PANGASTUTI, BEKTI (2021) Perbandingan Klasifikasi Analisis Sentimen Media Sosial Mengenai Larangan Mudik Di Masa Pandemi 2020. S1 thesis, Universitas Mercu Buana Jakarta.
|
Text (HAL COVER)
01. Cover.pdf Download (586kB) | Preview |
|
![[img]](https://repository.mercubuana.ac.id/style/images/fileicons/text.png) |
Text (BAB I)
02. Bab 1.pdf Restricted to Registered users only Download (248kB) |
|
|
Text (BAB II)
03. Bab 2.pdf Restricted to Registered users only Download (118kB) |
|
|
Text (BAB III)
04. Bab 3.pdf Restricted to Registered users only Download (87kB) |
|
|
Text (BAB IV)
05. Bab 4.pdf Restricted to Registered users only Download (52kB) |
|
|
Text (BAB V)
06. Bab 5.pdf Restricted to Registered users only Download (210kB) |
|
|
Text (DAFTAR PUSTAKA)
08 Daftar Pustaka.pdf Restricted to Registered users only Download (200kB) |
|
|
Text (LAMPIRAN)
09 Lampiran.pdf Restricted to Registered users only Download (264kB) |
Abstract
Homecoming is very synonymous in Indonesia, this habit is routinely carried out every year before the religious feast. Sentiment analysis is useful for processing or extracting public comment data. This study aims to compare three algorithms, namely Supoort Vector Machine, K-Nearest Neighbor, and Decision Tree using data derived from comments on social media twitter and youtube. In order to find out the comments of the public regarding the regulations on the prohibition of going home during the pandemic period issued by the government. The tools used by researchers are python, and the process consists of several stages, namely data labeling, pre-processing, split training and testing, feature extraction, finding the best parameters, models, and finally evaluation. The accuracy results conclude that the SVM algorithm is superior at 91% compared to the KNN algorithm which gets 89% and DT gets 86%. Key words: Sentiment analysis, Support vector machine, K-Nearest Neighbor, Decission Tree, Classification teks. Mudik sangat identik di Indonesia, kebiasaan tersebut rutin dilakukan pada tiap tahun menjelang hari raya umat beragama. Analisis sentimen berguna untuk mengolah atau mengekstrak data komentar publik. Penelitian ini bertujuan untuk membandingkan tiga algoritma yaitu Supoort Vector Machine, K-Nearest Neighbor, dan Decision Tree dengan menggunakan data yang berasal dari komentar di media sosial twitter dan youtube. Agar dapat mengetahui komentar masyarakat mengenai peraturan dilarang mudik pada masa pandemi yang dikeluarkan oleh pemerintah. Tools yang digunakan peneliti ialah python, dan untuk proses terdiri dari beberapa tahapan yaitu labelling data, pre-processing, split training dan testing, fitur extraction, mencari parameter terbaik, model, dan yang terakhir evaluasi. Hasil akurasi menyimpulkan bahwa algoritma SVM lebih unggul yaitu sebesar 91% dibandingkan algoritma KNN yang mendapatkan hasil 89% dan DT mendapatkan hasil 86%. Kata kunci: Analisis sentimen, Support vector machine, K-Nearest Neighbor, Decission Tree, Klasifikasi teks
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41517010026 |
| Uncontrolled Keywords: | Analisis sentimen, Support vector machine, K-Nearest Neighbor, Decission Tree, Klasifikasi teks |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.3 Bibliographic Analysis and Control/Bibliografi Analisis dan Kontrol Perpustakaan |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Dede Muksin Lubis |
| Date Deposited: | 17 Oct 2023 02:10 |
| Last Modified: | 17 Oct 2023 02:10 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/82617 |
Actions (login required)
 |
View Item |
