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KLASIFIKASI JENIS MANFAAT PENSIUN DENGAN MENGGUNAKAN METODE FUZZY RANDOM FOREST BERDASARKAN RESAMPLING SPLIT VALIDATION

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Along with the development of various pension benefit types, many people now are starting to choose early retirement. PT Perkebunan Nasional II is one of the plantation companies that provides Old Age Benefit (SHT). With these problems the company needs to prepare sufficient funds for the employees retirement costs. This study purpose to classify the types of retirement benefits based on predictor variables using Resampling Split Validation on the Fuzzy Random Forest method which combines fuzzy set theory and Random Forest. The data used in this study has 8 variables and 5140 data from the retired employees list of PT Perkebunan Nasional II from 2012-2019 with 2 classifications of pension benefits types. In classifying using the Random Forest method based on Resampling Split Validation with ratio of 80% train data and 20% test data, from 100 decision trees which obtained the model's levels of accuracy, precision, recall, specificity and f1 score sequentially are 90.08%, 62.69%, 55.79%, 95.12%, and 59.04%. Then for classification using the Fuzzy Random Forest method based on Resampling Split Validation with the same ratio, from 100 decision trees which obtained the model's levels of accuracy, precision, recall, specificity and f1 score sequentially are 90.73%, 83.18%, 34.72% , 98.97%, and 49%.

Availability
Inventory Code Barcode Call Number Location Status
2307002143T107470T1074702023Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1074702023
Publisher
Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Sriwijaya.,
Collation
xiii, 81 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
511.307
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Logika Matematika
Jurusan Matematika
Specific Detail Info
-
Statement of Responsibility
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No other version available

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