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ANALISIS METODE ANTREAN DAN SIMULASI MONTE CARLO PADA ANTREAN DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL (DISDUKCAPIL) KOTA SALATIGA DILENGKAPI GUI-R

*Diyah Rahayu Ningsih  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sugito Sugito  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Agus Rusgiyono  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2022 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
One of the services that often occurs in everyday life is the queue service. Queues can arise due to delays in a service system in providing a service, resulting in a row of a group of people to get a service. The queue analyzed in this study is a queue in The Salatiga City Disdukcapil. The parameters on which this research is based are the number of arrivals (λ) and service time (μ) of visitors who arrive. The methods used are queue analysis and Monte Carlo simulation. The Monte Carlo method provides more effective results at each counter than using queue analysis. The result of this study is a decrease in the utilization rate of service facilities, so that it is accompanied by a decrease in the size of system performance for the calculation of Lq, Ls, Wq, and Ws. Decreases in utilization rates and system performance measures at each counter make an increase in the probability of idle systems at each counter. The model generated by the sample data with the Monte Carlo simulation data tends to be the same, namely for counter 1,2,3,4, counter 5 model (G/G/c):(GD/¥/¥), and for counter 6 with queuing model ( G/M/1):(GD/¥/¥).
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Keywords: Sentiment Analysis Association; Brainly; K-Nearest Neighbor

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  1. Arifin, Miftahol. 2009. Simulasi Sistem Industri. Yogyakarta : Graha Ilmu
  2. A.Taha, H. (2007). Operation Research . Pearson Prentice Hall
  3. Bain, L.J., & Engelhardt, M. (1992). Introduction to Probability and
  4. Mathematical Statistics: Second Edition. California: Duxbury Press
  5. Daniel, W. W. 1989. Statistika Nonparametrik Terapan (terjemahan). Jakarta: PT. Gramedia
  6. Gross, D, & Harris, C. M. (1998). Fundamental of Queuing Theory 3rd. New
  7. York: John Wiley & Sons
  8. Hillier, F.S and Lieberman, G.J. 2015. Introduction To Operations Research. Tenth Edition. New York: Mc Graw Hill
  9. Kakiay, T. J. 2004. Dasar Teori Antrian Untuk Kehidupan Nyata. Yogyakarta: Andi
  10. Kotler, Philip. 2008. Manajemen Pemasaran. Edisi 12 Jilid 2. Jakarta: Indeks
  11. Muhajirin, & Disa, S. 2013. Penerapan Metode Monte Carlo dalam Pembuatan Perangkat Lunak Manajemen Aset pada PT.CAPRA KARYA. Edisi 2. Jurnal Inspiration. Vol. 3 No. 2. 1-8
  12. Nengsih, M., & Yustanti, N. 2017. Analisis Sistem Antrian Pelayanan
  13. Administrasi Pasien Rawat Jalan Pada Rumah Sakit Padmalalita Muntilan
  14. Management Insight. Vol. 12 No. 1. 68-78
  15. Sahab, N., & Butarbutar, F. 2019. Penerapan Model Simulasi Monte Carlo pada
  16. Line Assembling untuk Mengurangi Waktu Antrian di PT. XXX. Jurnal
  17. Industrikrisna. Vol. 14 No. 1. 27 – 33
  18. Sugito, & Fauzia, M. 2009. Analisis Sistem Antrian Kereta Api di Stasiun Besar
  19. Cirebon dan Stasiun Cirebon Prujakan. Media Statistika. Vol. 2 No. 2
  20. -120
  21. Walpole, R.E., Myers, R.H., Myers, S.L. & Ye,K. 1995. Probability and Statistics for Engineers and Scientist. Ninth Edition. New York: Pearson Education, Inc

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