DOI: https://doi.org/10.14710/j.gauss.v11i1.33993

ANALISIS ANTREAN BUS NONPATAS JALUR TIMUR TERMINAL TIRTONADI KOTA SURAKARTA MENGGUNAKAN METODE BAYESIAN

Rizka Nur Faizah  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
*Sugito Sugito  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sudarno Sudarno  -  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

The queuing system relates to customers and service facilities. Queuing theory designs service facilities to address service requests. Queues occur if the service capacity is not sufficient to provide services to many customers. The queuing phenomenon occurs on non-patas buses on the eastern route of Tirtonadi Terminal, Surakarta with Surabaya, Karanganyar, Wonogiri, Purwodadi and Pedesaan buses. The Bayesian method combines information from current research and previous studies with similar cases, and produces a posterior distribution to form a queuing system model and measure of service system performance. The bus queuing system model for Surabaya, Karanganyar, Wonogiri and Purwodadi has a Gamma-distributed arrival and service pattern. Pedesaan buses has an arrival pattern with a Gamma distribution and a service pattern with an Inverse Gamma distribution. Each line has 1 bus line as a service system, FIFO queue discipline, the number of customer capacity and call sources is not limited. The Surabaya buses has the highest probability of 93.49% that the line is idle and the Pedesaan buses  has the highest probability that the line will be busy serving at 89.50%. The queuing system are considered good because the five lines of service facilities are able to meet customer needs.

 

Keywords: Tirtonadi Terminal, Bayesian, Posterior Distribution, Queue Models, System Performance Measures

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Keywords: Tirtonadi Terminal; Bayesian; Posterior Distribution; Queue Models; System Performance Measures

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