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Gaya hidup urban yang tidak sehat, konsumsi makanan ultra proses, dan keterbatasan akses terhadap intervensi gizi yang tepat turut memperparah permasalahan ini. Di sisi lain, pendekatan berbasis data dan teknologi, seperti machine learning dan Large Language Models (LLM), menawarkan peluang besar untuk meningkatkan efektivitas pengelolaan pola makan. Penelitian ini mengusulkan pengembangan sistem prediksi dan rekomendasi program diet berbasis machine learning dan terintegrasi LLM yang diterapkan pada Cafe NUT Castle, sebuah penyedia makanan sehat di Kota Malang. Sistem dikembangkan untuk mendigitalisasi pencatatan data komposisi tubuh pelanggan, memprediksi program diet (penurunan berat badan, peningkatan berat badan, dan penurunan lemak tubuh) menggunakan algoritma Random Forest, serta menghasilkan rekomendasi diet awal yang dipersonalisasi melalui integrasi Gemini Flash-Lite API. Berdasarkan hasil pengujian, model prediksi mencapai akurasi sebesar 93% pada data uji dan 84% pada 50 data baru. Evaluasi terhadap rekomendasi diet yang dihasilkan oleh LLM menunjukkan tingkat kelayakan sebesar 86,6% yang dikategorikan sebagai sangat layak. Hasil ini menunjukkan bahwa sistem yang dikembangkan tidak hanya akurat dalam memprediksi program diet, tetapi juga efektif dalam memberikan rekomendasi awal yang dapat mendukung pengambilan keputusan dalam layanan konsultasi gizi secara digital.","\u003Cp>Masalah malnutrisi, baik dalam bentuk kelebihan maupun kekurangan berat badan, masih menjadi tantangan kesehatan global. Gaya hidup urban yang tidak sehat, konsumsi makanan ultra proses, dan keterbatasan akses terhadap intervensi gizi yang tepat turut memperparah permasalahan ini. Di sisi lain, pendekatan berbasis data dan teknologi, seperti machine learning dan Large Language Models (LLM), menawarkan peluang besar untuk meningkatkan efektivitas pengelolaan pola makan. Penelitian ini mengusulkan pengembangan sistem prediksi dan rekomendasi program diet berbasis machine learning dan terintegrasi LLM yang diterapkan pada Cafe NUT Castle, sebuah penyedia makanan sehat di Kota Malang. Sistem dikembangkan untuk mendigitalisasi pencatatan data komposisi tubuh pelanggan, memprediksi program diet (penurunan berat badan, peningkatan berat badan, dan penurunan lemak tubuh) menggunakan algoritma Random Forest, serta menghasilkan rekomendasi diet awal yang dipersonalisasi melalui integrasi Gemini Flash-Lite API. Berdasarkan hasil pengujian, model prediksi mencapai akurasi sebesar 93% pada data uji dan 84% pada 50 data baru. Evaluasi terhadap rekomendasi diet yang dihasilkan oleh LLM menunjukkan tingkat kelayakan sebesar 86,6% yang dikategorikan sebagai sangat layak. 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