Penerapan Regresi Logistik Ordinal pada Hubungan Malnutrisi dengan Klasifikasi Pendapatan

Evinka, Bernike Ditia (2023) Penerapan Regresi Logistik Ordinal pada Hubungan Malnutrisi dengan Klasifikasi Pendapatan. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Malnutrisi terus menjadi masalah kesehatan masyarakat utama di hampir seluruh negara berkembang. Terdapat 11 juta kematian pada balita di negara berkembang setiap tahunnya, 40% diantaranya disebabkan karena malnutrisi, tidak hanya meningkatkan risiko kematian, tetapi juga meningkatkan risiko gangguan motorik, kognitif, dan perkembangan psikososial. Anak yang mengidap permasalahan pertumbuhan cenderung memiliki kecerdasan yang kurang maksimal. rentan terhadap penyakit dan beresiko menurunnya produktivitas. Terdapat juga konsekuensi sosial dari dampak malnutrisi misalnya, menurunnya pencapaian pendidikan, berkurangnya pendapatan, memperlambat pertumbuhan ekonomi dan menimbulkan kemiskinan berkepanjangan, sehingga hal ini perlu menjadi perhatian khusus karena dampak jangka panjang yang bersifat global pada generasi kedepannya. Penelitian ini akan menggunakan data sekunder dengan tipe data ordinal pada variabel dependen (indeks pendapatan), dan tipe data rasio pada variabel independent (5 jenis malnutrisi). Terdapat empat kategori pendapatan negara berdasarkan Gross National Income (GNI) perkapita yang telah di klasifikasikan oleh Bank Dunia yaitu Low Income, Lower Middle Income, Upper Middle Income dan High Income. Pada penelitian ini akan membahas pengaruh permasalahan gizi terdapat pendapan negara. Terdapat lima variabel permasalahan gizi yang akan menjadi perhatian yakni wasting, severe wasting, overweigh, stunting, dan, underweight. Setelah dilakukan pengujian hanya variabel stunting berpengaruh secara signifikan terhadap klasifikasi pendapatan. Menurut uji kesesuaian model, hasil analisis pengaruh malnutrisi terhadap klasifikasi pendapatan dengan menggunakan regresi ordinal dapat disimpulkan bahwa model telah sesuai atau tidak perbedaan yang berarti antara hasil observasi dengan hasil prediksi model.
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Malnutrition continues to be a major public health problem in almost all developing countries. There are 11 million under-five deaths in developing countries every year, 40% of which are caused by malnutrition, which not only increases the risk of death, but also increases the risk of impaired motor, cognitive and psychosocial development. Children with growth problems tend to have less than optimal intelligence. susceptible to disease and at risk of decreased productivity. Nutritional status has a strong relationship with economic growth, thus improving economic growth has an impact on increasing family income and improving nutrition in children. This study will use secondary data with ordinal data types on the dependent variable (income index), and ratio data types on the independent variables (5 types of malnutrition). There are four categories of state income based on per capita Gross National Income (GNI) which have been classified by the World Bank, namely Low Income, Lower Middle Income, Upper Middle Income and High Income. This study will discuss the influence of nutritional problems on state income. There are five variables of nutritional problems that will be a concern, namely wasting, severe wasting, overweight, stunting, and underweight. After testing, only the stunting variable has a significant effect on income classification. According to the model suitability test, the results of the analysis of the effect of malnutrition on income classification using ordinal regression can be interpreted that the model is appropriate or there is no significant difference between the results of observations and the prediction results of the model.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Income Classification, Malnutrition, Ordinal Logistic Regression, Klasifikasi Pendapatan, Malnutrisi, Regresi Logistik Ordinal
Subjects:	Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis
Depositing User:	Bernike Ditia Evinka
Date Deposited:	30 Jul 2023 12:43
Last Modified:	30 Jul 2023 12:43
URI:	http://repository.its.ac.id/id/eprint/100708

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