Surislamputra, Rizky Rahmatullah (2025) ANALISIS SENTIMEN PENGARUH AI (ARTIFICIAL INTELLIGENCE) TERHADAP MASYARAKAT MENGGUNAKAN ALGORITMA RANDOM FOREST BERBASIS SMOTE. S1 thesis, Universitas Mercu Buana-Menteng.
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Abstract
Perkembangan teknologi Artificial Intelligence (AI) telah membawa dampak signifikan terhadap kehidupan masyarakat di berbagai bidang, termasuk pendidikan, industri, dan sosial. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap pengaruh AI menggunakan algoritma Random Forest dan metode SMOTE (Synthetic Minority Over-sampling Technique) untuk mengatasi ketidakseimbangan data. Data yang digunakan berasal dari media sosial Twitter (X) (X) sebanyak 4500 tweet, yang kemudian melalui tahapan preprocessing, pelabelan otomatis menggunakan IndoBERT, serta transformasi fitur menggunakan TF-IDF dan BoW. Hasil klasifikasi sentimen dibagi menjadi dua kategori: positif dan negatif. Evaluasi model menunjukkan bahwa kombinasi Random Forest dan TF-IDF dengan penerapan SMOTE menghasilkan akurasi tertinggi sebesar 76,10%, dibandingkan model tanpa SMOTE maupun dengan metode BoW. Penelitian ini menunjukkan bahwa SMOTE efektif dalam meningkatkan performa model klasifikasi pada data tidak seimbang. Hasil penelitian ini diharapkan dapat menjadi referensi dalam pengembangan sistem pemantauan opini publik terhadap AI serta memberikan kontribusi dalam bidang Natural Language Processing dan kecerdasan buatan di Indonesia. The advancement of Artificial Intelligence (AI) technology has significantly impacted society in various sectors, including education, industry, and social life. This study aims to analyze public sentiment towards the influence of AI using the Random Forest algorithm and the SMOTE (Synthetic Minority Over-sampling Technique) method to address data imbalance. The dataset comprises 4500 tweets collected from Twitter (X) (X), processed through several stages such as preprocessing, automatic labeling using IndoBERT, and feature transformation using TF-IDF and Bag of Words (BoW). Sentiment classification results were divided into two categories: positive and negative. The evaluation shows that the combination of Random Forest and TF-IDF with SMOTE achieved the highest accuracy of 76.10%, compared to models without SMOTE or using BoW. This study demonstrates that SMOTE effectively improves classification performance on imbalanced datasets. The results are expected to serve as a reference for developing AI-based public opinion monitoring systems and contribute to the fields of Natural Language Processing and artificial intelligence research in Indonesia.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41520010197 |
| Uncontrolled Keywords: | Artificial Intelligence, Sentimen, Random Forest, SMOTE, TF-IDF, IndoBERT Artificial Intelligence, Sentiment, Random Forest, SMOTE, TF-IDF, IndoBERT |
| 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 > 006 Special Computer Methods/Metode Komputer Tertentu 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 |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | ZAIRA ELVISIA |
| Date Deposited: | 13 Sep 2025 06:48 |
| Last Modified: | 13 Sep 2025 06:48 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/97820 |
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