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All Journal Journal of Computer System and Informatics (JoSYC) Journal of Computer Science and Informatics Engineering Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Media Teknik Elektro dan Komputer
Mesanda, Zery
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Journal : Jurnal Media Teknik Elektro dan Komputer

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Analisis Sentimen Komentar Intagram Pemindahan Ibu Kota Negara Membandingkan Alogritma Support Vecthor Meachine dan Random Forest Mesanda, Zery; Sitompul, Boy Arnol
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 1 (2025): Metrokom : Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i1.59

Abstract

Social media sentiment analysis has become an important approach in understanding public opinion on strategic issues, including the discourse on the relocation of the national capital. This study aims to compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms in classifying the sentiment of public comments on Instagram. A total of 794 comment data were collected using web scraping techniques with Selenium and BeautifulSoup, then divided into 80% training data and 20% test data. The classification process was conducted after the text preprocessing stage, which included case folding, tokenizing, filtering, and stemming. The experimental results show that SVM achieved an accuracy of 75.0% with precision 0.7200, recall 0.7800, and F1-score 0.7488. Meanwhile, Random Forest performed better with an accuracy of 79.4%, precision of 0.7795, recall of 0.8200, and F1-score of 0.7992. Evaluation based on sentiment class shows that SVM can only achieve a correct rate of 75.0% in the positive class and 75.1% in the negative class, while Random Forest excels with 79.4% in the positive class and 79.3% in the negative class. These findings confirm that Random Forest is more optimal and consistent than SVM in sentiment analysis based on social media comments. This study recommends the use of ensemble learning algorithms such as Random Forest in similar studies, as well as further development with larger datasets and deep learning approaches to improve model accuracy and generalization.
Co-Authors Muhammad Ikhsan Siahaan , Ahmad Taufik Al Afkari Sitompul, Boy Arnol Taufik Al Afkari Siahaan, Ahmad