ANALISIS SENTIMEN E-SPORTS PADA TWITTER MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (KNN) DAN LOGISTIC REGRESSION

LESMANA, ZIKO SURYA (2023) ANALISIS SENTIMEN E-SPORTS PADA TWITTER MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (KNN) DAN LOGISTIC REGRESSION. S1 thesis, Universitas Mercu Buana.

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Abstract

Currently, the rise of online games is very attached to the community. The community is competing to learn about the many online games that have the opportunity to become prestigious competitions such as mobile legend online games, free fire, and so on. Consumer satisfaction can be seen through reviews as good track record information in the world of esport. These reviews are on social networks, one of which is Twitter. Sentiment analysis is usually used as a reference for a company in improving products and services based on customer feedback using AI technology. The algorithms used in this sentiment analysis are KNN and Logistic Regression. The results of the two algorithms are very good, by obtaining an f1-score result of ≥ 0.8 or 80% with the best accuracy value obtained by the Logistic Regression Algorithm of 94% while using the KNN Algorithm of 93%, this produces superior results obtained by the Algorithm Logistic Regression with a difference of 0.1%. Keywords:Sentiment Analysis; E-Sports; KNN; Logistics Regression; Twitter. Saat ini maraknya Game Online sudah sangat melekat dilingkungan masyarakat. Masyarakat berlomba lomba mempelajari banyaknya game online yang mempunyai peluang untuk dijadikan ajang perlombaan bergengsi seperti game online mobile lagend, free fire, dan lain sebagainya. Kepuasan konsumen dapat dilihat melalui ulasan sebagai informasi track record yang baik dalam dunia e-sport. Ulasan tersebut ada pada jejaring sosial salah satunya twitter. Analisis sentimen biasanya digunakan untuk acuan sebuah perusahaan dalam meningkatkan produk dan layanan berdasarkan umpan balik pelanggan menggunakan teknologi AI. Algoritma yang digunakan dalam analisis sentimen ini yaitu KNN dan Logistic Regression. Hasil kedua Algoritma sudah sangat baik, dengan mendapatkan nilai hasil f1-score ≥ 0.8 atau 80% dengan nilai hasil akurasi terbaik didapatkan oleh Algoritma Logistic Regression sebesar 94% sedangkan menggunakan Algoritma KNN sebesar 93%, hal tersebut menghasilkan hasil yang lebih unggul didapatkan oleh Algoritma Logistic Regression dengan hasil selisih 0.1%. Kata Kunci : Analisis Sentimen; E-Sports; KNN; Logistic Regression; Twitter.

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 23 204
NIM/NIDN Creators: 41518110233
Uncontrolled Keywords: Analisis Sentimen; E-Sports; KNN; Logistic Regression; Twitter.
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 > 003 Systems/Sistem-sistem
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 > 003 Systems/Sistem-sistem > 003.5 Computer Modeling and Simulation/Model dan Simulasi Komputer
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
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 > 004.1 General Works on Specific Types of Computers/Karya Umum tentang Tipe-tipe Khusus Komputer
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: CALVIN PRASETYO
Date Deposited: 08 Nov 2023 07:36
Last Modified: 08 Nov 2023 07:36
URI: http://repository.mercubuana.ac.id/id/eprint/84017

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