Kombinasi Algoritma Naive Bayes dan K-Means dalam Penentuan Status Gizi Stunting

Ariastuti Rahman, Astuti Abdullah, Salmawati Salmawati, Nurmarifah Nurmarifah

Abstract


Stunting tetap menjadi masalah kesehatan masyarakat yang kritis, ditandai dengan malnutrisi kronis pada anak di bawah lima tahun, yang menyebabkan gangguan pertumbuhan, peningkatan morbiditas, dan risiko kematian yang lebih tinggi. Di Indonesia, khususnya di Kabupaten Polewali Mandar, prevalensi stunting yang tinggi memerlukan pendekatan yang lebih akurat dan berbasis data untuk mendukung identifikasi dan intervensi dini. Namun, studi yang ada sebagian besar berfokus pada pendekatan metode tunggal dan jarang memberikan bukti komparatif antara teknik machine learning supervised and unsupervised untuk klasifikasi stunting, sehingga membatasi kekokohan sistem pendukung keputusan. Studi ini bertujuan untuk mengevaluasi dan membandingkan efektivitas teknik klasifikasi dan pengelompokan dalam menentukan status gizi stunting dan untuk mengusulkan pendekatan pendukung keputusan berbasis data bagi praktisi kesehatan. Penelitian ini menggunakan pendekatan data mining dengan mengintegrasikan Naive Bayes untuk klasifikasi dan K-Means untuk pengelompokan, menggunakan indikator antropometri dari dataset 173 anak di bawah lima tahun. Kinerja kedua metode dievaluasi menggunakan metrik akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa Naive Bayes secara signifikan mengungguli K-Means, mencapai accuracy 97%, precision 100%, dan recall 94%, dibandingkan dengan 54%, 58%, dan 60% secara berturut-turut. Temuan ini menyoroti keunggulan klasifikasi probabilistik dibandingkan pengelompokan tanpa pengawasan dalam konteks ini dan menunjukkan potensi Naive Bayes sebagai metode yang andal untuk mendukung klasifikasi stunting. Studi ini berkontribusi dengan mengatasi kesenjangan penelitian melalui analisis komparatif langsung dari pendekatan supervised dan unsupervised, menawarkan wawasan empiris dan landasan praktis untuk mengembangkan sistem pendukung keputusan cerdas di bidang kesehatan masyarakat.

Keywords


(K-Means; Naïve Bayes; Stunting).

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References


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DOI: https://doi.org/10.55340/jiu.v15i1.2681

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Program Studi Teknik Informatika, Fakultas Teknik, Universitas Dayanu Ikhsanuddin Jl. Dayanu Ikhsanuddin no.124 Baubau, Sulawesi Tenggara 

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