KAJIAN MACHINE LEARNING DENGAN KOMPARASI KLASIFIKASI PREDIKSI DATASET TENAGA KERJA NON-AKTIF

J-Icon : Jurnal Komputer dan Informatika

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Title KAJIAN MACHINE LEARNING DENGAN KOMPARASI KLASIFIKASI PREDIKSI DATASET TENAGA KERJA NON-AKTIF
 
Creator Sina, Derwin Rony
KUSRORONG, NEUTRINO KUSRORONG SAE BELAUDIN
Rumlaklak, Nelci Dessy
 
Description Comparative studies of machine learning are carried out with the aim of determining the best method base based on the ability to predict with true data. The study carried out on the labor dataset aims to extract information on the choice of agency employees to exit or not. The method used in the comparative study is K-Nearest Neighbors (KNN) from the basis of similarity, Naïve Bayes (NB) from the probability base, and C4.5 from the basis of the decision tree. Application design and construction is done by receiving input labor data, the dataset is divided into training data and test data, training data for training and models while the test data is used when classifying by model. The classification process is carried out using supply training scenarios and cross validation of 14,999 data. The initial hypothesis C4.5 is the best method with an accuracy measure. Proof of the initial hypothesis will be true if the best accuracy majority is owned by the C4.5 method with supply trainning scenarios and cross validation. The results of the classification data analysis found that the C4.5 accuracy was superior in each parameter of the inventory training scenario data distribution and the k-fold parameter was 3. 5. 7, and 9 of the cross validation scenario so that the best method of non-active labor classification was C4.5.
 
Publisher Universitas Nusa Cendana
 
Date 2019-03-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ejurnal.undana.ac.id/jicon/article/view/880
10.35508/jicon.v7i1.880
 
Source J-Icon : Jurnal Komputer dan Informatika; Vol 7 No 1 (2019): Maret 2019; 37-49
2654-4091
2337-7631
10.35508/jicon.v7i1
 
Language eng
 
Relation http://ejurnal.undana.ac.id/jicon/article/view/880/760
 
Rights Copyright (c) 2019 Jurnal Komputer dan Informatika (JICON)
http://creativecommons.org/licenses/by-nd/4.0
 

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