Classification of Chicken Egg Price Patterns at Partner Farms Using the C4.5 Method - Kristin Lourensi Sitompul; Petti Indrayati Sijabat; Puteri Fajar Addini; Risda Sariah Cristina Siringo-ringo; Syahnaila Mayza Kirana (STMIK Methodist Binjai, 2026)
This study aims to classify chicken egg price patterns at Sunardi Farm from July 2021 to August 2025 using a data mining method based on the C4.5 algorithm. Changes in chicken egg prices, which are influenced by various factors—including production volume, feed costs, and major holiday periods—often create uncertainty for farmers when making decisions regarding production and marketing. The research data consisted of 50 historical data points comprising several price-determining attributes and grouped into two classes: Medium and High. The C4.5 algorithm was applied by building a decision tree model based on the gain ratio to identify the attributes with the most significant influence on price changes. The results indicate that production volume is the primary factor influencing chicken egg price patterns, with the number of major holidays serving as a supporting factor. The resulting classification model achieved an accuracy rate of 80%, indicating that the C4.5 algorithm is quite effective in classifying chicken egg price patterns. The resulting decision rules are expected to serve as a basis for decision-making regarding production planning, sales strategies, and distribution management at Sunardi Farm.