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All Journal KLIK: Kajian Ilmiah Informatika dan Komputer
Sanggir Dyah Fritama
Sekolah Tinggi Teknologi Wastukancana, Purwakarta
Computer Science & IT

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Analisis Sentimen Review Produk Acne Spot Treatment di Female Daily Menggunakan Algoritma K-Nearest Neighbor Sanggir Dyah Fritama; Yudhi Raymond Ramadhan; Mutiara Andayani Komara
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1070

Abstract

Acne, besides having a negative impact on the skin, also has a psychological impact, namely embarrassment and reduced self-confidence, which can be prevented by providing medical knowledge to treat acne. One of the products used for acne treatment is acne spot treatment. The large number of advertisements on social media related to acne spot treatment products can influence larger purchases, but the product is not necessarily good. Seeing direct reviews from acne spot treatment users can increase information regarding whether the product is good or not. One website that provides reviews related to beauty products is Female Daily, but the large number of reviews does not allow reading in its entirety. Of course, this takes time. Sentiment analysis is used to overcome this problem, which aims to classify acne spot treatment reviews as Positive or negative. In this study, the classification method used is K-Nearest Neighbor because it has a simple concept that is easy to apply and understand. The sentiment results showed that the reviews that contained the most positive sentiments were Whitelab with 1,190 reviews, and the ones that contained the most negative sentiments were Skin Games with 173 reviews. The results of the classification using KNN are that the best accuracy is the Whitelab brand at 97%, then the Skin Game gets 81% accuracy, and the lowest accuracy value is the ERHA brand at 75%
Co-Authors Mutiara Andayani Komara Ramadhan, Yudhi Raymond