ANALISA SENTIMEN MENGENAI CGV PADA TWITTER MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE

FADLI, MUHAMAD (2024) ANALISA SENTIMEN MENGENAI CGV PADA TWITTER MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

CGV Cinemas is a cinema network in Indonesia that offers a new concept to provide a different experience when watching films. CGV Cinemas opened its first cinema chain at Paris Van Java Mall, Bandung. The presence of CGV Cinemas eliminates the impression of monopoly that existed in the cinema business network in Indonesia because it was previously dominated by Cinema 21 which had previously been successful in the cinema market in Indonesia. In Indonesia itself, CGV was founded in 2006 by Ananda Siregar and David Hilman. This research will use the Naïve Bayes algorithm and Support Vector Machine, where this algorithm is the Naïve Bayes Classifier (NBC) which is a classification method that is rooted in Bayes' theorem. The method used is the Naïve Bayes classification algorithm and Support Vector Machine assisted by RapidMiner and Python tools. Experimental results show that the Support Vector Machine algorithm provides the highest accuracy value, namely 92% for automatic labeling and 87% for manual labeling using the Naïve Bayes algorithm. Keywords: CGV, Biokop CGV, Naïve Bayes, Support Vector Machine, Sentiment, Algorithm CGV Cinemas merupakan salah satu jaringan bioskop di Indonesia yang menawarkan konsep baru untuk memberikan pengalaman yang berbeda saat menonton film. CGV Cinemas membuka jaringan bioskop pertamanya di Paris Van Java Mall, Bandung. Kehadiran CGV Cinemas menghilangkan kesan monopoli yang terjadi dalam jaringan bisnis bioskop di Indonesia karena sebelumnya didominasi oleh Cinema 21 yang telah lebih dahulu sukses dalam pasar sinema di Indonesia. Di Indonesia sendiri CGV didirikan pada tahun 2006 oleh Ananda Siregar dan David Hilman. Pada penelitian ini akan menggunakan algoritma Naïve Bayes dan Support Vector Machine, yang dimana algoritma ini adalah Naïve Bayes Classifier (NBC) merupakan sebuah metode klasifikasi yang berakar pada teorema Bayes. Metode yang digunakan adalah algoritma klasifikasi Naïve Bayes dan Support Vector Machine dengan dibantu oleh tools RapidMiner dan Python. Hasil eksperimen menunjukkan bahwa algoritma Support Vector Machine memberikan nilai akurasi paling tinggi yaitu 92% untuk labeling otomatis dan 87% untuk labeling manual menggunakan algoritma Naïve Bayes. Kata kunci: CGV, Bioskop CGV, Naïve Bayes, Support Vector Machine, Sentimen, Algoritma

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 028
NIM/NIDN Creators:	41518010051
Uncontrolled Keywords:	CGV, Bioskop CGV, Naïve Bayes, Support Vector Machine, Sentimen, Algoritma
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 > 006 Special Computer Methods/Metode Komputer Tertentu > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social > 006.754 Online Social Network/Situs Jejaring Sosial, Sosial Media 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 512 Algebra/Aljabar > 512.5 Linear, Multilinear, Multidimensional Algebra/Aljabar Linear, Multilinear, Aljabar Multidimensional > 512.52 Vector Spaces/Ruang Vektor 700 Arts/Seni, Seni Rupa, Kesenian > 790 Recreational and Performing Arts/Olah Raga dan Seni Pertunjukan > 791 Public Performances/Pertunjukan Umum > 791.4 Motion Pictures, Radio, Television/Gambar Gerak, Radio, Televisi > 791.43 Motion Pictures, Movies, Cinema/Bioskop, Movie, Film Hiburan, Sinema
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	07 Feb 2024 03:19
Last Modified:	07 Feb 2024 03:19
URI:	http://repository.mercubuana.ac.id/id/eprint/85921

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