Advertising

Artificial intelligence

Islam

Archaeology

Business

Usury

Geography

Data pre-processing

Operating system

Data mining

Computer science

Process (computing)

Actuarial science

Preprocessor

Sentiment analysis

JURNAL MEDIA INFORMATIKA BUDIDARMA

This study aims to formulate public opinion about bank interest included in the category of usury or not. The method used in this study is the analysis of usury sentiments on bank interest using Twitter data with the K-NN algorithm. Sentiment analysis using the K-NN algorithm gives good results. Evidenced by testing 170 twitter dataset using the K-NN algorithm obtained an accuracy of ± 70.59%. Assisted by the preprocessing process which aims to erase unnecessary parts and also change the form of documents in the form of tweets to a standard form so that classification can be carried out, so that the results of usury sentiment analysis on bank interest can clarify assumptions in the community and serve as a reference in determining appropriate banking products to the needs of customers

Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter