Sentiment Analysis of Nanovest Investment Application Using Naive Bayes Algorithm - Lelianto Eko Pradana; Yova Ruldeviyani (Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI, 2023)
Various applications provide simple ways for individuals interested in investing in crypto assets or stocks - both domestic and international - to do so. One of the companies in this industry, Nanovest, has launched the Nanovest investment application. Since its release in 2022, numerous positive and negative responses have been on Google Play, the App Store, and Twitter. However, Nanovest faces two main problems regarding the use of its application. First, they often receive complaints submitted to the operational team, indicating dissatisfaction or problems faced by users. Second, Nanovest has never conducted formal research regarding user experience in using their application. This indicates a lack of understanding of the perspectives, needs and challenges faced by users. This study tries to find out how the public responds to the Nanovest application through a sentiment analysis. This study used tweet and review data from January 1, 2022, to February 17, 2023. The data underwent sentiment analysis, employing the Naïve Bayes algorithm, and were classified into positive and negative sentiments. The findings revealed that 96.07% of the sentiments expressed towards Nanovest were positive, while 22.11% were negative, with these percentages calculated based on the total number of sentiments detected in the data. To evaluate the model's performance, a 10-fold cross-validation approach was utilized alongside the Naïve Bayes algorithm, resulting in an impressive accuracy rate of 94.8391%. This positive sentiment suggests that users are highly favorable towards the crypto assets and global stock investment services offered by the Nanovest application. Nevertheless, 3.93% of users still expressed dissatisfaction with the app due to some flaws that existed when Nanovest was initially launched. Based on the results that have been obtained and analyzed for the development team, it is recommended to make three improvements, namely reducing application size to minimize memory usage, increasing overall application performance, and increasing access speed across all features to allow application users to access more efficiently. It is recommended for the product team and stakeholders to consider developing the Candlestick chart feature into the application. This also increases the competitiveness of the Nanovest application against other applications.