Classification of Broiler Production Success Rate using Algorithm Support Vector Machine (SVM)

Abstract

Broiler production is one of the fastest growing livestock sectors in Indonesia. Assessing the success rate of broiler production is very important for farmers in evaluating performance and optimizing yields. However, the manual evaluation process often leads to subjectivity and potential errors. This study aims to develop a classification model of the success rate of broiler production using the Support Vector Machine algorithm based on three main indicators, namely chicken age, chicken weight, and feed amount. This research also implements the classification model into an interactive web application using the Streamlit framework built with the Python programming language. The data was obtained from Timan Farm and went through a normalization process and data division into training and test data. The results of model testing show that the Support Vector Machine algorithm is able to classify the success rate of production with sufficient accuracy. The web-based application developed with Streamlit allows users to perform classification automatically, quickly, and accurately without having to have an in-depth technical background.