Analisis Data Produksi Biskuit Dengan Algoritma Naive Bayes Dan Random Forest - Sabarrudin; Agung Budi Susanto; Sajarwo Anggai (Universitas Pamulang, 2024)
In the manufacturing industry, production problems often occur, often production does not match market demand, production is not well planned, therefore this study aims to develop a classification model using machine learning based on the Naive Bayes and Random Forest algorithms to classify biscuit production data. The main focus of this study is to utilize variables such as dough, number of mixers, production time parameters, and other relevant production factors to improve accuracy in classification. The dataset used in this study includes information from several previous production periods, namely data in 2019-2023, which is then used to train and test the Naive Bayes and Random Forest algorithm models. The training and validation process is carried out using commonly used model performance evaluation techniques. The results of the study show that the Random Forest model is able to provide high accuracy, namely 97.54% while Naive Bayes is 96.45%. Further analysis was also carried out to identify the variables that most influence production results, providing additional insights for optimizing the production process. The results of this study can contribute to the development of classification models for the food and beverage industry, especially in biscuit products, but also offer a more specific view of the factors that influence biscuit production. The implementation of this study can be a basis for manufacturers to make more precise and effective decisions in managing their production.