Untuk menjabarkan model-model matematik tersebut di atas menjadi model komputer maka diperlukan dua macam alat bantu, yaitu block-diagram untuk mengarahkan algoritme perhitungan dan bahasa pemrograman yang bersifat umum, seperti BASIC, FORTRAN, atau PASCAL. Sebagai teladan ilustratif adalah perhitungan dugaan kehilangan tanah di suatu lokasi lahan tertentu dengan menggunakan model Wischmeier dan Smith (1978). Block diagramnya dapat disajikan dalam Gambar 8.
Mulai
Komponen Bio-ekonomi: Persiapan dan input data: Model-model usahatani Biofisik, sosek, sosbud, Model-model usahata-ternak demografis, dan lainnya
Model Alokasi/Optimasi Sumberdaya air :
Model-model hidrologi
Model-model hujan Output sistem DAS
Sumberdaya lahan: Selesai Model-model kualitas lahan
Model-model produktivitas Model-model degradasi Sumberdaya Manusia: Model-model demografi Model-model kependudukan Model-model dinamika sosial
Tujuan: Pola tanam aman erosi
dan layak ekonomi
Jenis tanaman yang sesuai
secara agroekologi dan
sosial-budaya
Pola pergiliran tanaman di lahan tegalan
B/C ratio Faktor Pengelo-
laan tanaman
(Faktor C)
Evaluasi kelayakan Evaluasi keamanan
ekonomi erosi
Pola pergiliran tanaman
yang aman erosi dan layak Toleransi erosi
ekonomi
Gambar 6. Diagram alir deskriptif penentuan pola pergiliran tanaman yang aman erosi dan layak ekonomi .
Data hujan, tanah, topo grafi, tanaman, landuse
Faktor R
Faktor K
Faktor LS
Evaluasi Erosivitas
Evaluasi erodibilitas
Kesesuaian lahan Tanaman ygsesuai
Pemetaan dan eva-luasi satuan lereng
Pendugaan erosi Indeks bahaya erosi RKLS,
IBE Evaluasi neraca le-ngas lahan setahun
Evaluasi pola pergi-liran tanaman
EVALUASI AGROTEKNOLOGI
Faktor P
Saran agrotekno-logi yg sesuai
Gambar 7. Diagram alir formulatif untuk menemukan agro teknologi yang aman erosi dan layak ekonomi (Soemarno, 1991).
RKLSCP
R
K LS
C P
Gambar 8. Diagram kotak perhitungan dugaan kehilangan tanah di suatu bidang lahan (Soemarno, 1991).
BAHAN BACAAN
Arsham H., 1990. What-if Analysis in Computer Simulation Models: A Comparative Survey with Some Extensions, Mathematical and Computer Modelling, 13(1), 101-106, 1990.
Arsham H., 1991. Perturbation Analysis in Discrete-Event Simulation, Modelling and Simulation, 11(1), 21-28, 1991.
Arsham H., 1992. A Simulation Technique for Estimation in Perturbed Stochastic Activity Networks, Simulation, 58(8), 258-267, 1992. Arsham H., 1996. Performance Extrapolation in Discrete-event Systems
Simulation, Journal of Systems Science, 27(9), 863-869, 1996. Arsham H., Algorithms for Sensitivity Information in Discrete-Event
Systems Simulation, Simulation Practice and Theory, 6(1), 1-22, 1998.
Arsham H., Feuerverger, A., McLeish, D., Kreimer J. and Rubinstein R., 1989. Sensitivity analysis and the what-if problem in simulation analysis, Mathematical and Computer Modelling, 12(1), 193-219, 1989.
Batmaz I., and S. Tunali, 2003. Small response surface designs for metamodel estimation, European Journal of Operational Research, 145(3), 455-470, 2003.
Bossel H., 1994. Modeling & Simulation, A. K. Peters Pub., 1994.
Delaney W., and E. Vaccari, 1989. Dynamic Models and Discrete Event Simulation, Dekker, 1989.
Fishman G., 2001. Discrete-Event Simulation: Modeling, Programming and Analysis, Springer-Verlag, Berlin, 2001.
Fishwick P., 1995. Simulation Model Design and Execution: Building Digital Worlds, Prentice-Hall, Englewood Cliffs, 1995.
Fu M., and J-Q. Hu, 1997. Conditional Monte Carlo: Gradient Estimation and Optimization Applications, Kluwer Academic Publishers, 1997. Fu M., and J-Q. Hu, 1997. Conditional Monte Carlo: Gradient Estimation
and Optimization Applications, Kluwer Academic Publishers, 1997. Ghosh S., and T. Lee, 2000. Modeling & Asynchronous Distributed
Simulation: Analyzing Complex Systems, IEEE Publications, 2000. Gimblett R., 2002. Integrating Geographic Information Systems and
Agent-Based Modeling: Techniques for Simulating Social and Ecological Processes, Oxford University Press, 2002.
Haas P., 2002. Stochastic Petri Net Models Modeling and Simulation, Springer Verlag, 2002.
Harrington J., and K. Tumay, 1998. Simulation Modeling Methods: An Interactive Guide to Results-Based Decision, McGraw-Hill, 1998. Headrick, T. 2002. Fast fifth-order polynomial transforms for generating
univariate and multivariate nonnormal distributions, Computational Statistics and Data Analysis, 40 (4), 685-711.
Hill D., 1996. Object-Oriented Analysis and Simulation Modeling, Addison-Wesley, 1996.
Ibidapo-Obe O., O. Asaolu, and A. Badiru, 2002. A New Method for the Numerical Solution of Simultaneous Nonlinear Equations, Applied Mathematics and Computation, 125(1), 133-140, 2002.
Karian Z., and E. Dudewicz, 1998. Modern Statistical Systems and GPSS Simulation, CRC Press.
Kleijnen J., and W. van Groenendaal, 1992. Simulation: A Statistical Perspective, Wiley, Chichester.
Korn G., 2005. Real statistical experiments can use simulation-package software, Simulation Modelling Practice and Theory, 13(1), 39-54. Kouikoglou V., and Y. Phillis, 2001. Hybrid Simulation Models of Production
Networks, Kluwer Pub., 2001.
Lamb J., and R. Cheng, 2002. Optimal allocation of runs in a simulation metamodel with several independent variables, Operations Research Letters, 30(3), 189-194, 2002.
Law A., and W. Kelton, Simulation Modeling and Analysis, McGraw-Hill, 2000.
Lewis P., and E. Orav, Simulation Methodology for Statisticians, Operations Analysts, and Engineers, Wadsworth Inc., 1989.
Madu Ch., and Ch-H. Kuei, 1993. Experimental Statistical Designs and Analysis in Simulation Modeling, Greenwood Publishing Group. Nelson B., Stochastic Modeling: Analysis & Simulation, McGraw-Hill, 1995. Oakshott L., Business Modelling and Simulation, Pitman Publishing,
London, 1997.
Pang K., Z. Yang, S. Hou, and P. Leung, 2002, Non-uniform random variate generation by the vertical strip method, European Journal of Operational Research, 142(3), 595-609.
Pidd M., Computer Simulation in Management Science, Wiley, 1998.
Rajasethupathy, P., S. J. Vayttaden, U. S. Bhalla, 2005. Systems modeling: A pathway to drug discovery. Curr. Opin. Chem. Biol. 9, 400–406 (2005).
Robert C., and G. Casella, 1999. Monte Carlo Statistical Methods, Springer.
Rollans S. and D. McLeish, 2002. Estimating the optimum of a stochastic system using simulation, Journal of Statistical Computation and Simulation, 72, 357 - 377.
Rubinstein R., and A. Shapiro, 1993. Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, John Wiley & Sons.
Rubinstein R., and A. Shapiro, 1993. Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, John Wiley & Sons.
Rubinstein R., and B. Melamed, 1998. Modern Simulation and Modeling, Wiley, 1998.
Severance F., 2001. System Modeling and Simulation: An Introduction, Wiley, 2001.
Simpson T., J. Poplinski, P. Koch, and J. Allen, 2001. Metamodels for Computer-based Engineering Design: Survey and Recommendations, Engineering with Computers, 17(2), 129-150, 2001.
Tsai C-Sh., 2002. Evaluation and optimisation of integrated manufacturing system operations using Taguch's experiment design in computer simulation, Computers And Industrial Engineering, 43(3), 591-604, 2002.
Van den Bosch, P. and A. Van der Klauw, 1994. Modeling, Identification & Simulation of Dynamical Systems, CRC Press, 1994.
Whitt W., 1984. Minimizing delays in the GI/G/1 queue, Operations Research, 32(1), 41-51, 1984.
Woods R., and K. Lawrence, Modeling and Simulation of Dynamic Systems, Prentice Hall, 1997.