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All Journal JUITA : Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Kuncoro Hadi
Universitas Amikom Yogyakarta
Computer Science & IT Education

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ANALISIS K-NN DENGAN INTEGRASI BOW, TF-IDF, DAN N-GRAMS UNTUK KLASIFIKASI UJARAN KEBENCIAN PADA TWITTER Hadi, Kuncoro; Utami, Ema
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6694

Abstract

Media sosial telah menjadi salah satu sarana komunikasi utama di dunia modern, tetapi juga menjadi platform di mana ujaran kebencian dapat dengan mudah menyebar. Penelitian ini bertujuan untuk mengevaluasi model klasifikasi ujaran kebencian dengan menggunakan algoritma K-Nearest Neighbors (K-NN) dan teknik pendekatan ekstraksi fitur, yaitu Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), dan N-Grams. Dataset yang digunakan dalam penelitian ini, terdiri dari 13.169 data, yang mencakup berbagai jenis ujaran kebencian yang umum ditemukan di platform media sosial. Dalam eksperimen ini, kami menguji performa model dengan menggunakan pendekatan tunggal dari masing-masing teknik ekstraksi fitur serta kombinasi dari ketiganya (BoW + TF-IDF + N-Grams). Hasil eksperimen menunjukkan bahwa model K-NN mencapai performa terbaik saat parameter k diatur pada nilai 3 (k=3). Pada konfigurasi ini, model menghasilkan akurasi sebesar 86.88%, dengan nilai precision mencapai 88.27%, recall sebesar 86.88%, dan F1 Score sebesar 86.50%. Hasil ini menunjukkan bahwa teknik ekstraksi fitur TF-IDF dan K-NN dapat memberikan kinerja terbaik dalam mengklasifikasikan ujaran kebencian.
Analysis of K-NN with the Integration of Bag of Words, TF-IDF, and N-Grams for Hate Speech Classification on Twitter Kuncoro Hadi; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23829

Abstract

Social media has emerged as one of the primary communication channels in the modern world, but it has simultaneously become a platform where hate speech can spread easily. This study attempts to evaluate the performance of a hate speech classification model using the K-Nearest Neighbors (K-NN) algorithm along with various feature extraction techniques, specifically Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and N-Grams. The dataset used in this study consists of 13169 entries, which represent a diverse range of hate speech examples commonly encountered on social media platforms. In this experimental investigation, we assess the efficacy of the model using each feature extraction technique. The findings reveal that the K-NN model exhibits optimal performance when the k parameter is set to 3 (k=3). Under this configuration, the model achieves an accuracy of 86.88%, with a precision of 88.27%, a recall of 86.88%, and an F1-Score of 86.50%. These results show that the integration of TF-IDF feature extraction technique with K-NN algorithm produces superior performance in hate speech classification.
Co-Authors Ema Utami