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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Simetris AKSIOMA ILKOM Jurnal Ilmiah Jurnal Telematika Jurnal Teknik Informatika (JUTIF) Transformasi Alamtana
ali khumaidi, ali
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics

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A Hybrid Decision Support Framework for Food and Nutrition Security Assessment Using Multi-Criteria Decision Making and Machine Learning Solikin, Solikin; Wicaksono, Harjunadi; Setyarini, Tri Ana; Khumaidi, Ali; Darmawan, Risanto
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5474

Abstract

Food and nutrition security assessment requires an adaptive analytical approach due to the multidimensional and temporal complexity of food systems. This study proposes a hybrid decision support system integrating Multi-Criteria Decision Making (MCDM) methods, namely Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), with machine learning to evaluate and predict food security indicators dynamically. Panel data from West Java and East Nusa Tenggara for the period 2018–2024 were analyzed to capture structural and temporal characteristics. AHP was used to determine expert-based indicator weights, which were applied in TOPSIS to generate regional food security scores. These scores were subsequently modeled using machine learning with temporal feature engineering, including lag variables and rolling statistics, and evaluated using time-series cross-validation. The results reveal a strong negative correlation (−0.7398) between AHP weights and machine learning feature importance, indicating complementary expert-based and data-driven perspectives. Ridge Regression achieved the best predictive performance with an R² of 0.9983 on training data and 0.8186 under cross-validation. East Nusa Tenggara outperformed West Java in TOPSIS scores (0.4829 vs. 0.4626), highlighting the importance of food stability and utilization. This study advances Informatics by enabling dynamic and adaptive food security decision support.
Co-Authors Darmawan, Risanto Diajeng Reztrianti Harjunadi Wicaksono, Harjunadi Herwanto Herwanto Indra Permana Solihin Kartika, Harry Dwiyana Khuluk, Nazaruddin L. Chusna, Nuke Lelana, Angga Mulya Nur Hikmah Pranoto, Setiadi Wiro Ridwan Raafi'udin Setyawati, Kiki Solikin Solikin Sony Hartono Wijaya Tri Ana Setyarini Utomo, Alif Wahyono, Herry Wiga Ariani