NADIYA SAFITRI, NPM 2109100049 (2025) IMPLEMENTASI DATA TENTANG KASUS STUNTING GIZI PADA BALITA DAN ANAK MENGGUNAKAN METODE ALGORITMA APRIORI DAN NAÏVE BAYES DI PUSKESMAS PERLAYUAN. Skripsi thesis, Universitas Labuhanbatu.
![[img]](http://repository.ulb.ac.id/style/images/fileicons/text.png) |
Text
COVER.pdf Download (10MB) |
|
Text
BAB I.pdf Download (88kB) |
|
Text
BAB II .pdf Download (364kB) |
|
Text
BAB III.pdf Download (672kB) |
|
Text
BAB IV.pdf Download (1MB) |
|
Text
BAB V.pdf Download (33kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (182kB) |
Abstract
Stunting merupakan kondisi gagal tumbuh pada anak balita akibat kekurangan gizi kronis, infeksi berulang, dan stimulasi yang tidak memadai, terutama pada 1.000 hari pertama kehidupan. Prevalensi stunting di Kabupaten Labuhanbatu mencapai 23,9% dengan target penurunan hingga 14% di wilayah Puskesmas Perlayuan. Tantangan utama dalam pencegahan stunting adalah rendahnya tingkat pendidikan orang tua, buruknya sanitasi, serta keterbatasan akses layanan kesehatan. Penelitian ini bertujuan untuk menganalisis faktor risiko stunting dan memprediksi potensi kasus di masa depan menggunakan metode Algoritma Apriori dan Naïve Bayes. Algoritma Apriori digunakan untuk menemukan pola asosiasi antara faktor risiko, sedangkan Naïve Bayes digunakan untuk klasifikasi risiko berdasarkan data historis. Data yang digunakan berasal dari Puskesmas Perlayuan dan dianalisis menggunakan RapidMiner. Hasil penelitian menujukkan bahwa faktor seperti berat badan rendah, tinggi badan pendek, dan usia anak berkontribusi signifikan terhadap kejadian stunting. Model Naïve Bayes yang diterapkan dalam penelitian ini mencapai akurasi 100%, menunjukkan efektivitas dalam prediksi kasus stunting. Temuan ini dapat digunakan sebagai dasar pengambilan keputusan dalam program pencegahan stunting di wilayah Puskesmas Perlayuan serta meningkatkan kualitas pelayanan kesehatan bagi balita dan anak. Kata Kunci : Stunting, Algoritma Apriori, Naïve Bayes, Data Mining, Puskesmas Perlayuan ====================================================================================== Stunting is a condition of impaired growth in toddlers due to chronic malnutrition, recurrent infections, and inadequate stimulation, particularly during the first 1,000 days of life. The stunting prevalence in Labuhanbatu Regency reaches 23,9% with a reduction target of 14% in the Perlayuan Health Center area. The main challenges in stunting prevention include low parental education, poor sanitation, and limited access to healthcare services. This study aims to analyze stunting risk factors and predict future cases using the Apriori Algorithm and Naïve Bayes Method. The Apriori Algorithm is used to identify associative patterns among risk factors, while Naïve Bayes is applied to classify risk based on historical data. The data utilized in this study was collected from the Perlayuan Health Center area and analyzed using the RapidMiner platform. The results indicate that factors such as low body weight, short stature, and child’s age significantly contribute to stunting cases. The Naïve Bayes model implemented in this study achieved 100% accuracy, demonstrating its effectiveness in predicting stunting cases. These findings can serve as a basis for decision-making in stunting prevention programs in the Perlayuan Health Center area and improve the quality of healthcare services for infants and children. Keywords : Stunting, Apriori Algorithm, Naïve Bayes, Data Mining, Perlayuan Health Center
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Stunting, Algoritma Apriori, Naïve Bayes, Data Mining, Puskesmas Perlayuan =================================== Stunting, Apriori Algorithm, Naïve Bayes, Data Mining, Perlayuan Health Center |
| 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: | 02 May 2025 09:37 |
| Last Modified: | 02 May 2025 09:37 |
| URI: | http://repository.ulb.ac.id/id/eprint/1334 |
Actions (login required)
 |
View Item |
