ANALISIS SENTIMEN MASYARAKAT TERHADAP KENAIKAN BAHAN BAKAR MINYAK PADA MEDIA SOSIAL YOUTUBE DENGAN METODE K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE

RAMADHAN, MUHAMMAD ZAIDAN (2025) ANALISIS SENTIMEN MASYARAKAT TERHADAP KENAIKAN BAHAN BAKAR MINYAK PADA MEDIA SOSIAL YOUTUBE DENGAN METODE K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana - Menteng.

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Abstract

Penelitian ini menggunakan metode machine learning, yaitu K-Nearest Neighbor dan Support Vector Machine dalam menganalisis sentimen masyarakat terhadap kenaikan bahan bakar minyak pada media sosial youtube . Tujuan dari penelitian ini yaitu untuk melihat pola tren terhadap persepsi masyarakat terhadap kenaikan bahan bakar minyak. Penelitian ini memberikan kontribusi dalam dunia data science dengan menggunakan machine learning, serta menjadi kajian untuk pemerintah dalam menaikkan harga bahan bakar minyak yang membuat masyarakat resah . Data yang digunakan untuk analisis ini sebanyak 1469 data terkait kenaikan bahan bakar minyak. Pada hasil analisis, model menunjukkan keakuratan sebesar 88%, recall 88%, precision 90% dan f1-score 85% untuk model K-nearest Neighbor dan untuk model SVM memiliki keakuratan sebesar 89%, recall 89%,f1-score 84% dan precision 90%. Hasil untuk pelabelan menunjukkan bahwa sebanyak 920 merupakan komentar negatif dan 123 komentar untuk positif. Analisis tersebut memberikan gambaran yang jelas mengenai opini atau sentimen masyarakat terhadap kenaikan bahan bakar minyak. This research uses machine learning methods, namely K-Nearest Neighbor and Support Vector Machine to analyze public sentiment towards the increase in fuel oil on YouTube social media. The aim of this research is to look at trend patterns in public perceptions of the increase in fuel oil. This research makes a contribution to the world of data science by using machine learning, as well as being a study for the government in raising fuel prices which makes people anxious. The data used for this analysis was 1469 data related to the increase in fuel oil. In the analysis results, the model shows an accuracy of 88%, recall 88%, precision 90% and f1- score 85% for the K-nearest Neighbor model and for the SVM model it has an accuracy of 89%, recall 89%, f1-score 84% and precision 90%. The results for labeling showed that 920 were negative comments and 123 were positive comments. This analysis provides a clear picture of public opinion or sentiment regarding the increase in fuel oil.

Item Type:	Thesis (S1)
NIM/NIDN Creators:	41520010202
Uncontrolled Keywords:	BBM, K-Nearest Neighbor, Support Vector Machine, Analisis Sentimen, Youtube. Fuel, K-Nearest Neighbor, Support Vector Machine, Sentiment Analysis, Youtube.
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
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	FHADHILAH SHAFA ARISTA
Date Deposited:	25 Feb 2025 03:49
Last Modified:	25 Feb 2025 03:49
URI:	http://repository.mercubuana.ac.id/id/eprint/94409

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