ANALISIS SENTIMEN DAN IDENTIFIKASI TOPIK TWITTER TERKAIT KONTROVERSI RUU KUHP

WIJAYA, RIZKY FEBRIO (2024) ANALISIS SENTIMEN DAN IDENTIFIKASI TOPIK TWITTER TERKAIT KONTROVERSI RUU KUHP. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The Criminal Code Bill (RUU KUHP) has sparked heated debates within society, particularly on social media platforms such as Twitter. This research aims to conduct sentiment analysis and topic modeling on conversations related to the RUU KUHP using the VADER algorithm for sentiment analysis and the Latent Dirichlet Allocation (LDA) method to identify key topics discussed. The dataset utilized comprises a large number of tweets discussing issues surrounding the RUU KUHP. Through the VADER approach, this study accurately evaluates sentiments expressed in tweets, categorizing them as positive, negative, or neutral regarding the RUU KUHP. Meanwhile, employing the LDA method successfully clusters and identifies primary topics emerging from these discussions, offering an in-depth insight into the prominent issues concerning the RUU KUHP on Twitter. The research findings indicate that VADER is able to identify sentiments, while LDA can categorize topics. This combined approach provides crucial insights into public opinion and widely-discussed topics on social media platforms like Twitter, aiding in understanding the dynamics and perceptions surrounding the RUU KUHP. Keywords: Sentiment Analysis, Topic Modeling, RUU KUHP, Twitter, VADER, Latent Dirichlet Allocation (LDA). Rancangan Undang-Undang Kitab Undang-Undang Hukum Pidana (RUU KUHP) menjadi perdebatan hangat di masyarakat, terutama di platform media sosial seperti Twitter. Penelitian ini bertujuan untuk melakukan analisis sentimen dan topic modelling terhadap percakapan yang terkait dengan RUU KUHP menggunakan algoritma VADER untuk analisis sentimen dan metode Latent Dirichlet Allocation (LDA) untuk mengidentifikasi topik utama yang dibahas. Dataset yang digunakan mencakup sejumlah besar tweet yang membahas isu terkait RUU KUHP. Dengan menggunakan pendekatan VADER, penelitian ini mampu mengevaluasi sentimen dari tweet, mengkategorikan antara sentimen positif, negatif, atau netral terkait RUU KUHP. Sementara itu, melalui metode LDA, penelitian dapat mengelompokkan dan mengidentifikasi topik-topik utama yang muncul dari percakapan, memberikan gambaran mendalam mengenai isu-isu yang menjadi fokus perbincangan terkait RUU KUHP di Twitter. Pendekatan gabungan ini memberikan wawasan yang penting terkait opini publik dan topik yang sedang diperbincangkan secara luas di platform media sosial seperti Twitter, membantu dalam memahami dinamika dan persepsi terhadap RUU KUHP. Kata Kunci: Analisis Sentimen, Topic Modelling, RUU KUHP, Twitter, VADER, Latent Dirichlet Allocation (LDA).

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 021
Call Number:	SIK/15/24/019
NIM/NIDN Creators:	41520010108
Uncontrolled Keywords:	Analisis Sentimen, Topic Modelling, RUU KUHP, Twitter, VADER, Latent Dirichlet Allocation (LDA)
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 > 003 Systems/Sistem-sistem > 003.1 System Identification/Identifikasi 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 > 006 Special Computer Methods/Metode Komputer Tertentu > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social 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 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis 900 Geography and History/Sejarah, Geografi dan Disiplin Ilmu yang Berkaitan > 920 Biography, Obituary/Biografi, Obituari > 929 Genealogy, names, insignia > 929.9 Forms of Identifications/Bentuk-bentuk Identifikasi
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	02 Feb 2024 05:05
Last Modified:	02 Feb 2024 05:05
URI:	http://repository.mercubuana.ac.id/id/eprint/85743

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