Prediksi Risiko Depresi Pascapersalinan Menggunakan Algoritma K-Nearest Neighbor (KNN)
DOI:
https://doi.org/10.37859/jf.v15i2.9562
Abstract
Abstrak Depresi pascapersalinan merupakan gangguan kesehatan mental serius yang sering terlewat pada tahap awal, sehingga dapat menurunkan kualitas hidup ibu dan memengaruhi tumbuh kembang anak. Deteksi dini menjadi kunci, namun pemeriksaan manual kerap memakan waktu dan dipengaruhi bias subjektif. Penelitian ini bertujuan mengembangkan model prediksi risiko depresi pascapersalinan berbasis machine learning dengan algoritma K-Nearest Neighbor (KNN) yang dikenal sederhana, transparan, dan efektif. Dataset berjumlah 1.503 sampel dengan sepuluh atribut psikologis sebagai prediktor serta satu label target risiko depresi. Tahapan preprocessing mencakup imputasi nilai hilang menggunakan rata-rata, pengkodean variabel kategorikal dengan label encoding, serta normalisasi fitur melalui StandardScaler. Data dibagi menjadi 65% untuk pelatihan dan 35% untuk pengujian. Eksperimen dilakukan untuk menentukan nilai K optimal, dan diperoleh K = 15. Hasil evaluasi menunjukkan akurasi sebesar 98,86%, menandakan kemampuan tinggi dalam membedakan individu berisiko dan tidak berisiko depresi pascapersalinan. Model ini berpotensi digunakan tenaga kesehatan untuk skrining awal secara cepat, objektif, dan terstandarisasi, sekaligus mengurangi stigma sosial karena penilaian berbasis data. Meski demikian, penerapan klinis tetap harus memperhatikan keamanan data, keterbukaan hasil, serta mitigasi bias algoritmik agar manfaatnya dapat dirasakan secara adil dan luas.
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