Penerapan Data Mining Untuk Prediksi Harga Kayu Manis Menggunakan Algoritma Regresi Linear Sederhana ( Studi Kasus : Usaha Dagang Munang ) - Aris Putra (Riset Sinergi Indonesia (RISINDO), 2025)
The development of information technology has enabled more accurate data analysis methods in the business sector, one of which is through data mining techniques. This research aims to predict cinnamon prices using the Simple Linear Regression algorithm at Usaha Dagang Munang. The main problem faced by traders is the unpredictable price fluctuation caused by various external factors such as market demand, weather conditions, and currency exchange rates. Using historical price data from 2015 to 2025, this study applies the Simple Linear Regression method to develop a data-driven prediction model. The analysis process was carried out using RapidMiner software, which produced a regression equation with a positive coefficient value of 1172.727, indicating an upward trend in price each year. The results show that the Simple Linear Regression algorithm can provide accurate price estimations and serve as a useful reference for farmers and traders in determining sales strategies. The application of data mining has proven effective in supporting data-based decision-making in the agricultural sector, particularly in the cinnamon commodity market.