%0 Thesis
%9 Skripsi
%A Siti Shofiyah, NIM.: 16650055
%B Fakultas Sains dan Teknologi
%D 2020
%F digilib:42686
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K Data Mining, Naive Bayes, Ensemble Bootstrap Aggregating,
%P 110
%T 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)
%U https://digilib.uin-suka.ac.id/id/eprint/42686/
%X 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.
%Z Pembimbing : Rahmat Hidayat, S.Kom., M.Cs.