Lelianto Eko Pradana
Universitas Indonesia

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

Sentiment Analysis of Twitter Users to the PeduliLindungi Using Naïve Bayes Algorithm Lia Ellyanti; Yova Ruldeviyani; Lelianto Eko Pradana; Andro Harjanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4684

Abstract

Covid-19 was declared as a pandemic by World Health Organization (WHO) in March 2020, has a major impact on the lives. Indonesian’s government has made several efforts to suppress the spread of the virus by requiring the societies to use PeduliLindungi in every activity. There are many pros and cons from the societies in using PeduliLindungi, many reviews about the performance of this application found through playstore, app store or social media. Twitter is one of social media that allows the societies to express their feeling, idea, opinion, or critics about any topics. This study takes the review of PeduliLindungi from Twitter with period from June up to December 2021, which has the highest cases of covid-19 and tighter movement restriction from the government. The data collected were manually labeling into positive and negative class and processed using sentiment analysis with Naïve Bayes algorithm, give the result 64.69% positive sentiment and 35.5% negative sentiment regarding PeduliLindungi. The model tested using Naïve Bayes algorithm with 10-fold cross validation has the highest performance, the accuracy obtained is 95.86%, with precision 96.99% and recall 94.12%. The positive sentiment indicates the pro expression from society, like the data integration with vaccine certificate, PCR or antigen result, that makes the activities to entry public transport or public space easily. The negative sentiment indicates the cons expression from the societies, related with the performance of the application and the data security. The result of this study expected being reference, give insight, and information for developers and governments to build a better strategy in improving the performance of PeduliLindungi application.
Sentiment Analysis of Nanovest Investment Application Using Naive Bayes Algorithm Lelianto Eko Pradana; Yova Ruldeviyani
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62302

Abstract

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.