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Fitri Juniaty Simatupang, Fitri Juniaty
Universitas Diponegoro

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KLASIFIKASI RUMAH LAYAK HUNI DI KABUPATEN BREBES DENGAN MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION DAN NAIVE BAYES Simatupang, Fitri Juniaty; Wuryandari, Triastuti; Suparti, Suparti
Jurnal Gaussian Vol 5, No 1 (2016): Wisuda periode Januari 2016
Publisher : Jurusan Statistika UNDIP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (659.428 KB)

Abstract

House is a very basic need for everyone besides food and clothing. House can reflect the level of welfare and the level of health of its inhabitants. The advisability of a house as a good shelter can be seen from the structure and facilities of buildings.  This research aims to analyze the classification of livable housing and determine the criteria of houses uninhabitable. The statistical method used are the Learning Vector Quantization and Naive Bayes. The data used in this final project are data of Survei Sosial Ekonomi Nasional (Susenas) Kor Keterangan Perumahan in 2014 Quarter 1 district of Kabupaten Brebes. In this research, the data divided into training data and testing data with the proportion that gives the highest accurate is 95% for training data and 5% for testing data. Training data will be used to generate the model and pattern formation, while testing data used to evaluate how accurate the model or pattern formed in classifying data through confusion tables. The results of analysis showed that the Learning Vector Quantization method gives 71,43% of classification accuracy, while Naive Bayes method gives 95,24% of classification accuracy. The Naive Bayes method has better classification accuracy than the Learning Vector Quantization method.Keywords: House, Learning Vector Quantization, Naive Bayes, Classification
KLASIFIKASI RUMAH LAYAK HUNI DI KABUPATEN BREBES DENGAN MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION DAN NAIVE BAYES Simatupang, Fitri Juniaty; Wuryandari, Triastuti; Suparti, Suparti
Jurnal Gaussian Vol 5, No 1 (2016): Wisuda periode Januari 2016
Publisher : Departemen Statistika FSM Undip

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (659.428 KB)

Abstract

House is a very basic need for everyone besides food and clothing. House can reflect the level of welfare and the level of health of its inhabitants. The advisability of a house as a good shelter can be seen from the structure and facilities of buildings.  This research aims to analyze the classification of livable housing and determine the criteria of houses uninhabitable. The statistical method used are the Learning Vector Quantization and Naive Bayes. The data used in this final project are data of Survei Sosial Ekonomi Nasional (Susenas) Kor Keterangan Perumahan in 2014 Quarter 1 district of Kabupaten Brebes. In this research, the data divided into training data and testing data with the proportion that gives the highest accurate is 95% for training data and 5% for testing data. Training data will be used to generate the model and pattern formation, while testing data used to evaluate how accurate the model or pattern formed in classifying data through confusion tables. The results of analysis showed that the Learning Vector Quantization method gives 71,43% of classification accuracy, while Naive Bayes method gives 95,24% of classification accuracy. The Naive Bayes method has better classification accuracy than the Learning Vector Quantization method.Keywords: House, Learning Vector Quantization, Naive Bayes, Classification