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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information System, Applied, Management, Accounting and Research JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Journal of Computer Science and Engineering (JCSE) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Publikasi Pengabdian Masyarakat Komputer dan Teknologi (PUNDIMASKOT) Jurnal Multidisiplin Pengabdian Masyarakat (JMPM)
Cahyani Budihartanti, Cahyani
Department of Information System, STMIK Nusa Mandiri
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Journal : Journal of Computer Science and Engineering (JCSE)

 1 
Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time Winanto, Adi; Budihartanti, Cahyani
Journal of Computer Science and Engineering (JCSE) Vol 3, No 1: February (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v3i1.249

Abstract

Currently, the Government is intensively utilizing social media, one of which is Twitter as a place of interaction with the community. The results of these interactions can be used as feedback to determine whether public opinion on public policies is positive or negative. Tweets from users can be a supporting parameter for the government in evaluating future policies and decision making by applying the sentiment analysis method. This study aims to determine positive or negative sentiments on user tweets against the official twitter account of the DKI Jakarta Provincial Government during the COVID19 vaccine period. The data obtained are 1658 lines from March 30 to April 5, 2021 with queries on tweets containing words or mentioning the username @dkijakarta, which will be grouped by sentiment class, namely negative and positive using the TF-IDF Vectorizer for word weighting and classification using several methods, namely, nave Bayes with accuracy values. 82.50% with class recall on positive sentiment 88% and negative 77% and in class precision showing positive at 79.28% and negative at 86.52% in the rapid miner application then k-NN with an accuracy value of 81.50% with class recall on positive sentiment 85% and negative 78% and class precision shows positive at 79.44% and negative at 83.87% in the rapid miner application. And the accuracy value of the best method in this training data classification comparison is nave Bayes, the results the end of testing the sample dataset using the nave Bayes method with 84.80% accuracy with class recall at 85.01% positive sentiment and 84.59% negative sentiment and at c lass precision shows positive at 85.21% and negative at 84.38% in rapid mining applications.
Analisa Penerimaan Pengguna e-wallet Sebagai Transaksi Digital Menggunakan Metode TAM (Technologi Acceptance Model) Siahaan, Heppy Rosalina; Budihartanti, Cahyani
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.28

Abstract

As technology advances and digital transactions continue to grow, many companies are now launching digital or cashless transaction applications. This allows users to be more selective in choosing a digital or cashless transaction application, also known as an e-wallet, that they want to use. Factors considered in this selection process include ease of use, offered features, transaction speed, security, and the number of merchants available. This research aims to provide users with information to choose a high-demand e-wallet by implement the Technology Acceptance Model (TAM) to calculate the selection of a high-demand digital wallet. Based on the data calculated using the TAM method and survey results that include appearance criteria, offered items, convenience, security, and the number of merchants, GO-PAY is considered the primary alternative for a digital wallet with a demand rate of 47.8%. OVO comes in second place with 31.1%, followed by DANA in the last position with 21.1%
Co-Authors Adi Winanto Aenuddin, Muhammad Hardithya Arief Rusman Ifaru, Cyrill Ilario INDAH PURNAMASARI Juliyati, Dwi Kresna Ramanda Laila Septiana, Laila Maulana, Cahyadi Anugrah Mohammad Badrul, Mohammad Pratama, Riky Putra Prayogi, Randi Rusdiansyah Rusdiansyah Setiawan, Eri Nur Siahaan, Heppy Rosalina siti nurlela Yumi Novita Dewi Zahra, Alfia