Penerapan Algoritma Decision Making MCDM dan SAW Berbasis User-Personalize Profile dengan Fitur Food Preference

Richard Christoper Subianto; Laura Salsabilla Lutfiardhana; Neysavita Almira Haq; Peter Vallian; Bima Nafis Lazuardi; Cinantya Paramita

doi:10.33365/jtk.v20i2.1235

Richard Christoper Subianto Universitas Dian Nuswantoro, Semarang
Laura Salsabilla Lutfiardhana Universitas Dian Nuswantoro, Semarang
Neysavita Almira Haq Universitas Dian Nuswantoro, Semarang
Peter Vallian Universitas Dian Nuswantoro, Semarang
Bima Nafis Lazuardi Universitas Dian Nuswantoro, Semarang
Cinantya Paramita Universitas Dian Nuswantoro, Semarang

DOI: https://doi.org/10.33365/jtk.v20i2.1235

Keywords: MCDM, SAW, Obesitas,User Profile, Food Preference, Nutrisi, Sistem Pendukung Keputusan.

Abstract

Perubahan gaya hidup modern telah memicu pola konsumsi yang tidak seimbang dan meningkatkan prevalensi penyakit metabolik seperti obesitas dan diabetes. Masalah utama terletak pada kesulitan masyarakat dalam memahami kebutuhan nutrisi tubuh dan keterbatasan waktu tenaga medis untuk melakukan evaluasi diet secara personal. Penelitian ini bertujuan untuk menerapkan sistem pendukung keputusan yang memberikan rekomendasi makanan berbasis kondisi fisik dan preferensi pengguna. metode yang digunakan adalah Multi-Criteria Decision Making (MCDM) dengan algoritma Simple Additive Weighting (SAW) untuk menentukan peringkat makanan berdasarkan kriteria kalori, protein, lemak, dan karbohidrat. Parameter kesehatan pengguna ditentukan melalui perhitungan Body Mass Index (BMI) dan Total Daily Energy Expenditure (TDEE) untuk menghasilkan profil personalisasi. Hasil penelitian menunjukkan bahwa integrasi algoritma SAW mampu memberikan rekomendasi menu makanan yang akurat dan sesuai dengan kebutuhan nutrisi serta preferensi makanan pengguna (food preference). Sistem ini diharapkan dapat membantu individu dalam mengelola pola makan secara mandiri dan terstruktur.

References

F. Ferdowsy, K. S. A. Rahi, Md. I. Jabiullah, and Md. T. Habib, “A Machine Learning Approach for Obesity Risk Prediction,” Current Research in Behavioral Sciences, vol. 2, p. 100053, Nov. 2021, doi: 10.1016/j.crbeha.2021.100053.

World Health Organization, “Working for a Brighter, Healthier Future,” Working for a brighter, healthier future. Accessed: Dec. 18, 2025. [Online]. Available: https://www.who.int/publications/i/item/9789240093966

“SKI_2023-DALAM-ANGKA_BKPK_KEMENKES_compressed.pdf.” Accessed: Dec. 18, 2025. [Online]. Available: https://docu.bkkbndiy.id/wp-content/uploads/2024/05/SKI_2023-DALAM-ANGKA_BKPK_KEMENKES_compressed.pdf

D. Y. Niska, “Sistem Pendukung Keputusan Untuk Menentukan Menu Makanan Sehat Dengan Metode Simple Additive Weighting,” Jurnal Teknik dan Informatika, vol. 5, no. 2, pp. 1–5, Aug. 2018.

B. A. Mahendra, C. Supriyanto, C. Paramita, N. Z. B. M. Safar, and I. N. Dewi, “Development of a Smartphone-Based Cataract Detection System Using YOLOv10x and Ionic Framework with a UI/UX Centric Approach,” in 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML), Jun. 2025, pp. 1–5. doi: 10.1109/SIML65326.2025.11081150.

A. A. Haresta, C. Paramita, and W. D. Tjahtjono, “Development of ViScan: A Mobile Application for Skin Cancer Detection Using Ionic Framework and YOLOv10x,” Journal of Applied Informatics and Computing (JAIC), vol. 9, no. 3, pp. 1–5, Jun. 2025.

S. Nagati, “Diet Recommendation Systems Using Machine Learning: Approaches, Challenges, and Future Directions,” IRJET, vol. 6, no. 11, pp. 1872–1876, Nov. 2024, doi: 10.70127/irjedt.vol.6.issue12.1876.

D. Tsolakidis, L. P. Gymnopoulos, and K. Dimitropoulos, “Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review,” Informatics, vol. 11, no. 3, p. 62, Sep. 2024, doi: 10.3390/informatics11030062.

R. P. Laksana and C. E. B. Sanjaya, “Pemantauan Cepat Kadar Gizi Makanan Dengan Aplikasi Mobile Berbasis Ai Untuk Mencegah Kelebihan Konsumsi Karbohidrat,” JIK: Jurnal Ilmu Komputer, vol. 9, no. 01, pp. 46–51, Oct. 2024, doi: 10.47007/komp.v9i01.8586.

A. P. M. D. N. N. Kumari, T. P. Satya, B.Manikanta, A. P. Chandana, and Y. L. S. Aditya, “Personalized Diet Recommendation System Using Machine Learning,” International Journal of Engineering Research & Technology, vol. 13, no. 2, Mar. 2024, doi: 10.17577/IJERTV13IS020125.

J. H. Park, C. W. Lee, and C. Do, “Examining Users’ Acceptance Intention of Health Applications Based on the Technology Acceptance Model,” Healthcare, vol. 13, no. 6, p. 596, Jan. 2025, doi: 10.3390/healthcare13060596.

I. A. Rahman, “Tren Pengembangan Sistem Pendukung Keputusan Metode Simple Additive Weighting: Systematic Literature Review,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 7, no. 1, pp. 29–35, Jan. 2025, doi: 10.47233/jteksis.v7i1.1727.

C. Ünal and C. Çılgın, “Web-Based Expert System Design and Implementation for Personal Nutrition Planning,” Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, vol. 6, Jun. 2022, doi: 10.33461/uybisbbd.1013012.

H. Taherdoost, “Analysis of Simple Additive Weighting Method (SAW) as a MultiAttribute Decision-Making Technique: A Step-by-Step Guide,” j. of manag. sci. & eng. res., vol. 6, no. 1, pp. 21–24, Feb. 2023, doi: 10.30564/jmser.v6i1.5400.

K. Zioutos, H. Kondylakis, and K. Stefanidis, “Healthy Personalized Recipe Recommendations for Weekly Meal Planning,” Computers, vol. 13, no. 1, p. 1, Dec. 2023, doi: 10.3390/computers13010001.

C. Türkmenoğlu, A. Ş. Etaner Uyar, and B. Kiraz, “Recommending Healthy Meal Plans by Optimising Nature-Inspired Many-Objective Diet Problem,” Health Informatics J, vol. 27, no. 1, p. 1460458220976719, 2021, doi: 10.1177/1460458220976719.

D. E. Waluyo, C. Paramita, H. W. Kinasih, D. Pergiwati, and F. A. Rafrastara, “Aplikasi Prediksi IHSG Berbasis Web Dengan Integrasi Multi-Algoritma,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 9, no. 2, pp. 121–129, 2024.