Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Rianda, Muhamad
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sistem Perencanaan Gizi Harian Berbasis Optimasi Porsi dan Clustering K-Means Rianda, Muhamad; Viony, Viony; Azis, Taher Abdul; Riyanti, Nabillah April
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.13737

Abstract

Abstract — Personalized daily nutritional planning is a complex challenge due to the difficulty of translating individual nutritional needs into accurate food portions, exacerbated by the high prevalence of dual nutritional burdens in Indonesia. This study aims to design and implement an intelligent daily nutrition Decision Support System (DSS) capable of generating measured menu recommendations. The research method employs a hybrid approach, integrating an expert system knowledge base (Mifflin-St Jeor, FAO, IOM rules) with an inference engine based on dynamic portion optimization using linear programming (PuLP). Furthermore, unsupervised machine learning (K-Means) is applied to cluster food items to generate educational nutritional labels. The system was implemented as a web application using Python Flask and tested through case studies and functional verification. The main finding shows that the optimization engine successfully generated daily meal plans with specific grammages that closely approximated the target calories and macronutrients (case study caloric deviation <3%). The K-Means integration also proved effective in providing functional labels (e.g., "Pure Protein", "Energy Dense") for food items. This study concludes that a hybrid architecture based on dynamic portion optimization can provide a diet planning tool that is more quantitatively accurate and informative than traditional qualitative approaches.
Co-Authors Azis, Taher Abdul Riyanti, Nabillah April Viony, Viony