PREDIKSI CHILD MALNUTRITION DENGAN ALGORITMA RANDOM FOREST

PREDIKSI CHILD MALNUTRITION DENGAN ALGORITMA RANDOM FOREST

• By Purnama, Jajang Jaya;
• Tesis
• 510
• -

Kesehatan adalah hal yang sangat penting untuk selalu diperhatikan apalagi setelah

seseorang di diagnosa penyakit tertentu dan menghamabat aktifitas, segala aktifitas

akan mendapatkan hasil yang maksimal ketika tubuh dalam keaadan sehat, apalagi

untuk balita sangat perlu perhatian untuk kesehatannya, pertumbuhan balita

seringkali dihantui dengan masalah stunting, underweight, wasting. Stunting adalah

kondisi dimana balita mengalami kekurangan gizi yang mengakibatkan balita

memiliki tinggi yang rendah dari anak seusianya. Balita mengalami wasting

ditandai dengan kurangnya berat badan menurut panjang/tinggi badan anak,

underweight dimana balita apabila badannya kurus relatif terhadap umurnya.

Dataset yang dipakai adalah BDHS 2014 yang kelas mayoritasnya terjadi

imbalance data. Dalam penulisan ini melakukan eksperimen machine learning

algoritma J.48, naïve bayes, random forest, support vector machine dan artificial

neural netwok menggunakan teknik sampling undersampling resample dan

oversampling SMOTE. Kesimpulannya menentukan klasifiksi malnutrition dengan

data yang imbalance sangat cocok menggunakan algoritma random forest dengan

teknik sampling undersampling resample yang bekerja dengan mengurangi sampel

kelas mayoritas, pengurangan ini dapat dilakukan secara acak.

• Katakunci Malnutrition, Bangladesh, Machine Learning, Random Forest
• URL http://
• Penerbit ,

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