Implementasi Algoritma J48 Dengan Teknik Bagging Untuk Prediksi Kipi Peserta Vaksinasi Covid-19

Abstract

The Covid 19 vaccination is considered to be the most effective way to prevent the spread of the Corona Virus, in addition to a clean lifestyle such as washing hands, wearing masks, and keeping a distance from other people. Several large vaccine manufacturing companies in the world have issued a product in the form of a Covid-19 vaccine with various levels of effectiveness. The vaccine is still being distributed throughout the world, including Indonesia. The vaccine obtained an emergency distribution permit from the authorized institution and was administered to community groups that meet the requirements. However, during the implementation of the vaccine, many AEFIs (Post Immunization Adverse Events) were found, such as dizziness, fever, headaches, and some even fainted. Although not dangerous but quite disturbing for people with solid activities. Therefore, it is necessary to predict whether participants will get AEFI or not. The data consists of 8 Attributes, after being processed using the J48 Algorithm, the results show that the attributes that have a strong influence are 7 Attributes, while the rest have no major effect. The accuracy level of the prediction model obtained is 91,23% with this level of accuracy, precision 0,918, recall 0,912, F-Measure 0,913 and ROC Area 0,966. It means that the model can be utilized by the parties concerned to then be able to anticipate.

Klasifikasi Tingkat Kerawanan Banjir Wilayah Medan Menggunakan Metode Naive Bayes Dan Algoritma J48

“…Decision tree adalah metode klasifikasi yang paling populer digunakan, selain karena pembangunannya relatif cepat, hasil dari model yang dibangun mudah dipahami (Situmorang & Ginting, 2020). Algoritma ini menghasilkan pohon keputusan yang dapat digunakan untuk mengklasifikasikan suatu data ke dalam kelas-kelas dan juga memprediksi kelas dari suatu data (Rahmawati & Agustina, 2022). Metode penelitian yang dapat digunakan dalam pengklasifikasian tingkat kerawanan banjir yaitu menggunakan metode Naïve Bayes dikarenakan metode ini mampu bekerja dengan sangat baik dibandingkan dengan model classifier lainnya.…”