Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/120721
Title: Faktor-faktor yang Memengaruhi Anemia dan Defisiensi Besi pada Wanita Hamil di Indonesia
Other Titles: Factors Influencing Anemia and Iron Deficiency of Pregnant Women in Indonesia
Authors: Anisa, Rahma
Aidi, Muhammad Nur
Nurjanah, Nunung
Yuliana, Elis
Issue Date: 2023
Publisher: IPB University
Abstract: Wanita hamil berisiko tinggi mengalami anemia, defisiensi besi (DB), dan anemia defisiensi besi (ADB). Selama kehamilan, hal itu dikaitkan dengan berat badan lahir rendah, kelahiran prematur, dan peningkatan risiko kematian perinatal. Kondisi anemia, DB, ADB, dapat diprediksi dengan menggunakan metode regresi logistik multinomial. Namun, metode klasifikasi rentan terhadap data yang tidak seimbang. Salah satu metode yang dapat digunakan untuk mengatasi masalah ketidakseimbangan data adalah Random Over Sampling (ROS). Hasil metode regresi logistik multinomial menunjukkan bahwa faktor yang memengaruhi anemia dan defisiensi besi secara signifikan adalah usia kehamilan, status gizi, riwayat penyakit malaria, pendidikan, frekuensi konsumsi buah, dan frekuensi konsumsi sayuran. Model regresi logistik multinomial memberikan akurasi klasifikasi sebesar 44,559%. Penelitian ini juga menunjukkan bahwa tingkat ketepatan klasifikasi keempat kategori lebih seimbang setelah dilakukan Random Over Sampling.
Pregnant women are at high risk of anemia, iron deficiency (ID), and iron deficiency anemia (IDA). During pregnancy, it has been associated with low birth weight, premature delivery, and increased risk of perinatal mortality. Anemia, ID, IDA conditions can be predicted using multinomial logistic regression method. However, the classification method is prone to data imbalance. One method that can be used to overcome the problem of data imbalanced is Random Over Sampling (ROS). The results of multinomial logistic regression method indicated that outcome variable were significantly influanced by gestational age, nutritional status, malaria, education, frequency of fruit and vegetable consumption. Multinomial logistic regression model provided classification accuracy of 44,559%. This study also shows that the accuracy of the classification of the four categories is more balanced after Random Over Sampling.
URI: http://repository.ipb.ac.id/handle/123456789/120721
Appears in Collections:UT - Statistics and Data Sciences

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G14160042_Elis Yuliana.pdf
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G14160042_Lampiran.pdf
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