SENTIMEN ANALISIS MENGENAI POLUSI UDARA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST

DALIMUNTHE, MUHAMMAD VARIANSJAH (2024) SENTIMEN ANALISIS MENGENAI POLUSI UDARA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The term "air pollution" refers to the contaminating effects of chemical, physical, or biological contaminants on the atmosphere that alter its inherent characteristics in both indoor and outdoor spaces. Common sources of air pollution include automobiles, factories, homes with incinerators, and forest fires. There is no denying that Indonesia's numerous forest fires contribute to air pollution there. This situation has caused divisions in the opinions of a lot of people. Twitter is only one among the numerous diverse emotions that can be felt on the internet. Twitter is the social media site that allows the most variety of unbiased, affirmative, and negative opinions. Therefore, the research's objective is to solve the problem by using the Random Forest and SVM methods. Tweet Harvest was used to scrape results and compile the information. The data collection contained 8555 tweets. by dividing. Keywords: sentiment analisis, SVM, Random Forest, Twitter, Polusi Polusi udara adalah kontaminasi area dalam dan luar ruangan oleh zat kimia, fisik, atau biologis yang mengubah sifat alami atmosfer. Insinerator domestik, mobil, pabrik, dan kebakaran hutan merupakan sumber polusi udara yang umum. Di Indonesia, tidak diragukan lagi kalau polusi udara terjadi karena banyaknya kebakaran hutan di Indonesia. Akibat kasus tersebut, banyak opini masyarakat yang berbeda-beda. Berbagai sentiment terjadi di dunia maya, salah satunya Twitter. Twitter adalah social media yang paling banyak menampung berbagai macam opini positif, negatif maupun netral. Oleh karena itu, peneliti ingin memecahkan masalah dengan implementasi algoritma SVM dan Random Forest. Dataset didapatkan dari hasil scrapping menggunakan tweet harvest. Data yang diperoleh didapatkan sebanyak 8555 tweet. Dengan membagi model dataset 80% dan 20%, hasil didapat bahwa akurasi algoritma SVM lebih baik dari algoritma Random Forest. Akurasi dari algoritma SVM sebesar 83% sedangkan algoritma Random Forest sebesar 81%. Katakunci : sentiment analisis, SVM, Random Forest, Twitter, Polusi

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 020
Call Number:	SIK/15/24/018
NIM/NIDN Creators:	41519010191
Uncontrolled Keywords:	sentiment analisis, SVM, Random Forest, Twitter, Polusi
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 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 512 Algebra/Aljabar > 512.5 Linear, Multilinear, Multidimensional Algebra/Aljabar Linear, Multilinear, Aljabar Multidimensional > 512.52 Vector Spaces/Ruang Vektor 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan > 621.8 Machine Engineering, Machinery/Teknik Mesin
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	02 Feb 2024 03:09
Last Modified:	02 Feb 2024 03:09
URI:	http://repository.mercubuana.ac.id/id/eprint/85733

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