Public Sentiment Analysis on OJK Regulations for Online Lending Using Natural Language Processing

Analisis Sentimen Masyarakat terhadap Peraturan OJK Tentang Pinjaman Online dengan Menggunakan Natural Language Processing

Abstract

Financial technology, or fintech, has quickly changed how lending works in Indonesia. It has made it easier for people to get money, but it has also brought up problems like illegal loans, unfair debt collection, and high interest rates. To deal with these issues, the Financial Services Authority, known as OJK, has put in place new rules to better protect consumers and improve how the industry is managed. Despite these efforts, people have different feelings about the rules, and most of their opinions are shared on social media. This study looks at how the public feels about OJK's rules on online lending using a method called Natural Language Processing, or NLP. The research collects social media posts through web scraping, uses Naïve Bayes and Support Vector Machine to classify sentiments, and applies Latent Dirichlet Allocation to find the main topics people are discussing. The study expects to find out how people feel overall, which issues are most talked about, and what influences positive or negative opinions. This research adds to academic discussions by offering a new way to analyze public sentiment focused on regulations, and it also helps policymakers create better rules for fintech lending in Indonesia.