Deteksi Obesitas pada Anak Usia 6-12 tahun di Indonesia Menurut Kontribusi Macronutrients (Energi, Protein, Lemak, Karbohidrat, Serat) dengan Metode Regresi Logistik Biner dan Neural Networks (SOM-RBFNN)

Adiati, Hanny (2014) Deteksi Obesitas pada Anak Usia 6-12 tahun di Indonesia Menurut Kontribusi Macronutrients (Energi, Protein, Lemak, Karbohidrat, Serat) dengan Metode Regresi Logistik Biner dan Neural Networks (SOM-RBFNN). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Obesitas pada anak merupakan salah satu masalah kesehatan di Indonesia. Obesitas tergolong penyakit yang memiliki penyebab multifaktoral yang salah satu faktor penyebabnya ditentukan oleh asupan gizi yang masuk dalam tubuh. Macronutrients (energi, protein, lemak, karbohidrat dan serat) yang masuk dalam tubuh dapat menjadi salah satu faktor gizi utama untuk mendeteksi status obesitas pada anak. Regresi Logistik biner dan Radial Basis Neural Networks (RBFNN) merupakan metode yang sering digunakan dalam klasifikasi dan deteksi penyakit. Salah satu yang menjadi hal penting dalam RBFNN adalah inisialisasi pembobot awal. Self Organizing Maps (SOM) merupakan salah satu metode untuk mendapatkan inisialisasi pembobot awal pada RBFNN (SOM-RBFNN). Dalam peneltian ini, didapatkan hidden layer yang memberikan hasil terbaik adalah 2 hidden layer. Kesimpulan akhir yang didapatkan adalah regresi logistik biner dan neural networks dengan SOM-RBFNN memberikan ketepatan klasifikasi yang tidak jauh berbeda dalam upaya deteksi obesitas berdasarkan kontribusi macronutrients pada anak usia 6-12 tahun di Indonesia. ======================================================================================================== Nowaday obesity is become a common problem in Indonesia. Childhood obesity was become a serious problem because have a latent effect for their future. Obesity is classified as a disease which have a multifactoral causes that one of contributing factor is determined by the intake of nutrients which actually consume by human. Macronutrients (energy, protein, fat, carbohydrate and fiber) that enters the body can be one of the major nutritional factor to detect the status of obesity in children. Binary Logistic Regression and Radial Basis Neural Network (RBFNN) is a method often used in classification and detection of disease. Self Organizing Mapping (SOM) is one method to get the initial weighting of RBFNN. The aim of this study was to classify obesity by binary logistic regression and neural networks. All subjects are children 6-12 years old measured by ministry of health in Indonesia. This research effort to detect obesity using binary logistic regression method and RBFNN based SOM (SOM-RBFNN). In this research 2 hidden layer known as the best node in hidden layer of SOM-RBFNN. The conclusion is neural networks and logistic regression both were good classifier for obesity detection and they were not significantly different in classification obesity of children 6-12 years age in Indonesia based on macronutrients contribution (Energy, Protein, Fat, Carbohydrate, Fiber).

Item Type: Thesis (Undergraduate)
Additional Information: RSSt 519.536 Adi d 3100014056693
Uncontrolled Keywords: Obesitas anak, Macronutrientst, Klasifikasi, Regresi Logistik Biner, Self Organizing mapping (SOM), Radial Basis Function Neural Network (RBFNN), Ketepatan Klasifikasi.
Subjects: Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions: Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User: Yeni Anita Gonti
Date Deposited: 30 Dec 2020 03:57
Last Modified: 30 Dec 2020 03:57
URI: https://repository.its.ac.id/id/eprint/82351

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