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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Nadiyah Nur Rafifah
Universitas Singaperbangsa Karawang
Computer Science & IT

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Analisis Sentimen Ulasan Skintific Aqua Light Daily Sunscreen di Tokopedia Menggunakan InSet Lexicon Nadiyah Nur Rafifah; Mohammad Jajuli; Sofi Defiyanti
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7210

Abstract

Customer reviews on e-commerce platforms can be utilized to understand consumer perceptions of a product through sentiment analysis. This study aims to analyze the sentiment of customer reviews for Skintific Aqua Light Daily Sunscreen on the Tokopedia platform using the lexicon-based method with the InSet Lexicon and to evaluate its classification performance. The study applies the Knowledge Discovery in Databases (KDD) framework, which consists of data selection, data preprocessing, data transformation, data mining, and evaluation. The data were collected through web scraping using Python. Of the 672 reviews collected, 634 reviews remained after the data cleaning process. The preprocessing stage included case folding, text normalization, tokenization, stopword removal, and stemming. The results showed that 532 reviews (83.91%) were classified as positive, 78 reviews (12.30%) as negative, and 24 reviews (3.79%) as neutral. The evaluation was conducted on 100 randomly selected review samples validated by two validators. The inter-rater agreement, measured using Cohen's Kappa, was 0.34, indicating a Fair Agreement. Furthermore, the evaluation using a confusion matrix yielded an accuracy of 64.00%, a precision of 54.71%, a recall of 64.00%, and an F1-score of 58.42%. These results indicate that the lexicon-based method using the InSet Lexicon is capable of classifying most customer review sentiments, although it still has limitations in identifying neutral sentiment.
Co-Authors Mohammad Jajuli Sofi Defiyanti