Implementasi Metode Monte Carlo Untuk Memprediksi Permintaan Produk Mebel Pada CV. Yoss Sindanglaut - Faisal Akbar; Faizal Anwar; Susi Widyastuti (INFOKOM, 2023)
One of the problems experienced by entrepreneurs is unpreparedness when the demand for goods is higher than the existing supply. To overcome this problem, a demand prediction application is needed by analyzing data on goods sales transactions that have occurred so that a conclusion can be drawn about predicting the number of requests in the future. This study aims to create a system for calculating demand predictions and the number of web-based automatic sales by applying the Monte Carlo algorithm. The Monte Carlo algorithm is also known as simulated sampling or a sampling technique using a simulation model that includes taking random values ​​and calculating the probability distribution of the data to produce predictive data with a high percentage of similarity. This research was conducted on CV. Yoss Sindanglaut. The results of this study use different data to make predictions, namely sales data for one week, one month, and the previous two months. The result is the CV monte carlo method. Yoss Sindanglaut can predict the demand for furniture products as many as 14 requests for April 2022 with a profit of 4,900,000 and an error value of 12.5%.