ANALISIS SENTIMENT TWITTER KULIAH ONLINE PASCA COVID-19 MENGGUNAKAN ALGORITMA KNN DAN NAIVE BAYES

FAUZAN, NUR (2023) ANALISIS SENTIMENT TWITTER KULIAH ONLINE PASCA COVID-19 MENGGUNAKAN ALGORITMA KNN DAN NAIVE BAYES. S1 thesis, Universitas Mercu Buana.

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Abstract

World Health Organization (WHO) COVID-19 is an infectious disease caused by a coronavirus which originally originated from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that has occurred in many countries around the world. This disease caused the government to give a regional lockout status (lockdown) to students and students to implement online or online lectures, this caused various sentiments given by students in response to online lectures via Twitter social media. For sentiment analysis, the researcher applied the Naïve Bayes and K-Nearest Neighbor (KNN) algorithms with the performance results obtained for the Naïve Bayes algorithm with accuracy of 0.72, precision of 0.81, recall of 0.81 and f1-score of 0.81 while for the KNN algorithm, accuracy of 0.85, precision of 1.00, recall 0.93 and f1-score 0.90. Keywords: Sentiment analysis, Online Lecture, Naïve Bayes, KNN, Online Learning World Health Organization (WHO) COVID-19 merupakan penyakit menular yang disebabkan oleh Coronavirus yang awal mulanya berasal dari wabah di kota Wuhan, Tiongkok pada bulan Desember 2019 yang kemudian menjadi pandemi yang terjadi di banyak negara di seluruh Dunia. Penyakit ini menyebabkan pemerintah memberikan status penguncian daerah (lockdown) memberikan status "dirumahkan" terhadap pelajar dan mahasiswa untuk memberlakukan kuliah online atau daring, hal ini menyebabkan berbagai sentimen yang diberikan oleh mahasiswa dalam menanggapi kuliah online lewat sosial media twitter. Untuk analisis sentimen peneliti menerapkan algorima naïve bayes dan K-Nearest Neighbor (KNN) dengan hasil peforma yang didapat pada algoritma naïve bayes akurasi 0.72, presisi 0.81, recall 0.81 dan f1-score 0.81 sedangkan untuk algoritma KNN mendapatkan nilai akurasi 0.85, presisi 1.00, recall 0.93 dan f1-score 0.90. Katakunci : Analisis sentiment, Kuliah Online, Naïve Bayes, KNN, Belajar Online

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 187
NIM/NIDN Creators:	41519010175
Uncontrolled Keywords:	Analisis sentiment, Kuliah Online, Naïve Bayes, KNN, Belajar Online
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:	03 Nov 2023 06:32
Last Modified:	03 Nov 2023 06:32
URI:	http://repository.mercubuana.ac.id/id/eprint/83783

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