Analisis Resiko Obesitas Berdasarkan Aktivitas Fisik: Implementasi Metode Artificial Intelligence Machine Learning

Syam Hardwis; Jajat Jajat

doi:10.25157/jkor.v10i2.16884

Analisis Resiko Obesitas Berdasarkan Aktivitas Fisik: Implementasi Metode Artificial Intelligence Machine Learning

Autor(s): Syam Hardwis, Jajat Jajat

DOI: 10.25157/jkor.v10i2.16884

Abstract

Obesitas telah menjadi masalah global yang dihadapi oleh berbagai negara di seluruh dunia. Aktivitas fisik dan perilaku sedentari dianggap sebagai faktor kunci yang berkontribusi terhadap terjadinya obesitas. Penelitian ini bertujuan untuk menganalisis hubungan antara aktivitas fisik dan perilaku sedentari dengan indeks massa tubuh (BMI) menggunakan pendekatan algoritma machine learning. Sebanyak 280 mahasiswa Universitas Pendidikan Indonesia dari berbagai program studi berpartisipasi dalam penelitian ini, terdiri atas 101 laki-laki dan 179 perempuan berusia 17–23 tahun. Aktivitas fisik diukur menggunakan accelerometer Actigraph GT3X. Penelitian ini menggunakan tujuh algoritma machine learning, yaitu k-nearest neighbours (KNN), decision tree, random forest, dan Classification via Regression (CVR) untuk analisis risiko obesitas. Pengujian dilakukan dengan menggunakan perangkat lunak RapidMiner. Berdasarkan variabel aktivitas fisik, perilaku sedentari, dan status demografi, algoritma random forest menunjukkan akurasi tertinggi sebesar 71,09% dibanding algoritma lainnya. Demikian juga dengan sensitivitas, algoritma random forest paling tinggi dari algoritma lainnya sebesar 37,50%. Sementara untuk spesifisitas, algoritma decision tree paling tinggi dengan 77,5%. Aktivitas fisik, total Metabolic Equivalent of Task (MET), dan durasi perilaku sedentari merupakan faktor penting dalam memprediksi risiko obesitas. Oleh karena itu, promosi aktivitas fisik dan kebijakan kampus memiliki peran krusial dalam mengurangi prevalensi obesitas di kalangan mahasiswa

Keywords

Aktivitas fisik, artificial intelligence, BMI, machine learning, obesitas

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DOI: http://dx.doi.org/10.25157/jkor.v10i2.16884