Implementasi Metode CRISP DM dan Algoritma Decision Tree Untuk Strategi Produksi Kerajinan Tangan pada UMKM A - Asyraf, Haekal; Prasetya, Machmudin Eka (Universitas Budi Darma, 2024)
In industry 4.0, the utilization of information technology and data analysis is very important in helping business decision making. The use of data mining and the Decision Tree algorithm in analyzing data can help to classify products that best suit customer preferences for Micro, Small and Medium Enterprises (MSMEs) handicraft products. In this research we use the Cross Industry Standard Process for Data Mining methodology to analyze data in classifying the types of craft products produced by MSME A to determine production strategy that suits market demand after the Covid-19 pandemic. Covid-19 influenced MSME A as a handicraft producer to temporarily stop production due to decreased demand and produce a special order only. Changes in consumer behavior due to the Covid-19 pandemic mean that MSME A must determine the right production strategy so that products are sold and can reuse the capital. We succeeded in building a fairly effective model with CRISP-DM and the Decision Tree algorithm which has an accuracy rate of 74.2%. This model found as many as 21 types of products that were still selling well during the pandemic, making it useful for MSME A for making production decisions based on market conditions during the Covid-19 pandemic.