Strategi Pemilihan Leverage Point yang Tepat untuk Mendukung Keberhasilan Rencana Implementasi Perubahan Organisasi

Angga Sofyan Lutfi, Bambang Herawan Hayadi, Furtasan Ali Yusuf

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Perubahan dalam organisasi sering kali menghadapi tantangan signifikan yang memerlukan strategi implementasi yang efektif. Salah satu kunci keberhasilan adalah pemilihan leverage point yang tepat, yaitu titik-titik dalam sistem organisasi yang memberikan dampak terbesar ketika dimanipulasi. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis leverage point yang paling efektif dalam mendukung implementasi perubahan organisasi. Dengan pendekatan kualitatif, studi ini mengkaji berbagai teori perubahan organisasi. Hasil penelitian menunjukkan bahwa leverage point yang efektif meliputi aspek kepemimpinan, komunikasi, budaya organisasi, dan partisipasi karyawan. Selain itu, pentingnya pendekatan sistemik dalam memahami interkoneksi antar elemen organisasi diungkapkan sebagai faktor kunci dalam memilih leverage point. Rekomendasi yang dihasilkan dari penelitian ini diharapkan dapat membantu para pemimpin dan manajer dalam merancang strategi perubahan yang lebih berhasil dan berkelanjutan.

Kata kunci: pendekatan sistematik, kepemimpinan, implementasi perubahan

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Akter, A., Zafir, E. I., Dana, N. H., Joysoyal, R., Sarker, S. K., Li, L., Muyeen, S. M., Das, S. K., & Kamwa, I. (2024). A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation. Energy Strategy Reviews, 51(March 2023). https://doi.org/10.1016/j.esr.2024.101298

Aungkulanon, P., Hirunwat, A., Atthirawong, W., Phimsing, K., Chanhom, S., & Luangpaiboon, P. (2024). Optimizing maintenance responsibility distribution in real estate management: A complexity-driven approach for sustainable efficiency. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100239. https://doi.org/10.1016/j.joitmc.2024.100239

Avordeh, T. K., & Gyamfi, S. (2024). Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis. Heliyon, 10(4), e25807. https://doi.org/10.1016/j.heliyon.2024.e25807

Boutahri, Y., & Tilioua, A. (2024). Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings. Results in Engineering, 22(January), 102148. https://doi.org/10.1016/j.rineng.2024.102148

Cao, K., Zhou, C., Church, R., Li, X., & Li, W. (2024). International Journal of Applied Earth Observation and Geoinformation Revisiting spatial optimization in the era of geospatial big data and GeoAI. International Journal of Applied Earth Observation and Geoinformation, 129(December 2023), 103832. https://doi.org/10.1016/j.jag.2024.103832

Chan, K. M. A., Boyd, D. R., Gould, R. K., Jetzkowitz, J., Liu, J., Muraca, B., Naidoo, R., Olmsted, P., Satterfield, T., Selomane, O., Singh, G. G., Sumaila, R., Ngo, H. T., Boedhihartono, A. K., Agard, J., de Aguiar, A. P. D., Armenteras, D., Balint, L., Barrington-Leigh, C., … Brondízio, E. S. (2020). Levers and leverage points for pathways to sustainability. People and Nature, 2(3), 693–717. https://doi.org/10.1002/pan3.10124

Chidera Victoria Ibeh, Onyeka Franca Asuzu, Temidayo Olorunsogo, Oluwafunmi Adijat Elufioye, Ndubuisi Leonard Nduubuisi, & Andrew Ifesinachi Daraojimba. (2024). Business analytics and decision science: A review of techniques in strategic business decision making. World Journal of Advanced Research and Reviews, 21(2), 1761–1769. https://doi.org/10.30574/wjarr.2024.21.2.0247

Cui, T., Du, N., Yang, X., & Ding, S. (2024). Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach. Technological Forecasting and Social Change, 198(March 2023). https://doi.org/10.1016/j.techfore.2023.122944

Dash, A., Sethy, P. K., & Behera, S. K. (2023). Maize disease identification based on optimized support vector machine using deep feature of DenseNet201. Journal of Agriculture and Food Research, 14(August), 100824. https://doi.org/10.1016/j.jafr.2023.100824

De Grossi, F., & Circi, C. (2023). Nonlinear model predictive control leveraging quantum-inspired optimization in the three body problem with uncertainty. Acta Astronautica, 205(January), 68–79. https://doi.org/10.1016/j.actaastro.2023.01.028

Edunjobi, T. E. (2024). THE INTEGRATED BANKING-SUPPLY CHAIN ( IBSC ) MODEL FOR FMCG IN EMERGING MARKETS. 6(4), 531–545. https://doi.org/10.51594/farj.v6i4.992

Farhadi, F., Wang, S., Palacin, R., & Blythe, P. (2023). Data-driven multi-objective optimization for electric vehicle charging infrastructure. IScience, 26(10), 107737. https://doi.org/10.1016/j.isci.2023.107737

Gholami, M., Muyeen, S. M., & Lin, S. (2024). Optimizing microgrid efficiency: Coordinating commercial and residential demand patterns with shared battery energy storage. Journal of Energy Storage, 88(December 2023), 111485. https://doi.org/10.1016/j.est.2024.111485

Hauswirth, A., He, Z., Bolognani, S., Hug, G., & Dörfler, F. (2021). Optimization Algorithms as Robust Feedback Controllers. Annual Reviews in Control, 57(December 2023), 100941. https://doi.org/10.1016/j.arcontrol.2024.100941

Hu, H., Gong, S., & Taheri, B. (2024). Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm. Heliyon, 10(6), e27353. https://doi.org/10.1016/j.heliyon.2024.e27353

Jameel Al-Kamil, S., & Szabolcsi, R. (2024). Optimizing path planning in mobile robot systems using motion capture technology. Results in Engineering, 22(March). https://doi.org/10.1016/j.rineng.2024.102043

Kasper, L., Schwarzmayr, P., Birkelbach, F., Javernik, F., Schwaiger, M., & Hofmann, R. (2024). A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation. Applied Energy, 353(PB), 122192. https://doi.org/10.1016/j.apenergy.2023.122192

Lal, A., Ashworth, H. C., Dada, S., Hoemeke, L., & Tambo, E. (2022). Optimizing Pandemic Preparedness and Response Through Health Information Systems: Lessons Learned From Ebola to COVID-19. Disaster Medicine and Public Health Preparedness, 16(1), 333–340. https://doi.org/10.1017/dmp.2020.361

Lyu, D., Yang, F., Liu, B., & Gustafson, S. (2019). SDRL: Interpretable and data-efficient deep reinforcement learning leveraging symbolic planning. Electronic Proceedings in Theoretical Computer Science, EPTCS, 306.

Marwan, M., AlShahwan, F., Afoudi, Y., Ait Temghart, A., & Lazaar, M. (2023). Leveraging artificial intelligence and mutual authentication to optimize content caching in edge data centers. Journal of King Saud University - Computer and Information Sciences, 35(9), 101742. https://doi.org/10.1016/j.jksuci.2023.101742

Murifal, B. (2021). Strategi Manajemen Mengoptimalkan Kinerja dengan Konsep Beyond Budgeting. Ekonomis: Journal of Economics and Business, 5(1), 245. https://doi.org/10.33087/ekonomis.v5i1.318

Noordin, N. H., Eu, P. S., & Ibrahim, Z. (2023). FPGA Implementation of Metaheuristic Optimization Algorithm. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 6(July), 100377. https://doi.org/10.1016/j.prime.2023.100377

Olorunyomi Stephen Joel, Adedoyin Tolulope Oyewole, Olusegun Gbenga Odunaiya, & Oluwatobi Timothy Soyombo. (2024). Leveraging Artificial Intelligence for Enhanced Supply Chain Optimization: a Comprehensive Review of Current Practices and Future Potentials. International Journal of Management & Entrepreneurship Research, 6(3), 707–721. https://doi.org/10.51594/ijmer.v6i3.882

Rositch, A. F., Unger-Saldaña, K., Deboer, R. J., Ng’ang’a, A., & Weiner, B. J. (2020). The Role of Dissemination and Implementation Science in Global Breast Cancer Control Programs: Frameworks, Methods, and Examples. Cancer, 126(S10), 2394–2404. https://doi.org/10.1002/cncr.32877

Saha, C., Jana, D. K., & Duary, A. (2023). Enhancing production inventory management for imperfect items using fuzzy optimization strategies and Differential Evolution (DE) algorithms. Franklin Open, 5(June), 100051. https://doi.org/10.1016/j.fraope.2023.100051

Setyabudi, T. G., & Iswara, U. S. (2019). Perencanaan Laba Menggunakan Pendekatan Analisis Cost Volume Profit. Prosiding Seminar Nasional Dan Call for Papers UNISBANK (Sendi_U) Ke-5 Tahun 2019, 2018, 978–979.

Shafie, M. R., Khosravi, H., Farhadpour, S., Das, S., & Ahmed, I. (2024). A cluster-based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation. Decision Analytics Journal, 11(December 2023), 100461. https://doi.org/10.1016/j.dajour.2024.100461

Tao, B., & Kim, J. H. (2024). Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy. Journal of King Saud University - Computer and Information Sciences, 36(2), 101974. https://doi.org/10.1016/j.jksuci.2024.101974

Thantharate, P., Thantharate, A., & Kulkarni, A. (2024). GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks. Green Energy and Intelligent Transportation, 3(1), 100130. https://doi.org/10.1016/j.geits.2023.100130

Triana, D. H., Vitriana, N., & Suriyanti, L. H. (2020). Penerapan Analisis Cost-Volume-Profit Sebagai Alat Perencanaan Laba UD Sukma Jaya: Efektif atau Semu? Muhammadiyah Riau Accounting and Business Journal, 1(2), 054–062. https://doi.org/10.37859/mrabj.v1i2.1915

Uchenna Joseph Umoga, Enoch Oluwademilade Sodiya, Ejike David Ugwuanyi, Boma Sonimitiem Jacks, Oluwaseun Augustine Lottu, Obinna Donald Daraojimba, & Alexander Obaigbena. (2024). Exploring the potential of AI-driven optimization in enhancing network performance and efficiency. Magna Scientia Advanced Research and Reviews, 10(1), 368–378. https://doi.org/10.30574/msarr.2024.10.1.0028

Xiong, W., Zhu, D., Li, R., Yao, Y., Zhou, C., & Cheng, S. (2024). An effective method for global optimization – Improved slime mould algorithm combine multiple strategies. Egyptian Informatics Journal, 25(January), 100442. https://doi.org/10.1016/j.eij.2024.100442

Yaiprasert, C., & Hidayanto, A. N. (2024). AI-powered ensemble machine learning to optimize cost strategies in logistics business. International Journal of Information Management Data Insights, 4(1), 100209. https://doi.org/10.1016/j.jjimei.2023.100209

Yanto, M. (2020). Penerapan Cost – Volume – Profit (Cvp) Sebagai Dasar Perencanaan Laba Pada Cv. Usaha Bersama Tanjungpinang. Jurnal Dimensi, 9(2), 369–386. https://doi.org/10.33373/dms.v9i2.2547

Zhou, X., Xue, S., Du, H., & Ma, Z. (2023). Optimization of building demand flexibility using reinforcement learning and rule-based expert systems. Applied Energy, 350(May), 121792. https://doi.org/10.1016/j.apenergy.2023.121792




DOI: https://doi.org/10.37531/yum.v7i2.6658

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