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All Journal Journal of Computer Science and Research Journal of Technology and Computer (JOTECHCOM)
Aprilia, Katharina Tyas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science

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Optimization of Nutritional Meal Allocation Using the Greedy Algorithm : A Data – Driven Approach for Food Security in Indonesia Irwansyah Sitorus; Aprilia, Katharina Tyas; Muhammad Rasyid Ridha; Ricky Martin Ginting
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 4 (2024): October: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Food security and nutrition programs play a crucial role in improving public welfare, particularly in developing countries such as Indonesia. Efficient allocation of limited government resources to regions most in need remains a key challenge in reducing poverty and malnutrition. This study applies the Greedy Algorithm as a computational optimization method to determine the most effective and equitable distribution of nutritional meal program budgets cross Indonesian provinces. The algorithm prioritizes provinces with higher poverty rates and greater nutritional needs while ensuring that the total expenditure does not exceed the national budget constraint. By employing a data-driven approach and calculating the value-to-cost ratio for each province, the algorithm selects allocations that yield the maximum nutritional impact per unit of cost. The results indicate that the Greedy-based allocation model improves efficiency by approximately 18–25% compared to traditional allocation methods. This approach offers a transparent, adaptable, and computationally efficient framework that can support policymakers in enhancing food security, promoting social equity, and advancing sustainable development goals.
Comparison of Random Forest and Naïve Bayes Classifier Methods for Monkeypox Classification Aprilia, Katharina Tyas; Sitorus, Irwansyah Putera; Ridha, Muhammad Rasyid; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Monkey Pox is a disease caused by a virus with the genus orthopoxvirus that can infect humans. The initial symptoms of this disease are the appearance of lumps due to swollen lymph nodes, muscle pain, fever, feeling tired and weak. Although similar to Chickenpox, Monkey Pox is clinically difficult to distinguish from other smallpox diseases. This study aims to classify Monkey Pox disease using the "Monkey-Pox PATIENTS Dataset". Classification of Monkey Pox disease is done using Random Forest and Naïve Bayes methods. Random Forest produces higher accuracy than Naïve Bayes in classifying Monkey Pox disease, which is 69.24% with a k-fold value of 5 and the number of trees 64 using an unbalanced dataset with 6 attributes. While Naïve Bayes produces an accuracy of 68.56% using a dataset without balancing with 8 attributes (k-fold=5, kernel=Gaussian) and 9 attributes (k-fold=3 and 10, kernel=Gaussian).
Co-Authors Fachry Abda El Rahman Irwansyah Sitorus Muhammad Rasyid Ridha Muhammad Syahputra Novelan Ricky Martin Ginting Sitorus, Irwansyah Putera