Analisis sentimen terhadap elektabilitas bakal calon presiden Indonesia 2024 menggunakan algoritma Support Vector Machine

Iswandi, Sandi (2023) Analisis sentimen terhadap elektabilitas bakal calon presiden Indonesia 2024 menggunakan algoritma Support Vector Machine. Sarjana thesis, Uin Sunan Gunung Djati Bandung.

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Abstract

INDONESIA : Pemilihan umum adalah inti dari proses demokrasi dalam suatu negara, dimana masyarakat memiliki kesempatan untuk memilih pemimpin mereka. Dalam era digital saat ini, media sosial telah menjadi platform penting yang memengaruhi opini publik dan interaksi politik. Analisis sentimen, terutama menggunakan algoritma SVM, telah digunakan secara luas untuk mengolah dan menganalisis data dari media sosial terkait pemilihan umum. Dalam hal ini bagaimana mengimplementasikan algoritma SVM dalam pengklasifikasian analisis sentimen terhadap elektabilitas bakal calon presiden Indonesia 2024, bagaimanakah akurasi algoritma SVM dalam pengklasifikasian analisis sentimen terhadap elektabilitas bakal calon presiden Indonesia 2024 serta bagaimanakah hasil analisis sentimen terhadap elektabilitas bakal calon presiden Indonesia 2024. Proses penelitian ini dapat diarahkan oleh metodologi Cross-Industry Standard Process for Data Mining (CRISP-DM), pengujian yang dilakukan dengan menggunakan data test dan data traning, dari hasil pengujian nilai algoritma SVM diambil nilai akurasi tertinggi dari data setiap bakal calon presiden yaitu 63%, 75%, 82% diman rata-rata hasil akurasi pemodelan dari data anies baswedan yaitu 60,6%, data ganjar pranowo yaitu 70,3% dan data prabowo subianto yaitu 78,6%. ENGLISH : General elections are the core of the democratic process in a country, where people have the opportunity to choose their leaders. In today's digital era, social media has become an important platform that influences public opinion and political interactions. Sentiment analysis, especially using the SVM algorithm, has been widely used to process and analyze data from social media related to general elections. In this case, how to implement the SVM algorithm in classifying sentiment analysis on the electability of the 2024 Indonesian presidential candidates, what is the accuracy of the SVM algorithm in classifying sentiment analysis on the electability of the 2024 Indonesian presidential candidates and what are the results of sentiment analysis on the electability of the 2024 Indonesian presidential candidates. The process of this research can be directed by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, testing is carried out using test data and training data, from the results of testing the SVM algorithm values, the highest accuracy value is taken from the data for each presidential candidate, namely 63%, 75 %, 82% where the average modeling accuracy results from Anies Baswedan data is 60.6%, Ganjar Pranowo data is 70.3% and Prabowo Subianto data is 78.6%.

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