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Application Random Forest Method for Sentiment Analysis in Jamsostek Mobile Review
Abstract
Purpose: This study aims to monitor the service quality of JMO applications from time to time by classifying JMO user reviews into the class of positive, neutral, and negative sentiments.
Design/methodology/approach : The method used in this study is the random forest classification method. Data processing in this study uses feature extraction, TF-IDF and labeling with the lexicon-based method.
Findings/result: Based on the research results, it was found that the highest frequency of classification was the positive class with 17571 reviews compared to the neutral class with 8701 reviews and the negative class with 3876 reviews with an accuracy evaluation value of 93%, precision 88%, recall 93%, and f1-score 90%.
Originality/value/state of the art:
This study uses 150737 reviews that have been pre-processed using the random forest method and TF-IDF and lexicon-based feature extraction.
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DOI: https://doi.org/10.31315/telematika.v20i1.8868
DOI (PDF): https://doi.org/10.31315/telematika.v20i1.8868.g5404
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Status Kunjungan Jurnal Telematika