ANALISA SENTIMEN MEDIA SOSIAL DENGAN SUPPORT VECTOR MACHINE (SVM) UNTUK MENDUKUNG KEPUTUSAN DIGITAL MARKETING PADA HILABI KITCHEN

SONIA AZAHRA SAHIB, . (2025) ANALISA SENTIMEN MEDIA SOSIAL DENGAN SUPPORT VECTOR MACHINE (SVM) UNTUK MENDUKUNG KEPUTUSAN DIGITAL MARKETING PADA HILABI KITCHEN. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

ABSTRAK Perkembangan pesat media sosial sebagai sarana komunikasi dan informasi telah membuka peluang besar dalam bidang digital marketing. Salah satu tantangan dalam pemanfaatan media sosial adalah memahami dan mengelola sentimen pengguna yang tersebar dalam volume data yang sangat besar. Penelitian ini bertujuan untuk menganalisis sentimen pengguna media sosial X terhadap digital marketing menggunakan algoritma Support Vector Machine (SVM), serta memberikan rekomendasi yang dapat digunakan dalam pengambilan keputusan pemasaran oleh Hilabi Kitchen. Penelitian dilakukan dengan metode eksperimen berbasis simulasi menggunakan bahasa pemrograman Python melalui Google Collaboratory. Data diperoleh dengan teknik scraping dari platform X dan dilakukan tahapan praproses data teks (case folding, cleansing, tokenizing, stopword removal, stemming, normalization), pembobotan dengan TF-IDF, dan klasifikasi menggunakan algoritma SVM. Hasil penelitian menunjukkan bahwa model SVM mampu mengklasifikasikan sentimen tweet secara efektif dengan tingkat akurasi yang tinggi. Penerapan metode ini mampu memberikan pemahaman lebih mendalam terhadap opini publik dan membantu perusahaan dalam menyusun strategi pemasaran digital yang lebih responsif dan tepat sasaran. Kata Kunci: Analisis Sentimen, Support Vector Machine (SVM), Pemasaran Digital ***** ABSTRACT The rapid development of social media as a means of communication and information has opened up significant opportunities in the field of digital marketing. One of the challenges in utilizing social media is understanding and managing user sentiment that is dispersed across a vast volume of data. This research aims to analyze the sentiment of social media users on platform X towards digital marketing using the Support Vector Machine (SVM) algorithm, as well as to provide recommendations that can be used in marketing decision-making by Hilabi Kitchen. The research was conducted using a simulation-based experimental method with the Python programming language through Google Colaboratory. Data was obtained using scraping techniques from platform X and underwent text data preprocessing stages (case folding, cleansing, tokenizing, stopword removal, stemming, normalization), weighting with TF-IDF, and classification using the SVM algorithm. The research results show that the SVM model is capable of effectively classifying tweet sentiments with a high level of accuracy. The application of this method is capable of providing a deeper understanding of public opinion and assisting companies in formulating more responsive and targeted digital marketing strategies. Keywords: Sentiment Analysis, Support Vector Machine (SVM), Digital Marketing

Item Type:	Thesis (Sarjana)
Additional Information:	1. Murien Nugraheni, M.CS 2. Ali Idrus, S.KOM.,M.KOM
Subjects:	Teknologi dan Ilmu Terapan > Teknik Komputer
Divisions:	FT > S1 Sistem dan Teknologi Informasi
Depositing User:	Sonia Azahra Sahib .
Date Deposited:	06 Aug 2025 07:02
Last Modified:	06 Aug 2025 07:02
URI:	http://repository.unj.ac.id/id/eprint/58378

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