HAIDAR, IFAN (2023) PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES, SVM, DAN RANDOM FOREST TERHADAP ANALISIS SENTIMEN TRAGEDI KANJURUHAN. S1 thesis, Universitas Mercu Buana.
![[img]](https://repository.mercubuana.ac.id/style/images/fileicons/text.png) |
Text (COVER)
01 Cover.pdf Download (345kB) |
|
Text (ABSTRAK)
02 Abstrak.pdf Download (29kB) |
|
Text (BAB I)
03 Bab 1.pdf Restricted to Registered users only Download (95kB) |
|
Text (BAB II)
04 Bab 2.pdf Restricted to Registered users only Download (371kB) |
|
Text (BAB III)
05 Bab 3.pdf Restricted to Registered users only Download (120kB) |
|
Text (BAB IV)
06 Bab 4.pdf Restricted to Registered users only Download (730kB) |
|
Text (BAB V)
07 Bab 5.pdf Restricted to Registered users only Download (25kB) |
|
Text (DAFTAR PUSTAKA)
08 Daftar Pustaka.pdf Restricted to Registered users only Download (97kB) |
|
Text (LAMPIRAN)
09 Lampiran.pdf Restricted to Registered users only Download (1MB) |
Abstract
The tragedy at Kanjuruhan Stadium in Malang occurred on October 1, 2022, after the Arema FC football match, which they lost 2-3 against Persebaya. The Kanjuruhan tragedy claimed 754 victims, with 132 fatalities, 596 suffering moderate injuries, and 26 sustaining severe injuries on Sunday (October 15, 2022). The cause of death for the victims of the Kanjuruhan Stadium tragedy in Malang was primarily due to respiratory distress and trampling caused by panic following the release of tear gas by the police. The chaotic and panicked situation led to numerous casualties among the supporters, resulting in injuries and fatalities. The tragic incident at Kanjuruhan, which claimed many victims, sparked a flurry of comments on social media, particularly Twitter. With numerous Twitter users expressing their opinions, this platform can be utilized to gather information and analyze the positive and negative sentiments expressed. This study compared the performance of the Naive Bayes, Support Vector Machine (SVM), and Random Forest algorithms in analyzing the sentiment surrounding the Kanjuruhan tragedy. The experimental results demonstrated that the SVM method exhibited better accuracy compared to the Random Forest and Naive Bayes methods, with an average accuracy of 84.13%. The Random Forest method achieved an average accuracy of 81.12%. Meanwhile, the Naive Bayes method yielded an average accuracy of 79.95%. Keywords: Kanjuruhan, Sentiment Analysis, Naïve Bayes, Support Vector Machine, Random Forest Tragedi di Stadion Kanjuruhan Malang terjadi pada 1 Oktober 2022 usai pertandingan sepak bola Arema FC yang kalah 2-3 melawan Persebaya. Tragedi maut Kanjuruhan memakan korban 754 orang, 132 dinyatakan meninggal dunia, 596 luka ringan sedang dan luka berat 26 orang, Minggu (15/10/2022). Penyebab korban tragedi Stadion Kanjuruhan Malang meninggal dunia adalah karena mayoritas mengalami sesak nafas dan terinjak-injak karena panik setelah dilakukan pelepasan gas air mata oleh pihak kepolisian. Situasi panik karena rusuh menyebabkan banyaknya korban dari para suporter yang terluka hingga meninggal dunia. Atas kejadian tragedi Kanjuruhan yang menelan banyak korban menyebabkan banyaknya komentar di sosial media salah satunya adalah Twitter. Dengan banyaknya pengguna Twitter yang menyampaikan opini-opini, maka dapat dimanfaatkan untuk mencari sebuah informasi dan polaritas positif dan negatifnya opini-opini tersebut. Pada penelitian ini dilakukan Perbandingan Kinerja Algoritma Naive Bayes, Support Vector Machine (SVM), dan Random Forest Terhadap Analisis Sentimen Tragedi Kanjuruhan. Hasil eksperimen menunjukan bahwa kinerja dari Metode SVM menghasilkan nilai akurasi yang lebih baik dibandingkan metode Random Forest dan Naieve Bayes, dengan rata-rata akurasi mencapai 84.13%. Untuk metode Random Forest memiliki nilai akurasi rata-rata sebesar 81.12%. Sedangkan untuk metode Naive Bayes dengan rata-rata akurasi mencapai 79.95%. Kata Kunci : Kanjuruhan, Sentimen, Naïve Bayes, Support Vector Machine, Random Forest
Actions (login required)
 |
View Item |
