Susilowati, Anggun (2016) Implementasi Algoritma Genetika Untuk Melengkapi Missing Imputation Pada Data Klasifikasi Pasien Stroke. Diploma thesis, Politeknik Negeri Jember.
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Abstract
In several studies that exist in the real world there are many cases of loss of value on a dataset or lack of value in the data for a specific attribute. The problem of data loss in value is more commonly referred to missing imputation. The cause of missing imputation is the absence of a response to the unit or item, it is a problem that occurs in most large-scale survey. Problems missing imputation was also observed in the annual survey of large and medium industrial enterprises which is one of the regular surveys conducted the Central Bureau of Statistics. This paper will discuss the imputation techniques (techniques of imputation) the method of handling missing data is based on information available to the dataset that aims to predict the valid values in lieu of the lost value. The purpose of this study was to produce an application that can supplement the data with a blank or missing imputation high degree of accuracy, implementing the method of genetic algorithm on real data to compare with the original data. The experiment was conducted using a genetic algorithm. To design systems that use tools Power Designer to create a Data Flow Diagrams (DFD) and for processing the data using notepad ++. In the system making use tools that programming languages Netbeans 8.1.0 Keywords: Algoritma genetika, Missing Imputation.
| Item Type: | Thesis (Diploma) | ||||||
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| Subjects: | 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 461 - Sistem Informasi | ||||||
| Divisions: | Perpustakaan > Tugas Akhir > Diploma | ||||||
| Depositing User: | Usman Efendi | ||||||
| Date Deposited: | 15 Dec 2025 02:50 | ||||||
| Last Modified: | 15 Dec 2025 02:50 | ||||||
| URI: | https://sipora.polije.ac.id/id/eprint/47869 |
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