eprintid: 2900
rev_number: 9
eprint_status: archive
userid: 46
dir: disk0/00/00/29/00
datestamp: 2022-08-29 15:21:20
lastmod: 2022-09-03 13:29:32
status_changed: 2022-08-29 15:21:20
type: thesis
metadata_visibility: show
creators_name: Herdiawan, Tomi
creators_name: Tita Tosida, Eneng
creators_name: Maesya, Aries
creators_NPM: 065115351
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_type: http://www.loc.gov/loc.terms/relators/THS
contributors_name: Tita Tosida, Eneng
contributors_name: Maesya, Aries
contributors_NIDN: 0425087601
contributors_NIDN: 04090986301
corp_creators: Universitas Pakuan
corp_creators: Fakultas  MIPA
corp_creators: Ilmu Komputer
title: ANALYSIS OF EMPLOYMENT SENTIMENT IN THE 
INDONESIAN TELEMATICS FIELD USE MULTINOMIAL 
NAIVE BAYES AND VECTOR SPACE MODEL
ispublished: pub
subjects: QK
divisions: sch_ecs
full_text_status: none
abstract: Abstract
Indonesia in 2030 experienced a demographic bonus in the sense that Indonesia would have far more 
labor supply than in previous years. Then there is a discourse that this 4.0 industrial revolution will replace a lot 
of work, especially low-skilled work or does not require special skills and rough jobs replaced by machinery and 
artificial intelligent (AI). To obtain the value of the percentage of positive, negative and neutral sentiments from 
the public regarding the impact of the industrial revolution 4 against labor and employment on online news 
media sites and social media Twitter, the authors conducted a study "analysis of employment sentiment in 
Indonesian telematics using multinomial naïve bayes. " The author uses the preprocessing stages including the 
case folding, tokenizing, stopword, and stemming. Then weighting with Term Frequency - Invers Document 
Frequency (TF-IDF). After that the classification stage was done using the multinomial Naive Bayes Classifier 
method and compare it with the Vector Space model classification. The evaluation used is the Confusion Matrix 
evaluation method. This study produced an evaluation value in the multinomial method of Naïve Bayes for news 
data to produce an accuracy of 81.75%, average precision 82.77%, and the average recall of 78.15%. Whereas 
with the Vector Space model method for news data produces an accuracy of 67.88%, average precision 65.59%, 
and the average recall of 70.56%. On Twitter data with the Multinomial Naïve Bayes method resulted in an 
accuracy of 88.80%, average precision 93.75%, and the average recall of 74.44%. On Twitter data with the 
Vector Space Model method resulted in 85.60% accuracy, average precision 76.44% and average recall of 
86.07%.
Keywords: Mathematics, instructions for authors, manuscript template
date: 2021-11
date_type: published
institution: Universitas Pakuan
department: Ilmu Komputer
thesis_type: Skripsi
thesis_name: Sarjana
citation:   Herdiawan, Tomi and Tita Tosida, Eneng and Maesya, Aries  (2021) ANALYSIS OF EMPLOYMENT SENTIMENT IN THE INDONESIAN TELEMATICS FIELD USE MULTINOMIAL NAIVE BAYES AND VECTOR SPACE MODEL.  Skripsi thesis, Universitas Pakuan.