Comparison of KNN and Random Forest Algorithms on E-Commerce Service Chatbot
DOI:
https://doi.org/10.14421/jiska.2025.10.1.100-109Keywords:
Chatbot, E-Commerce, NLP, KNN, Random ForestAbstract
Technology heavily influences our lives, with the expansion of e-commerce being an important outcome that demands attention. Given the prevalence of smartphones equipped with messaging apps and fast networks, people often utilize these platforms to communicate with sellers, offering a convenient way for sellers to engage efficiently with a diverse customer base. Recognizing this trend, there is a need for digital transformation of services to improve operational efficiency. Thus, this study aimed to compare the efficiency of classification algorithms in e-commerce service chatbots. The researcher used machine learning techniques with KNN and Random Forest algorithms in this case. To assess the feasibility of the application, the chatbot results will be tested using the confusion matrix method to assess accuracy. From this study, it was obtained that the KNN method and calculating word weight using TF-IDF produces an accuracy value of 71.4%, thus confirming its feasibility.
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