Prediksi Potensi Penjualan Makanan Beku berdasarkan Ulasan Pengguna Shopee menggunakan Metode Decision Tree Algoritma C4.5 dan Random Forest (Studi Kasus Dapur Lilis) - Muhammad Ferdyandi; Nanang Yudi Setiawan; Fitra Abdurrachman Bachtiar (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
Dapur Kitchen is a micro-business that sells frozen foods in the form of Chicken Nuggets. Kitchen Lilis wants to add new products according to the interests of prospective buyers. However, Kitchen Lilis does not yet know the fact that prospective buyers want frozen food products like what. Therefore, it is necessary to conduct research on frozen food predictions based on data obtained on the Shopee page with frozen food category products. Data obtained a total of 140 data and made decision trees with data partitions 80% training data and 20% test data. The method used is Random Forest and applies the C4.5 Algorithm to each subtree formed. The rule model has an accuracy value of 0.73 for training data and 0.78 for test data. From the calculation of algorithm C4.5 manual use of preservatives has the most influence and from the rule model as many as three subtree, on the first rule of raw material quality, the second rule of the use of MSG, and the third rule of the use of preservatives. The results of the next classification are visualized into a dashboard and to predict products based on features can be executed on a prediction application. Both outputs from the study were tested using the System Usability Scale (SUS) and had a value of 90 which belonged to the category