Application of Data Mining to analyze the Best Suppliers - Haryanto, Hendrie; Sarif, Muhammad Irfan; Hardinata, Rio Septian (Bahasa Indonesia, 2024)
This research explores the application of data mining methods with the Naive Bayes algorithm to analyze and determine the best suppliers in supply chain management. Optimal supplier selection plays an important role in increasing company efficiency and competitiveness. The aim of this research is to develop an analytical model that is not only accurate but also effective in evaluating supplier performance based on available historical data. The data used covers a variety of critical attributes, including price, product quality, delivery times, and contract compliance from several suppliers. The research process involves data preprocessing steps such as cleaning, normalization, and dividing the data into training and test sets. The Naive Bayes method is applied to identify patterns and relationships in data, as well as produce predictions regarding supplier performance. The model is evaluated using accuracy, precision, recall, and F1-score metrics to measure the reliability and accuracy of predictions. The analysis results show that the Naive Bayes method can predict supplier performance with a high level of accuracy and good consistency. These findings provide a valuable tool for companies in data-based decision making for better supplier selection. This research concludes that the Naive Bayes method is an excellent method for supplier performance analysis, offering a superior combination of accuracy and effectiveness. Recommendations for further research include the application of other data mining techniques and integration with supply chain management information systems to improve overall analysis results.