Application of Naive Bayes Algorithm to Analysis of Free Fatty Acid (FFA) Production Based on Fruit Freshness Level

Abstract

Cooking oil is a basic need for everyone who is used to process food ingredients. The use of cooking oil repeatedly and continuously by heating at high temperatures can increase the free fatty acid levels in the oil. The more the oil is reused, the higher the free fatty acid content. Testing the levels of FFA in oil can be done using the FFA test, because FFA can affect the selling price of CPO when it is marketed. In addition, FFA affects the levels of free fatty acids of CPO. This study aims to determine the analysis of FFA production in palm oil products based on the level of freshness of the fruit. The research was conducted by classifying data mining using the Naïve Bayes Algorithm. The Naïve Bayes algorithm was used to determine whether FFA production had an effect on fruit freshness, fruit quality and fruit soiling. The research was conducted using RapidMiner Studio 9.10 tools. The results of the research from the distribution table show that the value of the FFA attribute obtained 2 conditions, namely super conditions and normal conditions. Where each of these attributes is influenced by the variables of fruit freshness and fruit quality. Probability accuracy results from 60 training data and 40 testing data used are 92.50% for super FFA conditions.

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“…Data mining is a series of processes to extract added value from a collection of data in the form of knowledge that has not been known manually [5] [5]. Data mining analyzes a large amount of observational data sets and discovers unexpected relationships and summarizes data so that it is easily understood by users [1] [8]. Data mining is a field that consists of various disciplines that integrate machine learning, pattern recognition, statistics, databases, and visualization [7] [9].…”