Klasifikasi Penyakit Stunting Menggunakan Algoritma Multi-Layer Perceptron

PUTRI INTAN ASHURI; INDAH ARDHIA CAHYANI; CHRISTIAN SRI KUSUMA ADITYA

doi:10.26760/mindjournal.v9i1.52-63

Klasifikasi Penyakit Stunting Menggunakan Algoritma Multi-Layer Perceptron

PUTRI INTAN ASHURI, INDAH ARDHIA CAHYANI, CHRISTIAN SRI KUSUMA ADITYA

Sari

Abstrak

Stunting adalah gangguan pertumbuhan dan perkembangan yang disebabkan kekurangan gizi yang ditandai dengan tinggi anak kurang dari dua kali standar deviasi yang ditetapkan oleh WHO. Kekurangan asupan gizi mengakibatkan menurunnya pertumbuhan anak, hal ini berhubungan dengan meningkatnya resiko sakit, kematian, hambatan pertumbuhan fisik maupun gangguan metabolisme tubuh. Beberapa metode telah dilakukan untuk membantu mengklasifikasi stunting pada anak salah satunya C4.5. Tujuan penelitian ini adalah mengklasifikasikan penyakit stunting menggunakan metode Multi-Layer Perceptron (MLP) dengan hyperparameter tuning RandomSearchCV. MLP memiliki beberapa kelebihan diantaranya mampu merepresentasikan hubungan lebih kompleks antara fitur input dan output, serta memproses data dalam berbagai bentuk, termasuk data tidak terstruktur. Penelitian ini menunjukan model MLP menggunakan hyperparameter tuning RandomSearchCV mendapatkan performa terbaik berdasarkan hasil evaluasi didapatkan accuracy sebesar 81.78%, precision 85.00%, recall 94.34%, dan F1-Score 89.43%.

Kata kunci: Stunting, Kekurangan gizi, Multi-Layer Perceptron (MLP), Hyperparameter tuning, RandomSearchCV

Abstract

Stunting is a growth and development disorder caused by malnutrition which is characterized by a child's height being less than twice the standard deviation set by WHO. Lack of nutritional intake results in decreased growth in children, this is associated with an increased risk of illness, death, physical growth restrictions and metabolic disorders. Several methods have been used to help classify stunting in children, one of which is C4.5. The aim of this research is to classify stunting using the Multi-Layer Perceptron (MLP) method with RandomSearchCV hyperparameter tuning. MLP has several advantages, including being able to represent more complex relationships between input and output features, as well as processing data in various forms, including unstructured data. This research shows that the MLP model using RandomSearchCV hyperparameter tuning got the best performance based on the evaluation results, which obtained accuracy of 81.78%, precision of 85.00%, recall of 94.34%, and F1-Score of 89.43%.

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Referensi

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DOI: https://doi.org/10.26760/mindjournal.v9i1.52-63