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Penelitian ini bertujuan menganalisis hubungan kedua faktor tersebut terhadap indeks massa tubuh (BMI) melalui pendekatan machine learning. Sebanyak 280 mahasiswa Universitas Pendidikan Indonesia (101 laki-laki, 179 perempuan; usia 17–23 tahun) berpartisipasi, dengan aktivitas fisik diukur memakai accelerometer Actigraph GT3X. Model dievaluasi menggunakan RapidMiner dan empat algoritma (KNN, decision tree, random forest, CVR). Hasil menunjukkan random forest mencapai akurasi tertinggi 71,09% dan sensitivity 37,50%, sedangkan decision tree memiliki specificity tertinggi 77,5%. Aktivitas fisik, total MET, dan durasi sedentari menjadi prediktor penting, sehingga promosi aktivitas fisik serta kebijakan kampus dibutuhkan untuk menekan prevalensi obesitas.","Analisis Resiko Obesitas Berdasarkan Aktivitas Fisik: Implementasi Metode Artificial Intelligence Machine Learning  \nSyam Hardwis1, Jajat2*  \n12Program Studi Ilmu Keolahragaan, Universitas Pendidikan Indonesia, Bandung, Indonesia  \nEmail: [jajat_kurdul@upi.edu](jajat_kurdul@upi.edu)  \nABSTRACT  \nObesity has become a global issue faced by various countries worldwide. Physical activity and sedentary behavior are considered key factors contributing to the occurrence of obesity. This study aims to analyze the relationship between physical activity and sedentary behavior with body mass index (BMI) using a machine learning approach. A total of 280 students from Universitas Pendidikan Indonesia, representing various study programs, participated in this research, consisting of 101 males and 179 females aged 17–23 years. Physical activity was measured using an Actigraph GT3X accelerometer. This study employed four machine learning algorithms—k-nearest neighbours (KNN), decision tree, random forest, and Classification via Regression (CVR)—to analyze obesity risk. The analysis was conducted using RapidMiner software. Based on physical activity, sedentary behavior, and demographic status variables, the random forest algorithm achieved the highest accuracy at 71.09% compared to the other algorithms. Similarly, in terms of sensitivity, the random forest algorithm outperformed others with a score of 37.50% . Meanwhile, the decision tree algorithm recorded the highest specificity at 77.5% . Physical activity, total Metabolic Equivalent of Task (MET), and sedentary behavior duration are critical factors in predicting obesity risk. Therefore, promoting physical activity and implementing campus policies play a crucial role in reducing obesity prevalence among students.  \nKeywords: Physical activity, artificial intelligence, BMI, machine learning, obesity  \nABSTRAK  \nObesitas 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 jugadengan sensitivitas, algoritma random forest paling tinggi dari algoritma lainnya sebesar 37,50%. Sementara untukspesifisitas, 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.  \nKata Kunci: Aktivitas fisik, artificial intelligence, BMI, machine learning, obesitas  \nCara sitasi:  \nHardwis, S. dan Jajat. J. (2024). Analisis Resiko Obesitas Berdasarkan Aktivitas Fisik: Implementasi Metode Artificial Intelligence Machine Learning. Jurnal Keolahragaan, 10(2), 29-36  \nSejarah Artikel:  \nDikirim 26 November 2024, Direvisi 27 November 2024, Diterima. 27 November 2024  \nPENDAHULUAN  \nObesitas telah menjadi salah satu tantangan kesehatan masyarakat yang paling mendesak di abad ke-21 (Wyatt et al., 2006; Egger, & Dixon, 2014; ","cbCaihmksXMMmzSM","https://ap.wps.com/l/cbCaihmksXMMmzSM","pdf",278784,3,1,8,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang masalah obesitas dan faktor risiko\n## Peran aktivitas fisik serta perilaku sedentari\n# Metode Penelitian\n## Subjek penelitian dan karakteristik peserta\n## Pengukuran aktivitas fisik dan variabel penelitian\n## Implementasi machine learning dan evaluasi model\n# Hasil dan Pembahasan\n## Perbandingan akurasi, sensitivity, dan specificity antar algoritma\n## Faktor penting dalam prediksi risiko obesitas\n# Kesimpulan dan Implikasi\n## Upaya promosi aktivitas fisik dan kebijakan kampus","[{\"question\":\"Penelitian ini bertujuan menganalisis apa?\",\"answer\":\"Penelitian ini bertujuan menganalisis hubungan aktivitas fisik dan perilaku sedentari terhadap indeks massa tubuh (BMI) menggunakan pendekatan machine learning untuk menilai risiko obesitas.\"},{\"question\":\"Bagaimana aktivitas fisik diukur dalam penelitian ini?\",\"answer\":\"Aktivitas fisik diukur menggunakan accelerometer Actigraph GT3X.\"},{\"question\":\"Algoritma machine learning apa yang memberikan performa terbaik?\",\"answer\":\"Algoritma random forest memberikan akurasi tertinggi (71,09%) dan sensitivity tertinggi (37,50%), sedangkan decision tree mencatat specificity tertinggi (77,5%).\"},{\"question\":\"Faktor apa yang dinilai penting untuk memprediksi risiko obesitas?\",\"answer\":\"Aktivitas fisik, total Metabolic Equivalent of Task (MET), dan durasi perilaku sedentari merupakan faktor penting dalam memprediksi risiko obesitas.\"}]","Analisis Resiko Obesitas Berdasarkan Aktivitas Fisik - 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