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All Journal KLIK: Kajian Ilmiah Informatika dan Komputer
Rizki Adi Saputra
Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta
Computer Science & IT

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Analisis Sentimen Aplikasi Tokocrypto Berdasarkan Ulasan Pada Google Play Store Menggunakan Metode Naïve Bayes Rizki Adi Saputra; Dion Parisda Ray; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1707

Abstract

The development of increasingly sophisticated technology has made many changes and conveniences for humans in all things, including in the financial sector, cryptocurrency has emerged as a form of innovation in the world of finance. A cryptocurrency exchange is an electronic means for carrying out crypto asset trading transactions by sellers or buyers via websites or applications on mobile devices. Currently, many cryptocurrency exchange applications have poor service, security is not guaranteed, withdrawals take quite a long time, expensive admin fees, and other problems, so many Indonesians use reviews on Google Play Store to see user reviews before deciding to use the cryptocurrency exchange application. Many Indonesians are looking for information on cryptocurrency exchange applications that provide the best service for carrying out cryptocurrency buying and selling transactions. One of the cryptocurrency exchange applications that offers this is the Tokocrypto application based on reviews on the Google Play Store. This research aims to understand sentiment towards user reviews on the Tokocrypto application using the Naïve Bayes algorithm method for data classification. The data obtained is review data from February 2024, totaling 2000 reviews from the Google Play Store using Google Collaboratory. This research stage consists of data scraping using web scraping techniques, data labeling, preprocessing, TF-IDF weighting, implementation of the Naïve Bayes algorithm, and evaluation. The clean data obtained was 1000 with a total of 396 positive sentiments and 604 negative sentiments. The results of the sentiment analysis research using the Naïve Bayes algorithm method resulted in an accuracy level of 74.22%, precision of 63.25%, and recall of 81.40%.
Analisis Sentimen Ulasan Aplikasi Samsat Digital Nasional Pada Google Playstore Menggunakan Algoritma Naïve Bayes Deni Wijaya; Rizki Adi Saputra; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1738

Abstract

Digital transformation has become a major factor of change in various aspects of modern life, including business, education, and government. In the current era of digital transformation, the government is trying to improve efficiency and services to the community through the implementation of various technological innovations. The application of digital technology in public services is increasingly widespread, including in the administrative service sector such as the National Digital Samsat (SIGNAL) which allows people to make online vehicle tax payments through the SIGNAL application. User evaluations of this application can provide important insights for service providers. This research aims to analyze the sentiment of user reviews of the National Digital Samsat application on the Google Playstore platform using the Naïve Bayes algorithm. This method is used to classify user reviews into positive and negative sentiment categories. From 2000 reviews taken, 1,665 reviews were categorized as positive and 335 reviews as negative after manual labeling. Data preprocessing using RapidMiner includes cleaning, transform cases, tokenizing, stopword filter, token by length filter, and stemming. TF-IDF weighting is used to give weight to each word in the document. Evaluation of the Naïve Bayes model resulted in an accuracy of 63.61%, with 307 True Positives, 74 True Negatives, 26 False Positives, and 192 False Negatives. Precision was 92.19% and recall was 61.52%. The overall analysis shows that user reviews tend to be more positive towards the SIGNAL app, although there are some negative reviews. This conclusion gives an idea of users' positive perception of the app.
Co-Authors Deni Wijaya Dion Parisda Ray Faldy Irwiensyah