eprintid: 42686
rev_number: 11
eprint_status: archive
userid: 12461
dir: disk0/00/04/26/86
datestamp: 2021-07-07 10:15:49
lastmod: 2021-07-07 10:15:49
status_changed: 2021-07-07 10:15:49
type: thesis
metadata_visibility: show
contact_email: anik.azizah@uin-suka.ac.id
creators_name: Siti Shofiyah, NIM.: 16650055
title: STUDI KOMPARASI METODE NAÏVE BAYES DAN METODE  ENSEMBLE BOOTSTRAP AGGREGATING (BAGGING) BERBASIS  NAÏVE BAYES UNTUK KLASIFIKASI CALON ANGGOTA (STUDI  KASUS : UKM JQH AL-MIZAN UIN SUNAN KALIJAGA)
ispublished: pub
subjects: TB
subjects: or_ganisasi
divisions: jur_tinf
full_text_status: restricted
keywords: Data Mining, Naive Bayes, Ensemble Bootstrap Aggregating,
note: Pembimbing : Rahmat Hidayat, S.Kom., M.Cs.
abstract: UKM JQH al-Mizan of UIN Sunan Kalijaga is one of the organizations
implementing selection in accepting prospective members. Based on the period of
the last two years, the management of UKM JQH al-Mizan determined the
graduation of prospective members based on the results of the selection test which
could not predict the potential for potential members. So the prediction analysis
needs to be done on the criteria used when selecting prospective members.
This study aims to determine the parameters that affect the activity of
members using the Ensemble Bootstrap Aggregating (Bagging) based on Naïve
Bayes. The criteria used are gender, domicile, non-formal education, organizational
experience, achievements, interviews, literacy, skills, and selection results. The
results of the implementation of the Bagging Ensemble based on Naïve Bayes show
that the interview and achievement parameters produced the highest prediction
classification accuracy of 73.58%.
Naïve Bayes-based Bagging method in its implementation managed to get
a higher accuracy value of 67.92% compared to the Naïve Bayes algorithm with an
accuracy value of 62.26%. The evaluation results using Confusion Matrix, Bagging
method based on Naïve Bayes produce a recall/sensitivity of 0.76, precision of 0.74,
and f1-score of 0.75, while the Naïve Bayes algorithm produces a recall / sensitivity
of 0.65, precision of 0.85, and f1-score of 0.74.
date: 2020-06-30
date_type: published
pages: 110
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: Fakultas Sains dan Teknologi
thesis_type: skripsi
thesis_name: other
citation:   Siti Shofiyah, NIM.: 16650055  (2020) STUDI KOMPARASI METODE NAÏVE BAYES DAN METODE ENSEMBLE BOOTSTRAP AGGREGATING (BAGGING) BERBASIS NAÏVE BAYES UNTUK KLASIFIKASI CALON ANGGOTA (STUDI KASUS : UKM JQH AL-MIZAN UIN SUNAN KALIJAGA).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/42686/1/16650055_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/42686/2/16650055_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf