DETEKSI EMOSI YOUTUBE LIVE CHAT MENGGUNAKAN DEEP LEARNING LONG SHORT-TERM MEMORY (LSTM)

JAGABAYA, MUHAMMAD ARIQ and Abdiansah, Abdiansah and Saputra, Danny Matthew (2022) DETEKSI EMOSI YOUTUBE LIVE CHAT MENGGUNAKAN DEEP LEARNING LONG SHORT-TERM MEMORY (LSTM). Undergraduate thesis, Sriwijaya University.

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Abstract

Live chat pada live streaming merupakan pesan-pesan yang tampil secara langsung bersamaan dengan video live streaming. YouTube yang merupakan penyedia layanan video streaming juga menyediakan fitur live streaming dengan live chat di dalamnya. Pada saat bersamaan pesan tersebut dapat dikategorikan dalam beberapa emosi. Beberapa emosi pada penelitian kali ini adalah bingung, lucu, marah, sedih dan suka. Penelitian ini mendukung agar Streamer dapat meningkatkan kualitas video live streamingnya dengan pendeteksian live chat ini sebagai salah satu pertimbangannya. Penelitian ini akan membahas tentang aplikasi pendeteksian emosi pada live chat menggunakan deep learning long short-term memory (LSTM) yang sudah cukup sering digunakan untuk analisis sentimen dan juga pendeteksian emosi. Setelah dilakukan validasi silang dengan himpunan data latih didapatkan hasil akurasi rata-rata sebesar 73% dengan rata rata waktu kompilasi sebesar 72 detik. Dengan menggunakan LSTM didapatkan hasil akurasi sebesar 83% dengan waktu kompilasi dalam pembuatan model selama 75 detik pada himpunan data uji dimana cukup bagus untuk pendeteksian emosi live chat ini.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Deteksi Emosi, deep learning, long short-term memory, YouTube Live Chat
Subjects: P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Muhammad Ariq Jagabaya
Date Deposited: 20 May 2022 08:41
Last Modified: 20 May 2022 08:41
URI: http://repository.unsri.ac.id/id/eprint/70278

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