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Optimasi Klasifikasi Status Gizi Balita Berdasarkan Indeks Antropometri Menggunakan Algoritma C4.5 Adaboost Classification

Sabtu, 21 Desember 2019 12:15:50 ,Oleh ,Dilihat : 53 x
Optimasi-Klasifikasi-Status-Gizi-Balita-Berdasarkan-Indeks-Antropometri-Menggunakan-Algoritma-C4.5-Adaboost-Classification
Infant health can be known one of them through the assessment of nutritional status. In general, Body Mass Index (BMI) has been used as a method for measuring the nutritional status of children. If there are two children who have same body weight and height, they may have different nutritional status. Whenever this occurs, the use of BMI for measuring the nutritional status shall be deemed less accurate. The anthropometry will be vital in measuring the nutritional statuss. The guidelines for determining the nutritional status Anthropometry parameters are selected and recommended which includes an assessment of the age, weight, body length or height. This research aims to build a model of C4.5 adaboost so it can recognize patterns and be able to classify the nutritional status of children into five classes: normal, fat, very fat, thin and very thin. The variables used in this classification is Gender, Age (Months), Weight (kg) Height (cm). C4.5 (decision tree) Method has a good performance in dealing with the classification of nutritional status but the C4.5 has a weakness in the class imbalance. Adaboost isone ofboosting methods that could reduce imbalances class by giving weight to the level of classification error which may alter the distribution of data. Experiments carried out by applying the adaboost method C4.5 to obtain optimal results and a good degree of accuracy. The experimental results obtained from C4.5 method show that accuracy is 89.53%, the error rate is 10.47%, while the results of C4.5 with adaboost show 90.23% accuracy and 9.77% error rate. It can be concluded in the classification of nutritional status of children with C4.5 and adaboost proven method to solve problems of class imbalance and improve the high accuracy and can reduce the level of classification error. Keywords: Child Nutrition Status Classification, Antropometrics Index, C4.5, Adaboost.

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