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)

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.

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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.

Item Type: Thesis (Skripsi)
Additional Information: Pembimbing : Rahmat Hidayat, S.Kom., M.Cs.
Uncontrolled Keywords: Data Mining, Naive Bayes, Ensemble Bootstrap Aggregating,
Subjects: Tehnik Informatika
Organisasi
Divisions: Fakultas Sains dan Teknologi > Teknik Informatika (S1)
Depositing User: Anik Nur Azizah
Date Deposited: 07 Jul 2021 17:15
Last Modified: 07 Jul 2021 17:15
URI: http://digilib.uin-suka.ac.id/id/eprint/42686

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