ANALISIS SENTIMEN PADA DASHBOARD KINERJA BISNIS MENGGUNAKAN POWER BI
DOI:
https://doi.org/10.5281/zenodo.22255058Keywords:
Analisis Sentimen, CRISP-DM, Naive Bayes, Power BI, Ulasan PelangganAbstract
This study aims to evaluate customer satisfaction with the delivery services of PT Tiki Jalur Nugraha Ekakurir (JNE) through the MyJNE application. The main problem faced is the large number of unstructured text reviews on the Google Play Store platform, making it difficult for companies to monitor customer satisfaction trends. To overcome this, this study uses a text mining method with the Naive Bayes algorithm to classify user sentiment into positive, neutral, and negative categories. The CRISP-DM (Cross-Industry Standard Process for Data Mining) framework is applied, covering business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data extracted through web scraping is processed using Term Frequency-Inverse Document Frequency (TF-IDF) before being trained into the model. The results showed that the Naive Bayes model has an accuracy rate of 95.87% in classifying sentiments. The majority of reviews show negative sentiment (94.43%) related to technical application issues and delivery delays. The classification results are then visualized in the form of an interactive dashboard using Microsoft Power BI which displays word clouds, monthly sentiment trends, and satisfaction proportions. This dashboard is expected to assist JNE management in making data-driven decisions to continuously improve service quality and user experience.
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