ABSTRAKUmumnya kelahiran bayi sehat cukup bulan berada pada minggu 38-42 kehamilan. Namun ada banyak bayi yang terlahir pada usia kelahiran yang kurang mencukupi bahkan lahir dalam usia kelahiran yang lewat waktu. Hal ini menjadi hal yang serius mengingat banyak terjadi kematian bayi akibat usia kelahiran yang kurang mencukupi atau yang lewat waktu. Penelitian ini bertujuan untuk membuat aplikasi prediksi yang nantinya akan dapat membantu pasien dalam mengetahui usia kelahirannya dan mengantisipasi hal yang tidak diinginkan kedepannya. Metode yang digunakan merupakan metode Naïve Bayes dengan variable inputan faktor-faktor yang dialami oleh ibu hamil, diantaranya: usia ibu, tekanan darah, jumlah bayi, riwayat persalinan, riwayat abortus/ kuretase, malnutrisi, penyakit bawaan sebelum hamil dan masalah saat kehamilan. Hasil dari penelitian ini merupakan sebuah aplikasi yang dapat memprediksi usia kelahiran dengan nilai akurasi aplikasi tertinggi pada angka 78,69%, nilai precision tertinggi ada pada angka 70.14% dan nilai recall tertinggi ada pada angka 63.64%.Kata kunci: aplikasi, naïve bayes, prediksi, usia kelahiran. ABSTRACT Generally babies born at
The birth of a healthy baby is generally around 38-42 weeks of pregnancy. However, there are many babies born at an inadequate age of birth and the age of birth that is past its time. This study aims to predict the age of the birth of a patient. The method used is a classification with the Naïve Bayes algorithm with input variable (X), the factors experienced by pregnant women in the form of 8 variables X and Y variable in the form of Birth Age. Problems that arise are too many attributes that affect the results of accuracy. To overcome this, preprocessing is used with the Correlation Based Features Selection (CBFS) method. CBFS chose the X variables which had the highest correlation with the Y variable (Birth Age) but had the least correlation between the X variables. From the CBFS that had been done, produced 4 X variables, namely: blood pressure, number of babies, congenital diseases before pregnancy, and problems during pregnancy. The results of the test showed an increase in Precision, recall, and accuracy in the Naïve Bayes classification when implemented CBFS. The highest value of accuracy after preprocessing is 67% with an increase of 2 percent compared to before preprocessing.
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