| Abstract |
The article herein details a procedure for classifying service cases by priority level based on the service level agreement (SLA) between an organization and the customer. The main factor in the article's publication was the accuracy of the classification of the importance of internal service work. However, many service evaluators remain confused about the tiering of service cases. Therefore, creating accurate service case classification models is imperative to simplify the classification process. The service cases consisted of four levels: series, critical, moderate, and low. We employed natural language processing (NLP) to develop a more efficient priority level of service for the organization. We implemented the weighting of the term frequency – inverse document frequency (TF-IDF) method and cosine Similarity with the measuring degree concept of similarity terms within each service case. The model consisted of four processes: data collection, preprocessing, TF-IDF calculation, and similarity and scoring calculation. The model presented here improved the accuracy of the classified process and produced better results in the test sets, measuring the efficiency from the cosine similarity. Lastly, our research contained 5,790 service cases with an accuracy of 70.14%, achieved through the combination of TF-IDF and cosine similarity. |
Publication
Journal Publication
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