Pengembangan Sistem Deteksi Dini Kehamilan Risiko Tinggi Berbasis Artificial Intelligence pada Program Integrasi Layanan Primer di Puskesmas Simalingkar
DOI:
https://doi.org/10.31004/joecy.v3i3.11143Keywords:
Artificial Intelligence; Deteksi Dini; Kehamilan Risiko Tinggi; Integrasi Layanan Primer; Random ForestAbstract
Angka Kematian Ibu (AKI) di Indonesia masih menjadi permasalahan kesehatan yang belum sepenuhnya teratasi. Sumatera Utara mencatat AKI sebesar 148 per 100.000 kelahiran hidup pada tahun 2022, yang lebih tinggi dibandingkan rata-rata nasional. Keterlambatan deteksi kehamilan risiko tinggi pada layanan primer menjadi salah satu faktor determinan kejadian komplikasi maternal. Program Integrasi Layanan Primer (ILP) membuka peluang transformasi digital pelayanan kesehatan maternal melalui integrasi teknologi Artificial Intelligence (AI). Penelitian ini bertujuan mengembangkan sistem deteksi dini kehamilan risiko tinggi berbasis AI pada Program ILP di Puskesmas Simalingkar Kota Medan. Penelitian menggunakan metode Research and Development (R&D) dengan model ADDIE (Analysis, Design, Development, Implementation, Evaluation) yang dilaksanakan pada periode September 2023 hingga Agustus 2024. Subjek penelitian adalah 150 ibu hamil yang melakukan kunjungan Antenatal Care (ANC) di Puskesmas Simalingkar, terdiri dari 85 kelompok risiko rendah, 40 kelompok risiko sedang, dan 25 kelompok risiko tinggi. Sistem dikembangkan berbasis web menggunakan Python-Django dan MySQL dengan algoritma Random Forest Classifier sebagai mesin klasifikasi risiko. Hasil evaluasi menunjukkan performa model yang sangat baik dengan akurasi 92,7%, presisi 91,4%, recall 90,8%, F1-Score 91,1%, dan AUC-ROC 0,94. Fitur dengan kontribusi tertinggi adalah tekanan darah sistolik (0,187), riwayat preeklamsia (0,152), kadar hemoglobin (0,134), usia ibu (0,121), dan Lingkar Lengan Atas/LILA (0,108). Hasil User Acceptance Test memperoleh skor 87,6% yang termasuk kategori "Sangat Baik". Sistem ini terbukti efektif mendukung skrining risiko kehamilan secara real-time dan dapat diintegrasikan dalam alur kerja bidan puskesmas pada Program ILP, berkontribusi pada upaya penurunan AKI melalui deteksi dini berbasis bukti.
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