Comparison of Machine Learning Classification Algorithms in Sentiment Analysis Product Review of North Padang Lawas Regency

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Yennimar Yennimar Reyhan Achmad Rizal

Abstract

The growth of SMEs in Indonesia, which has increased by 6% every year, is driven by continued growth by many parties, including the government and private institutions that often conduct business coaching and assistance. Problems that are often encountered are the lack of willingness of MSME business practitioners to apply information technology and the internet, besides that most of them live in rural areas with very limited internet access and many are not yet digital-literate, adequate digital technology utilization capabilities and the will of business people For SMEs to understand customer needs, a service that is consistent with standard service procedures will give a good impression and pay attention to customer feedback. This research was conducted by collecting data on MSME products obtained from the North Padang Lawas District Trade Industry Office followed by the development of a Paluta Market website as a marketplace for media promotion and marketing of MSME products in North Padang Lawas by applying a sentiment analysis approach using machine learning classification algorithm to produce product rating values based on public opinion of MSME products contained on the website, in addition the system is able to classify consumer comment data on MSME products from various sources from the umkm web, so that it becomes useful information for MSME businesses especially in North Padang Lawas Regency and the community at large. The results of the application of sentiment analysis of a product on the Paluta Market website can be used as a reference in improving service and product quality, so as to create a variety of new opportunities that are profitable for MSME businesses.

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YENNIMAR, Yennimar; RIZAL, Reyhan Achmad. Comparison of Machine Learning Classification Algorithms in Sentiment Analysis Product Review of North Padang Lawas Regency. SinkrOn, [S.l.], v. 4, n. 1, p. 268-273, oct. 2019. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10416>. Date accessed: 08 dec. 2019. doi: https://doi.org/10.33395/sinkron.v4i1.10416.
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