Online trading activities through mobile devices (m-commerce) are currently becoming a business trend. Online trading has contributed to making the process of buying and selling goods and services easier and more enjoyable because they are timeless and borderless though these still have negative impacts. Therefore, this study examines retail consumer’s perspectives related to their readiness of using the application mobile commerce (m-commerce) in Kuningan regency. Furthermore, this study aims to examine the impact of optimism, innovation, insecurity, and discomfort on consumers’ shopping intentions from mobile devices through the perceived risks and benefits. To analyze the data, this study applied Parasuraman TRI (Technology Readiness Index) Model with the dimensions of optimism and innovation as variable drivers, the dimensions of insecurity and discomfort as inhibitor variables. Meanwhile, the data was gained through survey methods, questionnaire distribution from 397 m-commerce users in Kuningan as respondents. In addition, SPSS was used to test the validity and reliability of the questionnaire. The result showed that the total TRI value of 3.58 (in the high technology readiness index category), the people of Kuningan Regency tend to have a high level of readiness to adopt m-commerce technology, and the user group segment is in the pioneers group (this segment tends to have high interest through the existence of a new technology, have high optimism and innovation, but at the same time they will easily stop trying if they encounter high inconvenience and insecurity.

Abstrak�Studi untuk mempelajari perilaku elemen antrian (waktu tunggu nasabah, waktu pelayanan, waktu menganggur teller dan sebagainya) dalam suatu sistem dapat ditinjau dengan teori antrian. Elemen antrian, terutama waktu pelayanan saat transaksi, dapat dijadikan tinjauan dalam meningkatkan kepuasan nasabah. Kecenderungan kesibukan aktivitas pelayanan di bank adalah merata setiap harinya. Kondisi ini berbeda dengan Bank BNI 46 Cabang Universitas Kuningan yang mempunyai antrian yang sangat panjang pada waktu menjelang UAS. Model antrian bank tersebut dianalisis dengan mempertimbangkan distribusi pola kedatangan, distribusi pola pelayanan dan jumlah teller menggunakan �fit test. Hasil uji ini menunjukkan pola kedatangan berdistribusi Poisson dan pola pelayanan berdistribusi eksponensial. Dan karena jumlah teller adalah 1, maka mengikuti notasi Kendall, model antrian bank adalah M/M/1. Simulasi pelayanan transaksi pada bank dilakukan untuk melihat kecenderungan perilaku elemen antrian. Simulasi menunjukkan semakin besar tingkat pelayanan, semakin besar peluang waktu menganggur teller. Akan tetapi, semakin besar tingkat pelayanan, banyak nasabah dalam antrian dan dalam sistem antrian akan semakin sedikit. Demikian juga dengan waktu rata-rata nasabah dalam antrian dan dalam sistem antrian.Kata Kunci: Model Antrian, Perilaku Elemen Antrian, Simulasi.�Abstract�A research to study behavior of queue elements (customer waiting time, service time, idle time teller and so on) in a system can be reviewed with queuing theory. The queue element, especially the transaction time, can be used as a review to improve customer satisfaction. The tendency of busy activity in bank service is evenly distributed every day. This condition is different from BNI 46 Bank branch Kuningan University which has a very long queue at the time before final examination. The bank queue model is analyzed by considering the distribution of arrival pattern, service pattern distribution and number of teller using fit test. The results of this test show the pattern of arrival of Poisson distributed and exponential distribution service pattern. And since the number of tellers is 1, then following Kendall notation, the bank queue model is M / M / 1. Simulation of transaction service at bank is done to see tendency of queue element behavior. Simulations show the greater the level of service, the greater the chance of idle time teller will be. However, the greater the level of service, the more customers in the queue and in the queuing system will be less. Likewise, the average time the customer is in the queue and in the queuing system.�Keywords: Queue Model, Behavior of Element Queue, Simulation.