Analisis Kelayakan Investasi Pembangunan Gudang Distribusi Baja Ringan PT. SAGUN LAGUNA Dengan Metode Capital Budgeting
Keywords:
Capital budgeting, Kelayakan investasi, Gudang baja ringan, NPV, IRRAbstract
Pertumbuhan industri konstruksi yang pesat meningkatkan permintaan material baja ringan secara signifikan. PT. Sagun Laguna berencana mengoptimalkan rantai pasoknya melalui pembangunan gudang distribusi baru. Penelitian ini bertujuan untuk menganalisis kelayakan investasi proyek tersebut dengan menggunakan pendekatan Capital Budgeting. Metode evaluasi finansial yang diterapkan meliputi Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PP), dan Profitability Index (PI) dengan asumsi umur ekonomis proyek selama 10 tahun dan biaya modal (WACC) sebesar 12%. Total investasi awal yang dibutuhkan adalah sebesar Rp 4.500.000.000. Hasil analisis kuantitatif menunjukkan nilai NPV sebesar Rp 1.841.455.000 (bernilai positif), IRR sebesar 21,54% (lebih besar dari WACC), Payback Period dicapai dalam waktu 4 tahun 2 bulan, dan Profitability Index sebesar 1,41 (> 1). Berdasarkan indikator-indikator finansial tersebut, investasi pembangunan gudang distribusi PT. Sagun Laguna dinyatakan layak untuk dijalankan
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REFERENCES
[1] M. Sigala, A. Beer, L. Hodgson, and A. O’Connor, Big Data for Measuring the Impact of Tourism Economic Development Programmes: A Process and Quality Criteria Framework for Using Big Data. 2019.
[2] G. Nguyen et al., "Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey," Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.
[3] C. Shorten and T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning," J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
[4] R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, vol. 7, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.
[5] K. Sivaraman, R. M. V. Krishnan, B. Sundarraj, and S. Sri Gowthem, "Network failure detection and diagnosis by analyzing syslog and SNS data: Applying big data analysis to network operations," Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9 Special Issue 3, pp. 883–887, 2019, doi: 10.35940/ijitee.I3187.0789S319.
[6] A. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, "A decentralized privacy-preserving healthcare blockchain for IoT," Sensors (Switzerland), vol. 19, no. 2, pp. 1–17, 2019, doi: 10.3390/s19020326.
[7] F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, "Quantifying uncertainty in internet of medical things and big-data services using intelligence and deep learning," IEEE Access, vol. 7, pp. 115749–115759, 2019, doi: 10.1109/ACCESS.2019.2931637.
[8] S. Kumar and M. Singh, "Big data analytics for healthcare industry: Impact, applications, and tools," Big Data Min. Anal., vol. 2, no. 1, pp. 48–57, 2019, doi: 10.26599/BDMA.2018.9020031.
[9] L. M. Ang, K. P. Seng, G. K. Ijemaru, and A. M. Zungeru, "Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges," IEEE Access, vol. 7, pp. 6473–6492, 2019, doi: 10.1109/ACCESS.2018.2887076.
[10] B. P. L. Lau et al., "A survey of data fusion in smart city applications," Inf. Fusion, vol. 52, no. January, pp. 357–374, 2019, doi: 10.1016/j.inffus.2019.05.004.
[11] Y. Wu et al., "Large scale incremental learning," Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2019-June, pp. 374–382, 2019, doi: 10.1109/CVPR.2019.00046.
[12] A. Mosavi, S. Shamshirband, E. Salwana, K. wing Chau, and J. H. M. Tah, "Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning," Eng. Appl. Comput. Fluid Mech., vol. 13, no. 1, pp. 482–492, 2019, doi: 10.1080/19942060.2019.1613448.
[13] V. Palanisamy and R. Thirunavukarasu, "Implications of big data analytics in developing healthcare frameworks – A review," J. King Saud Univ. - Comput. Inf. Sci., vol. 31, no. 4, pp. 415–425, 2019, doi: 10.1016/j.jksuci.2017.12.007.
[14] J. Sadowski, "When data is capital: Datafication, accumulation, and extraction," Big Data Soc., vol. 6, no. 1, pp. 1–12, 2019, doi: 10.1177/2053951718820549.
[15] J. R. Saura, B. R. Herraez, and A. Reyes-Menendez, "Comparing a traditional approach for financial brand communication analysis with a big data analytics technique," IEEE Access, vol. 7, pp. 37100–37108, 2019, doi: 10.1109/ACCESS.2019.2905301
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