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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) SITEKIN: Jurnal Sains, Teknologi dan Industri Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat)
Daud, Nathan
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Automated menu planning for pregnancy based on nutrition and budget using population-based optimization method Kurnianingtyas, Diva; Daud, Nathan; Arai, Kohei; Indriati, Indriati; Marji, Marji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3483-3492

Abstract

Nutritional fulfilment during pregnancy depends on the budget. Meanwhile, nutrition is needed during pregnancy to keep the mother and fetus healthy. Therefore, this study aims to assist maternal nutrition planning by using population-based optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO), duck swarm algorithm (DSA), and whale optimization (WO) according to their nutritional needs at minimum cost. Additionally, this study compares the method performance to find the best method. There are 55 foods obtained from previous studies divided into five groups: staple food (SF), vegetables (VG), plant-source food (PS), animal-source food (AS), and complementary (CP). The model evaluation results show that GA's performance differed significantly from other models because it obtained the highest fitness by 439.73 and more variation in fitness results. Three models other than GA have no significant difference, but DSA performance obtained a superior fitness of 367.18. Furthermore, optimization methods must be combined with other artificial intelligence methods to develop innovative technology to support maternal nutrition and prevent stunting.
Comparison Genetics Algorithm and Particle Swarm Optimization in Dietary Recommendations for Maternal Nutritional Fulfillment Kurnianingtyas, Diva; Daud, Nathan; Indriati, Indriati; Muflikhah, Lailil
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 21, No 2 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v21i2.28937

Abstract

Fulfilling maternal nutrition is an NP-hard problem. Optimization techniques are required to solve its complexity. This issue is crucial as it affects the number of stunted toddlers in Indonesia. Stunting begins in the womb due to inadequate maternal nutrition during pregnancy. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are optimization methods applied to NP-hard problems, including medicine. Their performance has not been compared in this field. This study aims to identify an alternative method for recommending daily menus based on maternal nutritional needs. There are 55 food ingredients used to fulfill five menu parts: staple food (SF), vegetables (VG), plant source food (PS), animal source food (AS), and complementary (CP). Nutritional adequacy for prenatal is determined by Total Energy Expenditure (TEE) based on basal energy, daily activity, and stress levels. Results show PSO outperforms GA in average fitness values, 30.45 to 102.51, while GA excels in execution time, 0.33 to 23.22 seconds. PSO is preferred for effectiveness, and GA for efficiency, but given the problem's urgency, PSO is recommended. Exploring other metaheuristic methods is advised to enhance menu recommendation solutions for maternal nutrition. Additionally, expanding the food database is necessary for more varied maternal menu to support stunting prevention.
Enhancing Islamic Boarding School Management in Jombang through Artificial Intelligence Kurnianingtyas, Diva; Daud, Nathan; Widodo, Agus Wahyu; Muflikhah, Lailil; Yudistira, Novanto
TRI DHARMA MANDIRI: Dissemination and Downstreaming of Research to the Community (Journal of Community Engagement) Vol 5 No 2 (2025)
Publisher : SMONAGENES Research Center, Univeritas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtridharma.2025.005.02.143

Abstract

The drive for digital transformation and the need to enhance governance efficiency in religious educational institutions provided the backdrop for this community service program, which implemented artificial intelligence (AI) technology at an Islamic boarding school in Jombang. This program aimed to enhance the management competencies of Islamic boarding schools in Jombang by applying AI technology. The activities included socialization and training sessions on AI-based applications such as facial recognition, attendance systems, and student nutrition management tools. A one-group pretest–posttest design was employed to evaluate management competence before and after the training. The analysis showed a significant increase in participants’ scores from the pretest (23.86 ± 3.34) to the posttest (45.06 ± 1.56), with Z = –6.166 and p < 0.001. This improvement contributed to more efficient student attendance tracking, optimized data-based nutrition management, and motivated participants to integrate technology into pesantren administration. The practical implication of this program is the need for continuous training to expand further the adoption of artificial intelligence in other Islamic boarding schools.
Co-Authors Agus Wahyu Widodo, Agus Wahyu Indriati Indriati Kohei Arai Kurnianingtyas, Diva Lailil Muflikhah Marji Marji Novanto Yudistira