KLASIFIKASI STATUS STUNTING PADA BALITA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (KNN)
Keywords:
Stunting, Balita, K-Nearest Neighbor (KNN), Klasifikasi, Status GiziAbstract
Stunting merupakan kondisi gangguan pertumbuhan pada balita yang disebabkan oleh kekurangan gizi kronis dalam jangka waktu yang panjang. Kondisi ini masih menjadi salah satu permasalahan kesehatan masyarakat yang memerlukan perhatian karena dapat memengaruhi pertumbuhan fisik, perkembangan kognitif, serta kualitas hidup anak di masa mendatang. Dalam pelaksanaannya, proses pemantauan status gizi balita di lapangan masih banyak dilakukan secara manual melalui pencatatan pada grafik pertumbuhan. Cara tersebut berpotensi menimbulkan kesalahan pencatatan maupun keterlambatan dalam proses identifikasi status gizi. Penelitian ini bertujuan menerapkan metode K-Nearest Neighbor (KNN) untuk mengklasifikasikan status stunting pada balita berdasarkan data antropometri. Tahapan penelitian meliputi pengumpulan data, preprocessing, pembagian data latih dan data uji, serta pengujian beberapa nilai parameter k untuk memperoleh hasil klasifikasi yang sesuai. Melalui metode KNN, status gizi balita ditentukan berdasarkan tingkat kemiripan data dengan data balita yang telah diketahui status gizinya. Hasil penelitian menunjukkan bahwa algoritma KNN dapat digunakan untuk mengklasifikasikan status stunting berdasarkan karakteristik antropometri balita. Model yang dihasilkan kemudian diimplementasikan ke dalam aplikasi berbasis web untuk membantu petugas kesehatan dan kader posyandu dalam melakukan identifikasi awal status stunting. Sistem yang dibangun juga membantu proses pencatatan dan pengolahan data sehingga dapat mengurangi kemungkinan kesalahan dalam pendataan.
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