Analisis Sentimen Masyarakat Indonesia Terhadap Saham Goto Group Pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier Dan Support Vector Machine

Prasetya, Ichwanul Kahfi (2022) Analisis Sentimen Masyarakat Indonesia Terhadap Saham Goto Group Pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier Dan Support Vector Machine. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perkembangan teknologi informasi yang pesat di era digital saat ini, khususnya media sosial, telah mengubah pola komunikasi masyarakat. Salah satu platform yang paling banyak digunakan adalah Twitter, di mana pengguna dapat dengan bebas membagikan opini, gagasan, dan perasaan mereka mengenai berbagai hal, termasuk topik keuangan seperti saham. Saham Goto Group, sebagai salah satu perusahaan teknologi terbesar di Indonesia, menjadi topik yang sering dibahas di Twitter. Analisis sentimen terhadap saham ini penting dilakukan untuk memahami persepsi dan kecenderungan opini publik yang dapat mempengaruhi nilai saham. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap opini masyarakat Indonesia mengenai saham Goto Group di Twitter dengan membandingkan kinerja dua metode klasifikasi, yaitu Naive Bayes Classifier dan Support Vector Machine (SVM). Data yang digunakan berupa tweet yang dikumpulkan dari Twitter. Proses analisis meliputi tahapan preprocessing data, pelabelan sentimen (positif, negatif, dan netral), pembobotan kata menggunakan TF-IDF, serta klasifikasi menggunakan Naive Bayes dan SVM. Berdasarkan hasil pengujian, metode Support Vector Machine (SVM) memberikan akurasi yang lebih tinggi dibandingkan dengan Naive Bayes Classifier dalam mengklasifikasikan sentimen masyarakat terhadap saham Goto Group.
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The rapid development of information technology in the current digital era, especially social media, has changed people's communication patterns. One of the most widely used platforms is Twitter, where users can freely share their opinions, ideas, and feelings about various things, including financial topics such as stocks. Goto Group stocks, as one of the largest technology companies in Indonesia, are a topic that is frequently discussed on Twitter. Sentiment analysis of these stocks is important to understand the perceptions and trends of public opinion that can affect stock values. This study aims to conduct sentiment analysis of Indonesian public opinion regarding Goto Group stocks on Twitter by comparing the performance of two classification methods, namely Naive Bayes Classifier and Support Vector Machine (SVM). The data used is in the form of tweets collected from Twitter. The analysis process includes data preprocessing stages, sentiment labeling (positive, negative, and neutral), word weighting using TF-IDF, and classification using Naive Bayes and SVM. Based on the test results, the Support Vector Machine (SVM) method provides higher accuracy compared to the Naive Bayes Classifier in classifying public sentiment towards Goto Group stocks.

Item Type:	Thesis (Other)
Additional Information:	RSSt 519.53 Pra a-1 2022
Uncontrolled Keywords:	GOTO, Naive Bayes Classifier, Sentiment, Support Vector Machine, Twitter. GOTO, Naive Bayes Classifier, Sentimen, Support Vector Machine, Twitter.
Subjects:	H Social Sciences > HA Statistics
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	10 Jun 2026 03:37
Last Modified:	10 Jun 2026 03:37
URI:	http://repository.its.ac.id/id/eprint/133681

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