Micro, Small, and Medium Enterprises (MSMEs) are vital to Indonesia's economic growth, contributing significantly to GDP and employment. As digital technologies advance, integrating digital solutions into MSME operations becomes crucial for sustained growth. Challenges like limited internet access and insufficient infrastructure hinder full digitization, with financial management standing out as a critical concern due to its potential impact on MSME viability.
In today's business landscape, optimizing online customer experience, harnessing big data's potential, conducting sentiment analysis, and implementing topic modeling have emerged as pivotal strategies. Customer experience encompasses various dimensions affecting loyalty and competitiveness. Leveraging the volume, variety, velocity, veracity, and value of big data offers a competitive advantage. Sentiment analysis extracts insights from user-generated content, while topic modeling uncovers prevalent themes efficiently. These concepts reshape decision-making, customer relations, and business strategies, becoming cornerstones for thriving in the digital age.
The research targets user reviews on BukuWarung, MoneyLover, and Kledo applications, using purposive sampling for relevant data selection. Leveraging secondary data, especially User Generated Content (UGC), serves as a valuable source for comprehending user sentiments. Data preprocessing involves techniques like case folding, cleaning, stemming, and stopword removal. Performing sentiment analysis using the Naïve Bayes algorithm, allows evaluating user sentiment in reviews, aiding the recommendation of the most fitting application for MSMEs. This research process promises to yield valuable insights into customer experiences and preferences, aiding MSMEs in their application selection.
Keywords: MSMEs, Financial Management, big data, Sentiment Analysis, Topic Modeling, Digital Solutions