dc.contributor.advisor | Ardiansyah, Firman | |
dc.contributor.advisor | Haryanto, Toto | |
dc.contributor.author | Ansari, Aysuka | |
dc.date.accessioned | 2024-07-12T06:55:04Z | |
dc.date.available | 2024-07-12T06:55:04Z | |
dc.date.issued | 2024 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/153582 | |
dc.description.abstract | Pada era internet saat ini, trailer umumnya diunggah melalui platform media
sosial seperti YouTube. Melalui platform ini, penonton dapat mengakses serta
memberikan sentimen mereka terhadap trailer film dengan mudah. Analisis
sentimen terhadap komentar trailer film perlu dilakukan agar diperoleh trailer yang
disukai oleh penonton sehingga minat penonton untuk menonton film tersebut dapat
meningkat. Penelitian ini bertujuan mengembangkan model Natural Language
Processing (NLP) dengan metode word embedding FastText dan arsitektur Gated
Recurrent Unit (GRU) untuk analisis kecenderungan sentimen komentar di dalam
trailer film Oppenheimer. Berdasarkan hasil pelatihan dan evaluasi diperoleh model
TextBlob VADER sebagai model terbaik dengan accuracy bernilai 0,93. Hasil
analisis kecenderungan sentimen pada masing-masing trailer film menggunakan
model ini menghasilkan pengamatan bahwa trailer 2 lebih diminati penonton
dibandingkan trailer 1. Selain itu, penelitian ini juga telah berhasil mengembangkan
aplikasi web app sederhana untuk membantu proses analisis sentimen. | |
dc.description.abstract | In today's internet era, trailers are commonly uploaded on social media
platforms like YouTube where viewers can easily access them and express their
opinions. Sentiment analysis for a movie trailer is needed in order to create a trailer
that is liked by the viewer so that it can increase the viewer’s interest in watching
the movie. This research aims to develop a Natural Language Processing (NLP)
model using the FastText word embedding method and Gated Recurrent Unit
(GRU) architecture for analyzing sentiment tendencies in comments on the
Oppenheimer’s movie trailers. Based on the training and evaluation results, the
TextBlob VADER model was found to be the best model with an accuracy of 0,93.
The sentiment trend analysis on each movie trailer using this model showed that
trailer 2 was more favored by viewers compared to trailer 1. Additionally, this
research successfully developed a simple web app to assist in the sentiment analysis
process. | |
dc.description.sponsorship | | |
dc.language.iso | id | |
dc.publisher | IPB University | id |
dc.title | Analisis Sentimen Komentar Trailer Film Oppenheimer Pada YouTube Menggunakan GRU dan FastText Embedding | id |
dc.title.alternative | Sentiment Analysis of Oppenheimer Movie Trailer Comments on YouTube using GRU and FastText Embedding | |
dc.type | Skripsi | |
dc.subject.keyword | analisis sentimen | id |
dc.subject.keyword | fasttext word embedding | id |
dc.subject.keyword | gated recurrent unit | id |
dc.subject.keyword | trailer film | id |
dc.subject.keyword | youtube | id |