IMPLEMENTATION OF THE BEST TRAINING PRODUCT SALES RECOMMENDATION SYSTEM USING COSENI SIMILARITY ALGORITHM

Abstract

Introduction: Sales of training products often encounter difficulties in providing the right recommendations according to customer needs and preferences. This research aims to develop a Content-Based Filtering-based recommendation system to address this challenge, especially for PT Menara Indonesia. Method: This research focuses on the Cosine Similarity algorithm, this research designs and implements the model through Django's REST API and Streamlit's Dashboard Website. The data used consists of tabular data that includes company profiles and product models. The Content-Based Filtering method is used to improve sales efficiency and support marketing strategies. Results and Discussion: The results show that the implementation of the Cosine Similarity algorithm provides training product recommendations that are suitable and relevant to customer needs. The suitability and relevance of the recommendation results are marked by the similarity between the two items that have been combined to produce a value of 10% to 100% which has been sorted according to the recommendation results. 10% is a recommendation result that is not very similar to the input needs and 100% is the most appropriate and accurate recommendation with the input needs. Conclusion: With the use of Natural Language Processing technology, this system can produce more accurate and relevant recommendations. This research is expected to help PT Menara Indonesia in increasing sales