ANALISIS SENTIMEN TWITTER UNTUK MENENTUKAN KEPRIBADIAN SESEORANG BERDASARKAN MODEL THE BIG FIVE PERSONALITY TRAITS DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES

SHOLECH, KHAIRUL (2024) ANALISIS SENTIMEN TWITTER UNTUK MENENTUKAN KEPRIBADIAN SESEORANG BERDASARKAN MODEL THE BIG FIVE PERSONALITY TRAITS DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This research implements the Naïve Bayes algorithm to analyze a person’s personality based on Twitter data according to The Big Five Personality Traits with influential Twitter users in indonesia with a total data of 500 tweets per user. Crawling data prod uces 50.024 tweets and after data becomes 46.028 tweets. The next step by appyling the Term Frequency Frequency- Inverse Document Frequency (TF TF-IDF) weighting technique and conducting NRC -Lexicon analysis, this research succeeded in extracting sentiment and emotion information contained in each tweet. The combination of the two methods is the basis for determining the label on the personality dimension that refers to the OCEAN (Openness, Conscienciousness, Extraversion, Agreeableness, Neuroticism). The Naïve Bayes al gorithm was used for classification, achieving the highest accuracy of 75.56% on a test dataset with a 90:10 ratio. Model evaluation showed improved performance as the size of the test dataset increased, emphasizing the importance of larger dataset to impr ove classification accuracy. This research provides insights for further research and practical applications in understanding personality dynamics derived from social media. Keywords : Naïve Bayes algorithm , The Big Five Personality Traits, NRC NRC- Lexicon, Sentiment Analysis Analysis, TF -IDF. Penelitian ini mengimplementasikan algoritma Naïve Bayes untuk menganalisis kepribadian seseorang berdasarkan data Twitter sesuai dengan The Big Five Personality Traits dengan pengguna Twitter yang berpengaruh di Indonesia dengan masing masing-masing 500 tweet per per-pengguna. Melalui proses Crawling data menghasilkan 50.02 50.024 tweets dan setelah melalui PrePre-processing data menjadi 46.028 tweet. Langkah selanjutnya dengan menerapkan teknik pembobotan Term Frequ encyency-Inverse Document Frequency (TF TF-IDF) dan melakukan analisis NRC NRC-Lexicon, penelitian ini berhasil menggali informasi sentimen dan emosi yang terkandung dalam setiap tweet. Kombinasi kedua metode ini menjadi dasar penentuan label pada dimensi kepribadian yang mengacu pada model OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism). Algoritma Naïve Bayes digunakan untuk klasifikasi, mencapai akurasi tertinggi 75.56% pada dataset uji dengan perbandingan 90:10. Evaluasi model menunjuk kan peningkatan performa seiring dengan pertumbuhan ukuran dataset uji, menekankan pentingnya dataset yang lebih besar untuk meningkatkan ketepatan klasifikasi. Penelitian ini memberikan wawasan mendalam untuk riset lebih lanjut dan aplikasi praktis dalam memahami dinamika kepribadian yang diperoleh dari media sosial. Kata Kunci : Algoritma Naïve Bayes Bayes, The Big Five Personality Traits Traits, NRCNRC- LexiconLexicon, Analisis Sentimen, TF TF-IDFIDF.

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 031
Call Number:	SIK/15/24/024
NIM/NIDN Creators:	41519120068
Uncontrolled Keywords:	Algoritma Naïve Bayes Bayes, The Big Five Personality Traits Traits, NRCNRC- LexiconLexicon, Analisis Sentimen, TF TF-IDFIDF.
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 > 006 Special Computer Methods/Metode Komputer Tertentu 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 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 155 Differential and Developmental Psychology/Psikologi Diferensial dan Psikologi Perkembangan > 155.2 Individual Psychology, Characters/Psikologi Individual, Karakter > 155.23 Traits and Determinants of Character and Personality/Ciri dan Faktor Penentu Karakter dan Kepribadian 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 155 Differential and Developmental Psychology/Psikologi Diferensial dan Psikologi Perkembangan > 155.2 Individual Psychology, Characters/Psikologi Individual, Karakter > 155.25 Character Development/Perkembangan Kepribadian, Perkembangan Karakter 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	12 Feb 2024 03:05
Last Modified:	12 Feb 2024 03:05
URI:	http://repository.mercubuana.ac.id/id/eprint/85958

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