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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) Journal of Biopesticides and Agriculture Technology
Daud, Nathan
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science 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.
Genetic Algorithm for Optimizing Footwear Logistics Distribution Using the Capacitated Vehicle Routing Problem (CVRP) Marodiyah, Inggit; Kurnianingtyas, Diva; Daud, Nathan; Sari, Indah Apriliana; Taurusta, Cindy
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 23, No 1 (2025): December 2025
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.v23i1.37620

Abstract

Micro, small, and medium enterprises (MSMEs) are important economic drivers for Indonesia, especially in labor-intensive sectors like footwear manufacturing. MSMEs, though, face acute logistical problems because of heterogeneous customer demand, limited production capacity, and ever-increasing transportation costs. Few existing works have focused on monthly logistics planning for MSMEs in developing countries with realistic costing and demand structures. To develop and analyze a Genetic Algorithm (GA) optimization model to maximize profit within a constrained monthly footwear profit distribution network. To achieve this, we needed to assess how multi-retailer product allocation balance could be achieved with minimum operational constraints such as production caps, cost-efficient logistics, and streamlined processes. This study employed a quantitative experimental design approach and implemented a GA with real-valued chromosome representation, tournament selection, single-point crossover, and Gaussian mutation. The model was built using real data from a footwear MSME operating in the Lamongan and Tulungagung regions of Indonesia. The algorithm was implemented using Python and tested for reliability with 10 executed validations for independence. Within 60 generations, the GA maintained consistent convergence and achieved a final fitness value with a coefficient of variation of 0.24%. The optimized allocation achieved a net profit margin of 15.22% while utilizing the available production capacity (600 units/month). Because of increased profit contribution, greater-distance wholesale customers were served first despite incurring higher transport costs. The model had no constraint violation and reduced transportation costs to 1.45% of total revenue. Using GA to address multi-objective distribution challenges in the context of MSMEs appeared to have positive results, confirming the effectiveness of this approach. The proposed approach helps frame and guide critical allocation and routing decisions, which can be made within the boundaries of operational constraints. Further work is needed to incorporate stochastic demand modelling and multi-objective problem extensions and seek real-time application to bolster support for decision-making in dynamic scenarios.
From concrete jungles to urban gardens: AI-powered solutions for sustainable food production in cities Widjanarko, Alexander Imanuel; Daud, Nathan; Kurnianingtyas, Diva
Journal of Biopesticides and Agriculture Technology Vol. 2 No. 1: (February) 2025
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jbiogritech.v2i1.2025.2545

Abstract

Introduction: Urban agriculture in Indonesia faces critical challenges including agricultural land conversion, aging farmer workforce (39% over 55 years, only 21% millennials), and rural urban inequality. While deep learning technologies prove effective for agricultural optimization, Indonesia lags neighboring countries due to regulatory ambiguity, limited incentives, and low youth participation. This study develops Urfalogy, an artificial intelligence powered platform addressing three primary urban farming constraints: limited space, insufficient capital, and inadequate technology. Methods: This research employed Agile software development methodology integrated with deep learning. The You Only Look Once version 8 (YOLOv8) algorithm was utilized for environmental object detection and segmentation. Dataset preprocessing included multiple augmentation techniques: scaling, geometric transformation, brightness adjustment, contrast and color saturation modifications. The platform integrates nine features: artificial intelligence layout designer, plant variety recommender, plant health detection, soil monitoring with internet of things sensors, e-commerce, real time expert consultation, appointment scheduling, interactive tutorials, and analytics dashboard. Finding: Model training achieved optimal performance metrics at epoch 100: segment loss of 0.56756, recall of 90.01%, and mean Average Precision at intersection over union 0.50 (mAP50) of 90.715%. During inference, the model successfully identified environmental components (ceiling, wall, floor), enabling precise spatial mapping for garden layout design. The integrated platform demonstrates comprehensive end to end capability supporting complete urban farming workflow from planning through sales. Conclusion: Urfalogy represents a transformative solution effectively bridging Indonesia's urban agriculture gap through artificial intelligence, Internet of Things integration, and human centered design, significantly advancing sustainability, food security, and economic opportunities. Novelty/Originality of this article: This research uniquely combines deep learning-based spatial optimization with comprehensive platform ecosystem design, integrating YOLOv8 environmental analysis with real-time consultation and e-commerce, addressing specific technological, economic, and accessibility barriers in Indonesian urban agriculture.
Co-Authors Agus Wahyu Widodo, Agus Wahyu Indriati Indriati Kohei Arai Kurnianingtyas, Diva Lailil Muflikhah Marji Marji Marodiyah, Inggit Novanto Yudistira Sari, Indah Apriliana Taurusta, Cindy Widjanarko, Alexander Imanuel