ANALISIS KOMPARATIF KLASIFIKASI MACHINE LEARNING UNTUK MEMPREDIKSI STUNTING PADA ANAK USIA DI BAWAH LIMA TAHUN
Abstract
Stunting merupakan salah satu permasalahan kesehatan masyarakat yang bisa berdampak jangka panjang terhadap kualitas sumber daya manusia di Indonesia. Deteksi dini terhadap status stunting anak usia di bawah lima tahun menjadi langkah dalam mencegah gangguan pertumbuhan kronis akibat stunting, sehingga penelitian ini bertujuan untuk membangun model klasifikasi status stunting dengan memanfaatkan pendekatan data mining menggunakan algoritma Decision Tree dan Random Forest. Data yang digunakan diperoleh dari hasil survei terhadap ibu yang memiliki anak dibawah umur lima tahun dengan sejumlah 193 responden, data tersebut mencakup variabel antropometri dan sosial ekonomi, seperti tinggi badan, berat badan, usia anak, pendidikan orang tua, pendapatan keluarga, dan urutan kelahiran. data tersebut diproses melalui tahapan Knowledge Discovery in Databases (KDD) meliputi seleksi atribut, imputasi, encoding, dan klasifikasi melalui proses permodelan data mining, selanjutnya evaluasi dilakukan dengan metrik klasifikasi Classification Accuracy(CA) dan Area Under the Curve (AUC) dari kurva Receiver Operating Characteristic (ROC). Hasil penelitian menunjukkan bahwa model Random Forest memiliki performa lebih baik dibandingkan Decision Tree dengan nilai CA 71% dan AUC 0.74. dibandingkan Decision Tree dengan nilai CA 67% dan AUC 0.68. Peneliti berharap bahwa Model prdiksi ini berpotensial dapat digunakan sebagai sistem deteksi dini stunting berbasis data atau sebagai rujukan untuk penelitian berikutnya
Kata Kunci—Stunting, Machine Learning, Random Forest, Decision Tree, Classification Model, ROC Curve.
ABSTRACT
Stunting is one of the public health issues that can have long-term impacts on the quality of human resources in Indonesia. Early detection of stunting status among children under five years of age is a critical step in preventing chronic growth disorders. Therefore, this study aims to develop a classification model for stunting status using a data mining approach with Decision Tree and Random Forest algorithms. The dataset was obtained from a survey of 193 mothers with children under five, encompassing anthropometric and socioeconomic variables such as height, weight, child’s age, parental education, family income, and birth order. The data were processed through the stages of Knowledge Discovery in Databases (KDD), including attribute selection, imputation, encoding, and classification modeling. The model performance was evaluated using classification metrics: Classification Accuracy (CA) and the Area Under the Curve (AUC) from the Receiver Operating Characteristic (ROC) curve. The results show that the Random Forest model outperformed the Decision Tree, achieving a CA of 71% and an AUC of 0.74, compared to the Decision Tree with a CA of 67% and an AUC of 0.68. This predictive model is expected to be useful as a data-driven early detection system for stunting or serve as a reference for future research.
Keywords—Stunting, Machine Learning, Random Forest, Decision Tree, Classification Model, ROC Curve.
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DOI: https://doi.org/10.46576/syntax.v6i1.6852
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