ANALISIS SENTIMEN KOMENTAR PENGGUNA DI TWITTER TERHADAP LAYANAN INDIHOME MENGGUNAKAN ALGORITMA NAIVE BAYES

RIZKY, ZICO CAHYA (2025) ANALISIS SENTIMEN KOMENTAR PENGGUNA DI TWITTER TERHADAP LAYANAN INDIHOME MENGGUNAKAN ALGORITMA NAIVE BAYES. S1 thesis, Universitas Mercu Buana Jakarta.

Preview	Text (HAL COVER) 01 COVER.pdf Download (532kB) | Preview
	Text (BAB I) 02 BAB 1.pdf Restricted to Registered users only Download (268kB)
	Text (BAB II) 03 BAB 2.pdf Restricted to Registered users only Download (435kB)
	Text (BAB III) 04 BAB 3.pdf Restricted to Registered users only Download (349kB)
	Text (BAB IV) 05 BAB 4.pdf Restricted to Registered users only Download (788kB)
	Text (BAB V) 06 BAB 5.pdf Restricted to Registered users only Download (258kB)
	Text (DAFTAR PUSTAKA) 07 DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (238kB)
	Text (LAMPIRAN) 08 LAMPIRAN.pdf Restricted to Registered users only Download (572kB)

Abstract

Sentiment analysis is an essential approach to understanding public opinion toward a service, including digital services such as IndiHome. This study aims to analyze user sentiment on Twitter regarding the IndiHome service using the Naive Bayes algorithm. The data was collected through a crawling process on the Twitter platform, resulting in 700+ user comments to be analyzed. The research method involves several stages, including data preprocessing, manual sentiment labeling, application of the Naive Bayes algorithm for sentiment classification (positive and negative), and model evaluation using a confusion matrix. The results of this study show that the Naive Bayes algorithm achieved an accuracy of 93.69% in classifying user comments. However, the model was only able to recognize negative sentiments optimally with a recall value of 100%, while positive sentiment recognition had a recall of 0%, indicating an imbalanced dataset. Nevertheless, these results still provide valuable insights into public opinion trends regarding the IndiHome service. Keywords: sentiment analysis, Naive Bayes, Twitter, IndiHome, text classification Analisis sentimen merupakan pendekatan penting dalam memahami opini publik terhadap suatu layanan, termasuk layanan digital seperti IndiHome. Penelitian ini bertujuan untuk menganalisis sentimen komentar pengguna di Twitter terhadap layanan IndiHome dengan menggunakan algoritma Naive Bayes. Data diperoleh melalui proses crawling pada platform Twitter, dengan jumlah sebanyak 700+ komentar yang dikumpulkan untuk dianalisis. Metode penelitian ini mencakup tahapan preprocessing data, pelabelan sentimen secara manual, penerapan algoritma Naive Bayes untuk klasifikasi sentimen positif dan negatif, serta evaluasi model menggunakan confusion matrix. Hasil dari penelitian menunjukkan bahwa algoritma Naive Bayes menghasilkan tingkat akurasi sebesar 93,69% dalam mengklasifikasikan komentar pengguna. Namun, model hanya mampu mengenali sentimen negatif secara optimal dengan nilai recall sebesar 100%, sedangkan untuk sentimen positif recall-nya sebesar 0%, yang menunjukkan adanya ketidakseimbangan distribusi data (imbalanced dataset). Meskipun demikian, hasil ini tetap memberikan gambaran yang bermanfaat dalam menganalisis opini publik secara umum terhadap layanan IndiHome. Kata Kunci: analisis sentimen, Naive Bayes, Twitter, IndiHome, klasifikasi teks

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 056
NIM/NIDN Creators:	41821110015
Uncontrolled Keywords:	analisis sentimen, Naive Bayes, Twitter, IndiHome, klasifikasi teks
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 > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 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 > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.35 Natural Language Processing/Pengolahan Bahasa Alami 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 300 Social Science/Ilmu-ilmu Sosial > 300. Social Science/Ilmu-ilmu Sosial > 303 Social Process/Proses Sosial > 303.3 Coordination and Control/Koordinasi dan Kontrol > 303.38 Public Opinion/Opini Publik 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 658 General Management/Manajemen Umum > 658.3 Personnel Management/Manajemen Personalia, Manajemen Sumber Daya Manusia, Manajemen SDM
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	11 Aug 2025 07:43
Last Modified:	11 Aug 2025 07:43
URI:	http://repository.mercubuana.ac.id/id/eprint/96769

Actions (login required)

View Item