Analisis Klasifikasi Data Stunting pada Balita di Kota Bekasi Menggunakan Logistic Regression dan Random Forest - Dalam bentuk pengganti sidang - Artikel Jurnal
AZKA NADHIRA

Informasi Umum

Kode

25.04.1347

Klasifikasi

000 - General Works

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Data Science

Dilihat

72 kali

Informasi Lainnya

Abstraksi

Stunting in toddlers is one of the most pressing nutritional issues in Indonesia. This condition, caused by chronic malnutrition, is a key indicator of maternal and infant health, particularly during early life. Regular monitoring of toddler growth and development is necessary for stunting prevention. Therefore, developing accurate and efficient predictive models is essential. This study aims to analyze the performance of two Machine Learning models, namely Logistic Regression and Random Forest, in predicting stunting status in toddlers. The dataset used is toddler development data from the Kota Baru Health Center, Bekasi City. Due to the limited amount of data, the data split is done using k-fold cross-validation with k=5 to maximize the use of the dataset. Given the imbalanced data, with 39.4% stunting from the total data, an oversampling method was applied to the minority class. Logistic Regression was selected for its simplicity and interpretability, whereas Random Forest was favored for its ability to handle data complexity by combining predictions from multiple decision trees. The evaluation results indicate that Random Forest outperforms Logistic Regression, achieving an average accuracy of 88.07% and a macro-average F1-score of 87.32%, compared to Logistic Regression’s average accuracy of 85.48% and macro-average F1-score of 84.73%. This research is expected to support stunting prevention in Indonesia by providing a predictive approach based on Machine Learning to improve the monitoring of the growth and development of toddlers.

Koleksi & Sirkulasi

Tersedia 1 dari total 1 Koleksi

Pengarang

Nama	AZKA NADHIRA
Jenis	Perorangan
Penyunting	Putu Harry Gunawan
Penerjemah

Penerbit

Nama	Universitas Telkom, S1 Data Sains
Kota	Bandung
Tahun	2025

Sirkulasi

Harga sewa	IDR 0,00
Denda harian	IDR 0,00
Jenis	Non-Sirkulasi

Download / Flippingbook