eprintid: 58412
rev_number: 11
eprint_status: archive
userid: 12243
dir: disk0/00/05/84/12
datestamp: 2023-05-09 03:08:29
lastmod: 2023-05-09 03:08:29
status_changed: 2023-05-09 03:08:29
type: thesis
metadata_visibility: show
contact_email: muchti.nurhidaya@uin-suka.ac.id
creators_name: Tachiyya Nailal Khusna, NIM.: 21206051002
title: KOMPARASI ALGORITMA NAÏVE BAYES CLASSIFIER, K-NEAREST NEIGHBOR (KNN) DAN DECISION TREE UNTUK MENGANALISIS SENTIMEN MASYARAKAT TERHADAP KESEHATAN MENTAL PADA MEDIA SOSIAL TWITTER
ispublished: pub
subjects: TB
subjects: ms
divisions: jur_tinf
full_text_status: restricted
keywords: sentiment analysis; mental health; K-Nearest Neighbors (K-NN); Naïve Bayes Classifier; twitter
note: Pembimbing: Dr. Bambang Sugiantoro, S.Si., M.T.
abstract: Twitter is one of the many social media platforms that people often use to express their opinions. Twitter is the most popular social media platform on the internet and offers users the opportunity to express their opinions on a variety of topics, including news, current events, cabaret and other topics. One such topic is opinions on mental health. Issues on Health The only significant problem in the world is mental. A decrease in the number or rate of mental illnesses can be successfully eliminated when society contributes to not discriminating against sufferers. However, there are many different opinions on Twitter from different internet users, and one of the most prominent is the opinion on mental health. Therefore, it is necessary to conduct a basic analysis of public opinion to explain and provide new information on certain topics related to mental health, the methods used are Naïve Bayes Clasifier and K-Nearest Neighbors (K-NN) algorithms. A total of 5000 data were taken using the twitter API with the keyword "Mental Health". Starting from the classification of positive or negative opinions, data cleansing, preprocessing, until the final result is obtained. Then calculated into two different algorithms to compare, the algorithms used are Naïve Bayes Classifier and K-Nearest Neighbors (K-NN) with the aim of finding the best accuracy. The Rapidminer Version 7.1 application is also used to facilitate the author in processing data. The highest result of this research is the Naïve Bayes algorithm with an accuracy value of 98.3%, precision 79% and recall 87.17%.
date: 2022-03-10
date_type: published
pages: 87
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Tachiyya Nailal Khusna, NIM.: 21206051002  (2022) KOMPARASI ALGORITMA NAÏVE BAYES CLASSIFIER, K-NEAREST NEIGHBOR (KNN) DAN DECISION TREE UNTUK MENGANALISIS SENTIMEN MASYARAKAT TERHADAP KESEHATAN MENTAL PADA MEDIA SOSIAL TWITTER.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/58412/1/21206051002_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/58412/2/21206051002_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf