eprintid: 42658
rev_number: 12
eprint_status: archive
userid: 12461
dir: disk0/00/04/26/58
datestamp: 2021-07-07 09:55:42
lastmod: 2021-07-07 09:55:42
status_changed: 2021-07-07 09:55:42
type: thesis
metadata_visibility: show
contact_email: anik.azizah@uin-suka.ac.id
creators_name: Ahmad Nur Fauzi, NIM.: 16650029
title: STUDI PERBANDINGAN METODE ANALISIS NAIVE BAYES CLASSIFIER  DENGAN SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN  (STUDI KASUS: TWEET BERBAHASA INDONESIA TENTANG COVID-19)
ispublished: pub
subjects: TB
subjects: _sistem_informasi
full_text_status: restricted
keywords: Naive Bayes Classifier, Support Vector Machine, Analisis Sentimen
note: Pembimbing : Agung Fatwanto, Ph.D
abstract: Sentimen analysis is a technique for detecting opinions about a subject (for
example individuals, organizations or products) in a data set. Community life today
supports the emergence of social interaction through social media which is a
suggestion in conveying positive and negatif opinions. In this study using 2
methods, namely Naïve Bayes Classifiers (NBC) and Support Vector Machine
(SVM) to classify sentimens.  
A total of 9015 data were used in the analysis process with TF-IDF
weighting techniques and Term Frequency. Evaluation uses 2 ways, that is
confusion matrix and K-fold cross validation. With the confusion matrix, SVM has
an accuracy value of 83%, a precision value of 83%, a recall value of 98.8%, and a
specificity value of 9%. While NBC has an accuracy value of 82.3%, a precision
value of 82.3%, a recall value of 99.7%, and a specificity value of 3.4% with TFIDF.
Then,
SVM
has
an
accuracy
value
of
82.8%,
a
precision
value
of
83%,
a
recall

value
of
99%,
and
a
specificity
of
8%.
While
NBC
has
an
accuracy
value
of
80.1%,

a
precision
value
of
85.7%,
a
recall
value
of
90%,
and
a
specificity
of
29%
with
TF.

K-fold cross Validation with TF-IDF weighting, SVM accuracy value of
82.6%, precision value of 82.9%, recall rate of 98.8%, and specificity of 10.9%.
NBC accuracy is 82%, precision value is 82%, recall rate is 99.6%, and specificity
is 4.8%. SVM Term Frequency accuracy value is 82.5%, precision value is 82.6%,
99.1% recall rate, and specificity is 7%. NBC accuracy value is 79.8%, precision
value is 85.9%, recall rate is 89.2%, and specificity is 32%. NBC data processing
time is faster than SVM with NBC under 5 seconds while SVM is above 500
seconds.
date: 2020-06-25
date_type: published
pages: 152
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: Fakultas Sains dan Teknologi
thesis_type: skripsi
thesis_name: other
citation:   Ahmad Nur Fauzi, NIM.: 16650029  (2020) STUDI PERBANDINGAN METODE ANALISIS NAIVE BAYES CLASSIFIER DENGAN SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN (STUDI KASUS: TWEET BERBAHASA INDONESIA TENTANG COVID-19).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/42658/1/16650029_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/42658/2/16650029_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf