Prediksi Stunting Berbasis Machine Learning melalui CERDIS: Cepat Resposif Deteksi Dini Stunting

Ni Putu Riza Kurnia Indriana; Ida Ayu Utari Dewi; Putu Ayu Ratna Darmayanti

doi:10.25047/j-remi.v7i1.6486

Penulis

Ni Putu Riza Kurnia Indriana Institut Teknologi dan Kesehatan Bali
Ida Ayu Utari Dewi Universitas Hindu Indonesia
Putu Ayu Ratna Darmayanti Institut Teknologi dan Kesehatan Bali

DOI:

https://doi.org/10.25047/j-remi.v7i1.6486

Kata Kunci:

deteksi stunting, machine learning, BCW, CRISP-DM, DeLone & McLean

Abstrak

Stunting merupakan penyakit yang memiliki dampak jangka panjang pada fungsi kognitif dan produktivitas, sehingga memerlukan upaya deteksi dini akurat untuk mendukung intervensi yang efektif. Penelitian ini bertujuan mengembangkan aplikasi prediksi stunting berbasis machine learning dengan algoritma Support Vector Machine (SVM) yang terintegrasi dalam sistem mobile dan web. Metode pengembangan mengacu pada kerangka CRISP-DM untuk proses data mining yang terstruktur, serta Behaviour Change Wheel (BCW) untuk mendorong perubahan perilaku pengguna. Dataset sebanyak 121 ribu entri dari Kaggle digunakan untuk pelatihan model. Hasil evaluasi melalui blackbox testing menunjukkan aplikasi memiliki kualitas sistem, informasi, dan layanan yang sangat baik dengan skor manfaat 98%. Aplikasi ini terbukti meningkatkan efisiensi diagnosis, akurasi identifikasi risiko Stunting, serta mendukung percepatan intervensi dini di Puskesmas. Implementasinya diharapkan dapat berkontribusi pada penurunan prevalensi Stunting dan transformasi digital layanan kesehatan primer. Puskesmas disarankan mengoptimalkan pelatihan tenaga medis, meningkatkan antarmuka pengguna, serta memperluas integrasi dan cakupan layanan agar efektivitas aplikasi semakin maksimal.

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