The comparation of text mining with Naive Bayes classifier, nearest neighbor, and decision tree to detect Indonesian swear words on Twitter

Zulfikar, Wildan Budiawan; Irfan, Mohamad; Alam, Cecep Nurul; Indra, Muhammad

doi:10.1109/citsm.2017.8089231

Cited by 31 publications

(17 citation statements)

References 3 publications

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“…This tool does not use Twitter's API (Application Programming Interface). Because if we use the API, the data taken will be limited in number based on account, regional, trending topics, or keywords used [14]. By utilizing the crawling tool, the data gathering process can be done maximally and comprehensively.…”

Section: Data Crawlingmentioning

confidence: 99%

Levels of Political Participation Based on Naive Bayes Classifier

Hidayatillah

Mirwan

Hakam

et al. 2019

Indonesian J. Comput. Cybern. Syst.

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Nowadays, social media is growing rapidly and globally until it finally became an important part of society. During campaign period for the regional head election in Indonesia, the candidates and their supporting parties actively use social media as a campaign tool. Social media like Twitter has been known as a political microblogging media that can provide data about current political event based on users’ tweets. By using Twitter as a data source, this study analyzes public participation during campaign period for 2018 Central Java regional head election. The purpose is to observe how much reaction is given to each candidate who advanced in the election. By using the crawling program, all tweets containing certain candidate names will be downloaded. After going through a series of preprocessing stages, data can be classified using Naive Bayes. Predictor features in classification datasets are the number of replies, retweets, and likes. While the target variable is reaction that is divided into three levels, including high, medium, and low. These levels are determined based on users’ reaction in a tweet. By using these rules, Naive Bayes managed to classify data correctly as much as 76.74% for Ganjar Pranowo and 68.81% for Sudirman Said.

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Section: Data Crawlingmentioning

confidence: 99%

Levels of Political Participation Based on Naive Bayes Classifier

Hidayatillah

Mirwan

Hakam

et al. 2019

Indonesian J. Comput. Cybern. Syst.

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“…Gambar 5 menunjukkan pembagian jumlah akun Twitter yang menuliskan tweet bernada umpatan pada Pilkada Sumut 2018. Lagi pada kata umpatan di media sosial yang acapkali dilakukan oleh pengguna Twitter dari Indonesia untuk kegiatan sehari-hari (Zulfikar, Irfan, Alam, & Indra, 2017). Hasil ketiga yang dapat disimpulkan dari Gambar 1 sampai dengan Gambar 6 adalah akun Twitter yang bercirikan laki-laki lebih banyak melakukan umpatan dibandingkan dengan perempuan.…”

Section: Hasil Dan Pembahasanunclassified

Penggunaan kata umpatan di Twitter berdasarkan gender di pilkada Sumatera Utara 2018

et al. 2019

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Di dalam pandangan tradisional, perempuan distigmatisasi menjadi lemah lembut dan anggun. Oleh karena itu, sangat jarang kita melihat perempuan mengatakan kata kasar di ruang publik karena tabu bagi mereka. Media sosial menyediakan ruang virtual bagi orang untuk berinteraksi tanpa batas yang jelas. Orang-orang di Internet (netizen) dapat mengatakan hampir semuanya di media sosial tanpa memikirkan dampak negatif pada apa yang mereka katakan. Beberapa netizen melewati batas kesusilaan dengan mengucapkan kata kasar kepada netizen lain tanpa berpikir yang mungkin menyakiti seseorang. Studi terbaru menunjukkan bahwa kata-kata kasar dapat menyebabkan cyberbully yang pada akhirnya akan mengarah ke bunuh diri. Dengan demikian menggunakan kata kasar di media sosial mungkin memiliki dampak yang sama dengan menggunakannya dalam kehidupan nyata. Kemajuan media sosial telah menyebabkan penggunaannya dalam politik praktis. Kandidat politik menggunakan media sosial untuk mempromosikan profil mereka dan akhirnya mendapatkan pemilih. Sayangnya tidak semua dari mereka yang memposting komentar di media sosial beretika, termasuk perempuan. Penelitian ini bertujuan untuk mengkaji pemanfaatan Twitter dalam mengeluarkan kata umpatan berdasarkan perbedaan gender dalam pemilihan gubernur. Penelitian ini akan menganalisis Twitter sebagai platform media sosial paling umum yang digunakan dalam politik. Sebagai contoh, penelitian ini menangkap 5000 tweet dari pemilihan pilkada Sumatera Utara 2018 di Indonesia. Teknik statistik deskriptif digunakan dalam menganalisis data.

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“…However, this is not accompanied by knowledge that can extract the information needed from these electronic documents. Therefore a method is needed to make the classification of these documents ease and simplify [2]. One of the methods that can be used is text mining [3], [4].…”

Section: Introduction (10 Pt)mentioning

confidence: 99%

Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents

Hariri

2021

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Since the Indonesian Ulema Council (MUI) was established in 1975 until now, this institution has produced 201 edicts covering various fields. Text mining is one of the techniques used to collect data hidden from data that form text. One method of extracting text is Clustering. The present study implements the Fuzzy C-Means Clustering method in MUI fatwa documents to classify existing fatwas based on the similarity of the issues discussed. Silhouette Coefficient is used to analyze the resulting clusters, with the best value of 0.0982 with 10 clusters grouping. Classify fatwas based on the similarity of the issues discussed can make it easier and faster in the search for an Islamic law in Indonesia.

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The comparation of text mining with Naive Bayes classifier, nearest neighbor, and decision tree to detect Indonesian swear words on Twitter

Cited by 31 publications

References 3 publications

Levels of Political Participation Based on Naive Bayes Classifier

Levels of Political Participation Based on Naive Bayes Classifier

Penggunaan kata umpatan di Twitter berdasarkan gender di pilkada Sumatera Utara 2018

Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents

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