eprintid: 72940
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/07/29/40
datestamp: 2025-09-16 07:34:18
lastmod: 2025-09-16 07:34:18
status_changed: 2025-09-16 07:34:18
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Salim Athari, NIM.: 22206051023
title: PENGARUH PENERAPAN KOMBINASI METODE PREPROCESSING, VECTORIZER, DAN ALGORITMA TERHADAP KINERJA KLASIFIKASI TEKS PERUNDUNGAN SIBER
ispublished: pub
subjects: 004.
divisions: S2_inf
full_text_status: restricted
keywords: Perundungan Siber, Klasifikasi Teks, Preprocessing,
Vectorizer, Algoritma
note: Dr. Agung Fatwanto, S.Si., M.Kom.
abstract: Cyberbullying is a serious digital threat that requires automated
detection. Machine Learning (ML) and Natural Language Processing
(NLP) offer solutions, but their performance is significantly affected by
the combination of preprocessing methods, vectorization, and
classification algorithms. This study systematically compares these
combinations to identify the optimal configuration due to the lack of a
holistic approach. The preprocessing methods used include stopword
removal, stemming, lemmatization with a one-hot vectorizer, BoW, TFIDF,
and Logistic Regression, SVM, KNN, Decision Tree, and Naive
Bayes algorithms for cyberbullying text classification. The goal is to
find the most effective combination of methods based on standard
performance metrics.
The approach used is a comparative, quantitative, experimental
approach, using a labeled English-language cyberbullying dataset.
Data preprocessing was performed before vectorization and then
trained using a classification algorithm. Performance was rigorously
evaluated using accuracy, precision, recall, and F1-score. Therefore,
this holistic approach yielded the highest results among the various
combinations.
The results show that preprocessing significantly improves
performance, with lemmatization generally outperforming. One-Hot
Encoding yielded the lowest results, while TF-IDF was the most
effective vectorizer. Among the algorithms, SVM and Logistic
Regression performed best, with SVM demonstrating excellent
performance, especially with TF-IDF. The optimal combination was
obtained when using stopword removal, TF-IDF, and SVM, achieving
an F1-score of 0.863. The lowest combination result, 0.155, was
obtained when using no preprocessing, one-hot encoding
vectorization, and the KNN algorithm. This emphasizes the importance
of precise preprocessing, an informative vectorizer, and a robust
algorithm for effective cyberbullying detection.
date: 2025-08-16
date_type: published
pages: 80
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: masters
thesis_name: other
citation:   Salim Athari, NIM.: 22206051023  (2025) PENGARUH PENERAPAN KOMBINASI METODE PREPROCESSING, VECTORIZER, DAN ALGORITMA TERHADAP KINERJA KLASIFIKASI TEKS PERUNDUNGAN SIBER.  Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/72940/1/22206051023_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/72940/2/22206051023_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf