Whirlwind Classification with Imbalanced Upper Air Data Handling using SMOTE Algorithm and SVM Classifier

Novitasari, Dian Candra Rini; Foeady, Ahmad Zoebad; Nariswari, Rinda; Asyhar, Ahmad Hanif; Ulinnuha, Nurissaidah; Farida, Yuniar; Santi, Dessy; Ilham, Ilham; Setiawan, Fajar

doi:10.1088/1742-6596/1501/1/012010

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Data related with stunting are not balanced between data for normal children and stunting sufferers; this problem can be overcome by using the Synthesis Minority Over Sampling Technique (SMOTE) method. This method poises the data by creating synthesis data in the data class that has the least amount [10], [11]. After the data is preprocessed, they are entered into a classification system.…”

Section: Faris Mushlihul Amin Dian Candra Rini Novitasari Identificat...mentioning

confidence: 99%

Identification of Stunting Disease using Anthropometry Data and Long Short-Term Memory (LSTM) Model

Amin

Novitasari

2022

ComEngApp

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Children with unbalanced nutrition are currently crucial health issues and under the spotlight around the world. One of the terms for malnourished children is stunting. Stunting is a disease of malnutrition found in children aged under 5 years; as many as 70% of stunting sufferers are children aged 0-23 months. There are several ways to diagnose stunting, one of which is using stunting anthropometry. Stunting anthropometry can measure the physique of children so that some of the features that characterize the presence of stunting can be identified. Features resulted from the stunting anthropometry cover age, height, weight, gender, upper arm circumference, head size, chest circumference, and hip fat measurement. The process of identifying stunting can be simplified using an intelligent system called the Computer-Aided Diagnosis (CAD) system. CAD system contains 2 main processes, namely preprocessing and classification. Preprocessing includes normalization and augmentation of data using the SMOTE method. The classification process in this study uses the LSTM method. LSTM is a modification of the Recurrent Neural Network (RNN) method by adding a memory cell so that it can store memory data for a long time and in large quantities. The results of this study compare between the results of models that apply preprocessing and the one without preprocessing. The model that only uses LSTM has the best accuracy of 78.35%; the model with normalization produces an accuracy of 81.53%; the model that uses SMOTE produces an accuracy of 81.66%; and the model that uses normalization and SMOTE produces the best accuracy of 85.79%.

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Section: Faris Mushlihul Amin Dian Candra Rini Novitasari Identificat...mentioning

confidence: 99%

Identification of Stunting Disease using Anthropometry Data and Long Short-Term Memory (LSTM) Model

Amin

Novitasari

2022

ComEngApp

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“…The image from the feature extraction stage is classified using SVM [38]- [40]. Image data is divided into test data and training data using the k-fold crossvalidation method.…”

Section: Classificationmentioning

confidence: 99%

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Mu'jizah

Novitasari

2021

J. Teknol. dan Sist. Komput

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Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

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Whirlwind Classification with Imbalanced Upper Air Data Handling using SMOTE Algorithm and SVM Classifier

Cited by 2 publications

References 2 publications

Identification of Stunting Disease using Anthropometry Data and Long Short-Term Memory (LSTM) Model

Identification of Stunting Disease using Anthropometry Data and Long Short-Term Memory (LSTM) Model

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

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