Implementasi Self-Organizing Fuzzy Maps Pada Incomplete Data Untuk Pengelompokan Gizi Bahan Pangan

Milatina, Tara Amila (2017) Implementasi Self-Organizing Fuzzy Maps Pada Incomplete Data Untuk Pengelompokan Gizi Bahan Pangan. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Incomplete data merupakan permasalahan yang dapat mempengaruhi hasil clustering pada kasus yang berkaitan dengan pengenalan pola maupun clustering. Incomplete data menjadi kelemahan dalam clustering dimana hampir semua metode clustering hanya dapat bekerja pada data yang lengkap. Hal ini yang mendorong perlunya dicari metode khusus untuk penanganan terhadap permasalahan yang berkaitan dengan incomplete data. Salah satu permasalahan dalam pengolahan data adalah pada pengelompokan data bahan pangan berdasarkan kandungan gizinya. Hal ini disebabkan karena data gizi bahan pangan merupakan incomplete data. Berdasarkan hal tersebut, pada penelitian ini akan digunakan Self-Organizing Fuzzy Maps untuk mengelompokkan data bahan pangan yang merupakan incomplete data. Setelah dilakukan implementasi dan analisis hasil, didapatkan bahwa Self-Organizing Fuzzy Maps dapat mengelompokkan data bahan pangan yang merupakan incomplete data berdasarkan kandungan gizinya. Berdasarkan hasil tersebut, dapat dibuktikan bahwa Self-Organizing Fuzzy Maps merupakan algoritma clustering yang dapat bekerja pada incomplete data. ================================================================= Incomplete data is a problem that can affect the results of clustering on many cases related to the pattern recognition or clustering. Incomplete data become a weakness in clustering, where nearly all methods of clustering can only work on a complete data. This encourages the need to find specific methods for handling against problems associated with incomplete data. One of the problems in the processing of incomplete data is in the clustering of food based on the content of its nutrition value. This is because food nutrient data is incomplete data. Based on that, this research will use Self-Organizing Fuzzy Maps to cluster food nutrient data which is incomplete data. After the implementation and analysis of the results, obtained that Self-Organizing Fuzzy Maps can cluster food nutrient data which is incomplete data based on the content of its nutrition value. Based on these results, it can be proved that Self-Organizing Fuzzy Maps is a clustering algorithm that can be used on incomplete data.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Bahan Pangan; Clustering; Self-Organizing Fuzzy Maps; Food
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Q Science > QA Mathematics > QA248_Fuzzy Sets
Divisions: Faculty of Mathematics and Science > Mathematics > (S1) Undergraduate Theses
Depositing User: - TARA AMILA MILATINA
Date Deposited: 20 Mar 2017 04:12
Last Modified: 19 Dec 2017 07:51
URI: http://repository.its.ac.id/id/eprint/1996

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