Analisis SLR Terhadap Optimasi Penugasan Maksimum Untuk Meningkatkan Efisiensi Operasional Organisasi
Keywords:
Hungarian Method, Organizational Performance Evaluation, Maximum Assignment OptimizationAbstract
Penelitian ini bertujuan untuk menganalisis penerapan metode optimasi penugasan maksimum dalam meningkatkan efisiensi operasional organisasi melalui pendekatan Systematic Literature Review (SLR) dengan kerangka kerja PRISMA. Fokus kajian meliputi tiga konsep utama, yaitu Hungarian Method, Maximum Assignment Optimization, dan Organizational Performance Evaluation. Analisis dilakukan terhadap artikel-artikel ilmiah dari basis data Scopus periode 2023–2025 yang relevan dengan tema penelitian. Hasil kajian menunjukkan bahwa metode optimasi, khususnya Hungarian Method, terbukti efektif dalam mengalokasikan sumber daya secara optimal, mengurangi biaya operasional, dan meningkatkan produktivitas organisasi. Integrasi algoritma optimasi dengan teknologi kecerdasan buatan juga memperkuat akurasi dan efisiensi sistem pengambilan keputusan. Selain itu, evaluasi kinerja organisasi berperan penting dalam menilai efektivitas penerapan metode tersebut secara menyeluruh. Dengan demikian, penelitian ini memberikan kontribusi terhadap pengembangan strategi manajemen operasional yang adaptif dan berkelanjutan.
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Dhanasekar, S., Parthiban, V., & Gururaj, A. D. M. (2020). Improved hungarian method to solve fuzzy assignment problem and fuzzy traveling salesman problem. 9(11), 9417–9427.
Fetter, M., & Jónás, T. (2025). Forecasting the Number of Passengers on Hungarian Railway Routes Using a Similarity and Fuzzy Arithmetic-Based Inference Method.
Krapp, M., Schultze, W., & Weiler, A. (2023). Managerial performance evaluation and organizational form. January 2017, 1760–1794. https://doi.org/10.1111/1911-3846.12873
Moumin, I., & Scholar, E. (2024). ORGANIZATIONAL PERFORMANCE : A COMPREHENSIVE LITERATURE REVIEW OF. 28(4), 1–5.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Bmj, 372. https://doi.org/10.1136/bmj.n71
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Saroit, I. A., & Tarek, D. (2023). LBCC-Hung : A load balancing protocol for cloud computing based on Hungarian method. Egyptian Informatics Journal, 24(3), 100387. https://doi.org/10.1016/j.eij.2023.100387
Sumita, H. (n.d.). Online Task Assignment Problems with Reusable Resources. 1–23.
Szász, B., Heil, B., Kovács, G., & Mészáros, D. (2025). Comparison of Advanced Terrestrial and Aerial Remote Sensing Methods for Above-Ground Carbon Stock Estimation — A Comparative Case Study for a Hungarian Temperate Forest. 1–19.
Teknik, J., Medicom, I. C. I. T., Riandari, F., Dalimunthe, Y. A., Ginting, R., & Afifa, R. M. (2025). Hungarian maximization model approach for optimizing human resource assignment in multi-site projects. 17(2), 50–58.
Wang, T. (2025). A photovoltaic power ultra short-term prediction method integrating Hungarian clustering and PSO algorithm.
