SENTIMENT ANALYSIS OF MRT JAKARTA USER SATISFACTION USING NAÏVE BAYES AND LEXICON BASED APPROACH

ARDIANSYAH, SAHRU (2020) SENTIMENT ANALYSIS OF MRT JAKARTA USER SATISFACTION USING NAÏVE BAYES AND LEXICON BASED APPROACH. S1 thesis, Universitas Mercu Buana Jakarta.

Text (JURNAL MAHASISWA)
jurnal_SahruArdiansyah_41516010142.pdf
Restricted to Registered users only
Download (893kB)

Abstract

Mass Rapid Transit (MRT) has become the new mode of transportation in Jakarta. It is expected to reduce traffic jams and pollution in Jakarta. MRT is a new thing in Jakarta that supports public transportation developed by the government. Therefore, the participation of society is needed to improve the facilities and services of MRT. MRT has an official account on Twitter, so MRT users can deliver their opinion through Twitter social media. Sentiment Analysis is needed to analyze public opinion towards MRT. In this case, the author uses Naïve Bayes as the algorithm in analyzing the satisfaction of MRT user and Lexicon based approach to data scoring and labeling. In this research, the author acquires the final result with a value of eighty percent with K-fold cross-validation with five for k value. Keywords: Naive Bayes, Sentiment analysis, rapid mass transit (MRT), Lexicon Based Approach Mass Rapid Transit (MRT) menjadi moda transportasi baru di Jakarta. Hal ini diharapkan dapat dapat mengurangi kemacetan dan polusi di Jakarta. Karena ini merupakan hal baru yang ada di masyarakat maka dibutuhkan partisipasi dari masyarakat dalam meningkatkan pelayanan dan fasilitas MRT. MRT mempunyai akun resmi di Twitter sehingga pengguna MRT dapat menyampaikan opininya melalui media sosial twitter. Analisis sentimen dibutuhkan untuk menganalisa opini masyarakat yang ditujukan kepada MRT. Dalam hal ini penulis menggunakan Naïve bayes sebagai algoritma dalam menganalisis kepuasan pengguna MRT dan Lexicon based approach digunakan dalam proses scoring dan pelabelan pada data. Pada penelitian ini penulis mendapatkan hasil akhir sebesar 80% dengan validasi K-fold cross validation dengan k-value = 5. Kata kunci: Naive Bayes, Sentiment analysis, rapid mass transit (MRT), Lexicon Based Approach

Item Type:	Thesis (S1)
Call Number CD:	JM/TI. 20 123
NIM/NIDN Creators:	41516010142
Uncontrolled Keywords:	Naive Bayes, Sentiment analysis, rapid mass transit (MRT), Lexicon Based Approach
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	Dede Muksin Lubis
Date Deposited:	29 Nov 2021 02:46
Last Modified:	23 Sep 2022 07:11
URI:	http://repository.mercubuana.ac.id/id/eprint/51000

Actions (login required)

View Item