS (2021). UML Diagram in Creating Firebase Android Applications "Case Study of Garbage Bank Applications. Deepublish Publisher, Sleman.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Artificial Intelligence and Engineering Applications (JAIEA)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.