CHERIA RAHMADHANI PANJAITAN, NPM 2109100018 (2025) IMPLEMENTASI PENDETEKSI FAKTOR STUNTING PADA ANAK USIA DINI MENGGUNAKAN METODE ALGORITMA C4.5. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Stunting merupakan masalah kesehatan serius yang berdampak pada pertumbuhan fisik dan perkembangan kognitif anak. Di Indonesia, angka stunting masih tinggi, terutama di daerah dengan keterbatasan akses terhadap layanan kesehatan dan gizi yang baik. Penelitian ini bertujuan untuk mengembangkan sistem deteksi dini faktor risiko stunting pada anak usia dini menggunakan C4.5. metode ini dipilih karena kemampuannya dalam mengklasifikasikan data dengan baik serta menangani atribut yang hilang dan data numeric. Data penelitian diperoleh dari Puskesmas Janji, Labuhanbatu, yang mencakup berbagai faktor seperti status gizi ibu, pendidikan orang tua, akses layanan kesehatan, dan kondisi sanitasi. Hasil penelitian menunjukkan bahwa algoritma C4.5 mampu mengidentifikasikan faktor risiko utama stunting dan menghasilkan model prediksi yang akurat. Dengan implementasi sistem berbasis data mining ini, diharapkan deteksi dini dan pencegahan stunting dapat dilakukan lebih efektif, sehingga mendukung program intervensi kesehatan masyarakat secara lebih optimal. Kata Kunci : Stunting, Algoritma C4.5, Deteksi Dini, Data Mining, Faktor Risiko, Prediksi Stunting, Klasifikasi Data ================================================================================================= Stunting is a serious health issue affecting children’s physical growth and cognitive development. In Indonesia, the stunting rate remains high, particularly in areas with limited access to healthcare and adequate nutrition. This study aims to develop an early detection system for stunting risk factors in early childhood using the C4.5 algorithm. This method was chosen for its ability to classify data effectively while handling missing attributes and numerical data. The research data was obtained form Puskesmas Janji, Labuhanbatu, covering various factors such as maternal nutrition status, parental education, healthcare access, and sanitation conditions. The findings indicate that the C4.5 algorithm can identify key stunting risk factors and produce an accurate predictive model. By implementing this data mining-based system, early detection and prevention of stunting can be carried out more effectively, supporting more optimal public health intervention programs. Keywords : Stunting, C4.5 Algorithm, Early Detection, Data Mining, Risk Factors, Stunting Prediction, Data Classification
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Stunting, Algoritma C4.5, Deteksi Dini, Data Mining, Faktor Risiko, Prediksi Stunting, Klasifikasi Data =============================== Stunting, C4.5 Algorithm, Early Detection, Data Mining, Risk Factors, Stunting Prediction, Data Classification |
| Subjects: | R Medicine > RJ Pediatrics > RJ101 Child Health. Child health services Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 19 May 2025 07:43 |
| Last Modified: | 19 May 2025 07:43 |
| URI: | http://repository.ulb.ac.id/id/eprint/1392 |
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