ANALISIS SENTIMEN TERHADAP ISU ARTIFICIAL INTELLIGENCE DI TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN RANDOM FOREST (RF)

NAVIDKYA, ABRIEL (2025) ANALISIS SENTIMEN TERHADAP ISU ARTIFICIAL INTELLIGENCE DI TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN RANDOM FOREST (RF). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Artificial Intelligence (AI) has become an increasingly discussed topic on various social media platforms, including Twitter. The increasing number of public opinions regarding AI has given rise to the need for sentiment analysis to understand public perception in more depth. This study aims to conduct sentiment analysis on Twitter tweets related to the issue of Artificial Intelligence using two classification algorithms, namely Support Vector Machine (SVM) and Random Forest (RF). This research method involves the process of collecting tweet data using the Twitter API, then continued with text preprocessing stages such as case folding, tokenization, stopword removal, and stemming. The data is then labeled with sentiment (positive, negative, neutral) manually or semi-automatically for supervised learning needs. After going through the vectorization process with TFIDF, the SVM and RF models are trained and tested to compare their performance in sentiment classification. The results show that both algorithms are able to classify with a good level of accuracy, but the Support Vector Machine algorithm provides slightly superior performance than Random Forest in terms of precision and f1-score. These findings suggest that SVM can be a more efficient algorithm for short text-based sentiment analysis tasks such as tweets. This research is expected to contribute to monitoring public opinion and become a basis for decision-making related to AI-based technology issues. Keywords: Artificial Intelligence, Sentiment, Twitter, Support Vector Machine, Random Forest, Text Analysis. Artificial Intelligence (AI) telah menjadi topik yang semakin sering diperbincangkan di berbagai platform media sosial, termasuk Twitter. Meningkatnya jumlah opini publik terkait AI memunculkan kebutuhan akan analisis sentimen untuk memahami persepsi masyarakat secara lebih mendalam. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap cuitan Twitter yang berkaitan dengan isu Artificial Intelligence menggunakan dua algoritma klasifikasi, yaitu Support Vector Machine (SVM) dan Random Forest (RF). Metode penelitian ini melibatkan proses pengumpulan data tweet menggunakan Twitter API, kemudian dilanjutkan dengan tahap preprocessing teks seperti case folding, tokenisasi, stopword removal, dan stemming. Data kemudian diberi label sentimen (positif, negatif, netral) secara manual maupun semi-otomatis untuk kebutuhan supervised learning. Setelah melalui proses vektorisasi dengan TF-IDF, model SVM dan RF dilatih dan diuji untuk membandingkan performa keduanya dalam klasifikasi sentimen. Hasil penelitian menunjukkan bahwa kedua algoritma mampu melakukan klasifikasi dengan tingkat akurasi yang baik, namun algoritma Support Vector Machine memberikan performa yang sedikit lebih unggul dibanding Random Forest dalam hal presisi dan f1-score. Temuan ini menunjukkan bahwa SVM dapat menjadi algoritma yang lebih efisien untuk tugas analisis sentimen berbasis teks singkat seperti tweet. Penelitian ini diharapkan dapat memberikan kontribusi dalam pemantauan opini publik serta menjadi dasar dalam pengambilan keputusan terkait isu-isu teknologi berbasis AI. Kata kunci: Artificial Intelligence, Sentimen, Twitter, Support Vector Machine, Random Forest, Analisis Teks.

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 131
NIM/NIDN Creators:	41521010128
Uncontrolled Keywords:	Artificial Intelligence, Sentimen, Twitter, Support Vector Machine, Random Forest, Analisis 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 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 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 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	07 Aug 2025 03:59
Last Modified:	07 Aug 2025 03:59
URI:	http://repository.mercubuana.ac.id/id/eprint/96639

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