PENERAPAN METODE K-NEAREST NEIGHBOR (K-NN) UNTUK IDENTIFIKASI UJARAN KEBENCIAN TERHADAP AFLIATOR BINOMO DI TWITER

SUSANTO, FIRMAN ADI (2023) PENERAPAN METODE K-NEAREST NEIGHBOR (K-NN) UNTUK IDENTIFIKASI UJARAN KEBENCIAN TERHADAP AFLIATOR BINOMO DI TWITER. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Hate speech is speech or language that expresses hatred towards individuals or groups that aims to humiliate or embarrass the media where it can be found anywhere, one of which is Twitter. Twitter is a social media that allows users to express feelings and opinions through Tweets, including Tweets that contain hate. This study uses the KNearest Neighbor method. The data used is Tweets about the binomo affiliate case. Tweet data is obtained based on related hashtags. This study uses a dataset of 1366 data which is divided into test data and training data. The results of the testing process using the confusion matrix obtain the highest accuracy of 96%, recall of 1.0%, precision of 96%, and f1_score of 98% on a 60% dataset model: 40%. Based on the results of the study it can be concluded that the K-Nearest Neighbor method is good in the process of helping hate speech on Twitter social media. Keywords:Hatespeech,K-NN,ConfusionMatrix,Twitter. Ujaran kebencian adalah suatu ucapan atau bahasa yang mengekspresikan suatu kebencian terhadap individu maupun kelompok yang bertujuan untuk menghina atau mempermalukan yang medianya bisa terdapat dimana saja, salah satunya Twitter. Twitter merupakan media sosial yang memungkinkan pengguna untuk menyampaikan perasaan dan opini melalui Tweet, termasuk Tweet yang mengandung ujaran kebencian. Penelitian ini menggunakan metode K-Nearest Neighbor. Data yang digunakan yaitu Tweet tentang kasus afliator binomo. Data Tweet diperoleh berdasarkan hashtag terkait. Penelitian ini menggunakan dataset 1366 data yang dibagi menjadi data uji dan data latih. Data diproses dengan pembobotan kata menjadi vector menggunakan Term Frequency Inverse Document Frequency (TF-IDF). Hasil dari proses pengujian menggunakan confusion matrix memperoleh akurasi tertinggi yaitu sebesar 96%, recall sebesar 1.0%, precision sebesar 96%, dan f1_score sebesar 98% pada model dataset 60%:40%.Berdasarkan hasil penelitian dapat disimpulkan bahwa metode K-Nearest Neighbor baik dalam proses identifikasi ujaran kebencian pada media sosial Twitter. Kata Kunci : Ujaran kebencian, K-NN, Confusion Matrix, TF-IDF, Twitter .

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 23 028
NIM/NIDN Creators: 41518110168
Uncontrolled Keywords: Ujaran kebencian, K-NN, Confusion Matrix, TF-IDF, Twitter .
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
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
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 > 006 Special Computer Methods/Metode Komputer Tertentu > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social > 006.754 Online Social Network/Situs Jejaring Sosial, Sosial Media
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: ADELINA HASNA SETIAWATI
Date Deposited: 08 Apr 2023 07:18
Last Modified: 08 Apr 2023 07:18
URI: http://repository.mercubuana.ac.id/id/eprint/76243

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