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All Journal Bulletin of Electrical Engineering and Informatics PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Building of Informatics, Technology and Science Jurnal Manajemen Teknologi dan Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING
Istoningtyas, Marrylinteri
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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Journal : Bulletin of Electrical Engineering and Informatics

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Public complaint tweet data feature analysis for sentiment classification Rasywir, Errissya; Pratama, Yovi; Irawan, Irawan; Istoningtyas, Marrylinteri
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7172

Abstract

The perception of the public regarding a government's performance significantly impacts a city's advancement. This research involved analyzing complaint tweets from Jambi City residents directed at the government to gauge sentiment. In the testing phase, 500 Twitter accounts were examined to categorize sentiment as positive, negative, or neutral. Training data was prepared by extracting tokens through feature selection techniques such as information gain (IG) and mutual information (MI). For testing, all tokens are entered as data in the input layer in the recurrent neural network (RNN). From the tests carried out, the average use of feature selection can achieve a good value compared to no feature selection. But more specifically the use of IG produces better accuracy compared to the use of MI. From the research conducted, Twitter data is classified using a RNN and several tests by adding feature selection to produce differences. The results are proven to improve classification performance. With a recall value of 92.243%, it shows the system's success rate in sentiment classification and a precision of 92% indicates a level of accuracy that is sufficient to support the government's sentiment assessment.
Co-Authors ., SHARIPUDDIN Borroek, Maria Rosario Donzon, Allbet Errissya Rasywir Fachruddin Fachruddin Irawan Irawan Mulyanto, Ade Pahlevi, M. Riza Rambe, Lio Nauli Sabariah ., Sabariah Salwa, Shakira Yaasin, Muhammad Yani, Serli Putri Andri Yovi Pratama