TY  - THES
N1  - Arif Munandar, M.Sc. dan Muhamad Rashif Hilmi, S.Si., M.Sc.
ID  - digilib76835
UR  - https://digilib.uin-suka.ac.id/id/eprint/76835/
A1  - Laila Rohmatul I?zzah, NIM.: 22106010007
Y1  - 2026/06/03/
N2  - YouTube comments are unstructured textual data that contain diverse public
opinions and require a classification algorithm capable of effectively handling highdimensional
text data. This study aims to compare the performance of linear,
polynomial, and Radial Basis Function (RBF) kernels in the Support Vector
Machine (SVM) algorithm for sentiment classification of comments on the Malaka
Project YouTube channel. The dataset was collected through scraping using the
YouTube Data API v3 from a video entitled ?The Dark Business of Doctors and
Pharmaceutical Companies,? resulting in 3,428 comments collected in December
2025. The data were classified into three sentiment categories: positive, negative,
and neutral. The research procedure consisted of data preprocessing, sentiment
labeling using the InSet Lexicon dictionary, data splitting with an 80:20 ratio,
feature extraction using TF-IDF, classification using a multiclass SVM algorithm
with the One-Against-All (OvR) approach, and model evaluation using accuracy,
precision, recall, F1-score, and confusion matrix metrics. The results show that the
linear and RBF kernels achieved the same accuracy score of 0.73, while the
polynomial kernel obtained a lower accuracy score of 0.60. However, the linear
kernel achieved the highest precision and F1-score of 0.77 for the positive sentiment
class. Therefore, it can be concluded that the linear kernel performs better in
classifying sentiment in Malaka Project YouTube comments compared to the other
two kernels.
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - Klasifikasi Sentimen
KW  -  Komentar Youtube Malaka Project
KW  -  Support
Vector Machine (SVM)
KW  -  Kernel SVM
M1  - skripsi
TI  - KLASIFIKASI SENTIMEN KOMENTAR YOUTUBE MALAKA PROJECT MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) (STUDI KASUS: VIDEO "BISNIS GELAP DOKTER & PERUSAHAAN FARMASI")
AV  - restricted
EP  - 139
ER  -