Prediction of the Number of Arrivals of Training Students with the Monte Carlo Method
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Published:
Mar 31, 2022
Keywords:
Monte Carlo, Training, Prediction, Arrivals of Training, Simulation
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Abstract
The simulation of predicting student arrivals for training is an estimate of the calculation of the arrival rate of students in a period to conduct training. The number of student visits is too many, sometimes inversely proportional to the programmers who carry out learning, this causes the ongoing service to be less than optimal. This study aims to predict student arrivals in the future better. The data processed in this study were 3 periods sourced from the administration of a private company in West Sumatra. The data will be processed and calculated using the Monte Carlo method. The data were tested with various possible elements using a random sample. A powerful numerical calculation tool by simulating statistical data, this simulation obtains accurate values accurately from the physical form of the system that can be observed. The calculation implementation will be developed using an application-based system that will be built with the Hypertext Preprocessor (PHP) programming language. The system developed is easier and more relevant by applying Information Technology. The results obtained in predicting are 80% for 2017 and 84% for 2018. From the results of 80% accuracy in 2017 and 84% 2018 the system works very well to implement. Based on the results of data processing with the Monte Carlo method, it can be predicted that the number of student arrivals for training, as well as a good and fast decision-making process in the future.
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How to Cite
Sapriadi, S., Yunus, Y., & Dari, R. W. (2022). Prediction of the Number of Arrivals of Training Students with the Monte Carlo Method. Jurnal Informasi Dan Teknologi, 4(1), 9-13. https://doi.org/10.37034/jidt.v4i1.168
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References
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[16] Syahrin., E., Santony., J., & Na'am., J. (2019). Pemodelan Penjualan Produk Herbal Menggunakan Metode Monte Carlo.UPI YPTK Jurnal KomTekInfo Vol. 5, No. 3, Hal 33-41 ISSN: 2346-0010
[17] Astia., Y., Santony., J., & Sumijan. (2019). Prediction Of Amount Of Use Of Planning Family Contraception Equipment Using Monte Carlo Method (Case Study In Linggo Sari Baganti District). Indonesian Journal Of Artificial Intelegence and Data Mining (IJAIDM). Vol 2, No 1
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[3] Mastanbasheer, S., Sheu, G., Sai Dheeraj, M., Jaiswal, S., & Neyaz Imam, S. (2017). "A analytical study of depth profiling for MeV implants by using Monte Carlo and Taurus models". In 2017 6th International Symposium on Next Generation Electronics, ISNE 2017. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISNE.2017.7968720
[4] Novriansyah, A., Riswati, S. S., Bae, W., & Khalid, I. (2018). "Predicting Geothermal Reserves of Sorik-Marapi Field through Monte-Carlo Simulation Study". In Proceedings - 2018 2nd International Conference on Green Energy and Applications, ICGEA 2018 (pp. 5-9). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICGEA.2018.8356318
[5] De Castro Assis, S., do Couto Boaventura, W., Paulino, J. O. S., & Markiewicz, R. L. (2017). "Lightning Performance of Transmission Line with and without Surge Arresters: Comparison between a Monte Carlo method and field experience." Electric Power Systems Research, 149, 169-177. https://doi.org/10.1016/j.epsr.2017.04.012
[6] Bertot, Genaud & Gossa. (2018). "An Overview of Cloud Simulation Enhancement Using the Monte-Carlo Method". 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CCGRID.2018.00064
[7] Deepradit, S., Pisuchpen, R., & Ongkunaruk, P. (2017). "The harvest planning of aromatic coconut by using Monte Carlo simulation". In 2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017 (pp. 116-120). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IEA.2017.7939190
[8] El-Adawi, R., & Dessouky, M. (2017). "Monte Carlo general sample classification for rare circuit events using Random Forest". In SMACD 2017 - 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SMACD.2017.7981599
[9] Lapin, A. P., & Alsheva, K. V. (2017). "Investigation of conversion function for vortex sonic flowmeter using monte-carlo method". In Proceedings - 2017 2nd International Ural Conference on Measurements, UralCon 2017 (Vol. 2017-November, pp. 54-57). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/URALCON.2017.8120686
[10] Lyashenko, Y. O., Morozovich, V. V., & Liashenko, O. Y. (2017). "The lattice Monte Carlo method for calculating the effective diffusivities in the nanostructured two-phase media". In Proceedings of the 2017 IEEE 7th International Conference on Nanomaterials: Applications and Properties, NAP 2017 (Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/NAP.2017.8190401
[11] Oates, C. J., Girolami, M., & Chopin, N. (2017). "Control functionals for Monte Carlo integration. Journal of the Royal Statistical Society". Series B: Statistical Methodology, 79(3), 695-718. https://doi.org/10.1111/rssb.12185
[12] `1, D. C., Rossi, F., Seya, M., & Koizumi, M. (2018). "Delayed Gamma-Ray Spectroscopy Inverse Monte Carlo Analysis Method for Nuclear Safeguards Nondestructive Assay Applications". In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/NSSMIC.2017.8532898
[13] Xiaoyan, Z., Peng, Z., & Qisong, Z. (2018). "Research on information fusion method for mobile electronic commerce based on improved Monte Carlo algorithm under big data environment". In Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018 (pp. 3671-3675). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CCDC.2018.8407760
[14] Yan, X., Chen, J., & Yang, W. (2018). "Monte Carlo Analysis of Orbital Station Motion Parameter Errors Influence on Sar Azimuth Resolution Degradation". (pp. 7805-7808). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/igarss.2018.8518901
[15] Yudiana, Y., Hafidhuddin, D., & Ismal, R. (2018). "Pengukuran Risiko Operasional Pada Bank Syariah Indonesia (Studi Kasus Bank Syariah XYZ)". Jurnal Aplikasi Bisnis Dan Manajemen. https://doi.org/10.17358/jabm.4.2.179.
[16] Syahrin., E., Santony., J., & Na'am., J. (2019). Pemodelan Penjualan Produk Herbal Menggunakan Metode Monte Carlo.UPI YPTK Jurnal KomTekInfo Vol. 5, No. 3, Hal 33-41 ISSN: 2346-0010
[17] Astia., Y., Santony., J., & Sumijan. (2019). Prediction Of Amount Of Use Of Planning Family Contraception Equipment Using Monte Carlo Method (Case Study In Linggo Sari Baganti District). Indonesian Journal Of Artificial Intelegence and Data Mining (IJAIDM). Vol 2, No 1