KLASIFIKASI TINGKAT KEPUASAN PELANGGAN SAT & SUN : THE ALMEATY SERVICE MENGGUNAKAN NAIVE BAYESKLASIFIKASI TINGKAT KEPUASAN PELANGGAN SAT & SUN : THE ALMEATY SERVICE MENGGUNAKAN NAIVE BAYES

Abstract

Sat & Sun: The Almeaty Service, a semi-cafe with a retro pop theme located in Surabaya, faces challenges in acquiring and effectively utilizing customer satisfaction data to enhance service and product development. To address this, a study was conducted to analyze customer sentiment based on their opinions. Using Python software, sentiment analysis was performed with classification using Naive Bayes. From a survey involving 1020 respondents, the results indicated the satisfaction levels of customers visiting Sat & Sun: The Almeaty Service using Multinomial Naive Bayes with an 80:20 split. The findings revealed that customer satisfaction with vehicle access (Q1) was classified with 80% accuracy, parking facilities (Q2) with 75%, cleanliness of the area (Q3) with 78%, staff service (Q4) with 76%, and product quality (Q5) with 76% accuracy. These insights aim to guide improvements in service delivery and product offerings to better meet customer expectations and enhance overall customer experience at Sat & Sun: The Almeaty Service.