PREDIKSI CHILD MALNUTRITION DENGAN ALGORITMA RANDOM FOREST

• JAJANG JAYA PURNAMA
• 14002226

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ABSTRAK

ABSTRAK

 

 

 

Nama                           : Jajang Jaya Purnama

NIM                            : 14002226

Program Studi             : Ilmu Komputer

Jenjang                        : Strata Dua (S2)

Konsentrasi                 : Data Mining

Judul                           : “Prediksi Child Malnutrition dengan Algoritma Random Forest”

 

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.

 

Kata kunci: Malnutrition, Bangladesh, Machine Learning, Random Forest

KATA KUNCI

Data Mining,Algoritma Random Forest

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DAFTAR PUSTAKA

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