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All Journal International Journal of Research in Community Services International Journal of Quantitative Research and Modeling
Mulyo, Lukman Widoyo
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Physics Social Sciences Other

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Application of Genetic Algorithm on Knapsack Problem for Optimization of Goods Selection Hasanah, Indah Mauludina; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Hidayana, Rizki Apriva
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1020

Abstract

Knapsack Problemis one of the combinatorial optimization problems that often arise in everyday life, especially in making decisions about selecting goods with limited capacity. This study combines two previous studies that apply genetic algorithms to real cases: the selection of basic necessities and packaged fruits in limited containers. Genetic algorithms are used because they are flexible and able to find more than one optimal solution. The process includes the formation of an initial population, fitness evaluation, selection (roulette wheel), crossover, and mutation. From the two case studies analyzed, it was found that genetic algorithms consistently produce increased fitness between generations and are able to maximize the value of goods without exceeding capacity or budget limits. This study strengthens the potential of genetic algorithms as an effective method in solving Knapsack Problems based on real needs.
Application of Genetic Algorithm on Knapsack Problem for Optimization of Goods Selection Hasanah, Indah Mauludina; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Hidayana, Rizki Apriva
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1020

Abstract

Knapsack Problemis one of the combinatorial optimization problems that often arise in everyday life, especially in making decisions about selecting goods with limited capacity. This study combines two previous studies that apply genetic algorithms to real cases: the selection of basic necessities and packaged fruits in limited containers. Genetic algorithms are used because they are flexible and able to find more than one optimal solution. The process includes the formation of an initial population, fitness evaluation, selection (roulette wheel), crossover, and mutation. From the two case studies analyzed, it was found that genetic algorithms consistently produce increased fitness between generations and are able to maximize the value of goods without exceeding capacity or budget limits. This study strengthens the potential of genetic algorithms as an effective method in solving Knapsack Problems based on real needs.
Empowering High School Students with Software-Based Mathematical Skills for College and Career Readiness Megantara, Tubagus Robbi; Hidayana, Rizki Apriva; Nurkholipah, Nenden Siti; Amelia, Rika; Syarifudin, Abdul Gazir; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Agustin, Nemia
International Journal of Research in Community Services Vol. 6 No. 4 (2025): International Journal of Research in Community Service (IJRCS)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v6i4.1069

Abstract

In the current data-driven era, data literacy is a critical competency, yet many high school students lack practical training in essential software like Microsoft Excel, creating a significant skills gap. To address this challenge, the Mathematics Study Program at Universitas Kebangsaan Republik Indonesia conducted a community service program designed to empower high school students with foundational data analysis and visualization skills through a structured, hands-on Excel workshop. The program employed a phased approach, beginning with a socialization session for 31 students, followed by an intensive training workshop for a final cohort of 12 students. The workshop was segmented into three progressive modules: Foundations of Data Management, Logical Analysis and Data Interpretation, and Data Visualization. The program's effectiveness was evaluated using qualitative performance-based assessments and a feedback survey to measure changes in skill and confidence. The results indicate that the training was highly successful, demonstrably improving participants' competence and confidence as evidenced by overwhelmingly positive survey feedback. The foundational and data visualization modules were particularly effective, while the module on logical functions was identified as the most significant challenge for students. This initiative not only succeeded in delivering essential digital skills for college and career readiness but also offered valuable pedagogical insights, confirming the effectiveness of hands-on workshops and highlighting areas for refinement in technical education.
Co-Authors Abdul Gazir Syarifudin Agustin, Nemia Amelia, Rika Hasanah, Indah Mauludina Hidayana, Rizki Apriva Khan, Muhammad Fardeen Megantara, Tubagus Robbi Nenden Siti Nurkholipah, Nenden Siti