The development of higher education is very rapid rise to the tight competition both public universities and private colleges. XYZ University realized to win the competition, required continuous quality improvement, including the quality of existing service facilities. Amenities quality services is believed to support the success of the learning activities and improve user satisfaction. This study aims to determine the extent to which the quality of the services effect on user satisfaction. The research method used is survey-based questionnaire that measure perception and expectation. The results showed a gap between perception and expectations of the respondents have a negative value for each item. This means XYZ service facility at the university is not currently meet the expectations of society members. Three service facility that has the lowest index is based on the perception of respondents is a laboratory (2.56), computer and multimedia (2.63) as well as wifi network (2.99). The magnitude of the correlation between satisfaction with the quality of service facilities is 0.725 which means a strong and positive relationship. The influence of the quality of service facilities to the satisfaction of the students is 0.525 meaning that the variable quality of the services facility can explain 52.5% of the variable satisfaction. The study provided recommendations for improvements to enhance the quality of services facility at the XYZ university facilities.

Abstract. Ut ilization of Informat ion and Communication Technology (ICT) is still relatively low in the level of SM Es due to various limitations ranging fro m access to capital, till the market ing network. ICT is present to provide the ability for SM Es in improving the benefits and competitive advantage of the organization. This study aims to determine the level of readiness of SMEs in utilizing technology, especially ICT. The methodology used is a survey to see the technology readiness of 107 SM Es in South Tangerang selected by purposive sampling. The approach used is TRI (Technology Readiness Index) which is the individual perception of technology based on four criteria that is optimism, innovative ness, discomfort and insecurity. The results showed that the optimis m and innovativeness variables significantly positively influence the technology readiness while the variables of disco mfort and insecurity also significantly positively influence the readiness of ICT.

No abstract

This study was conducted with the aim to the clustering of provinces in Indonesia of the risk of the COVID-19 pandemic based on coronavirus disease 2019 (COVID-19) data. This clustering was based on the data obtained from the Indonesian COVID-19 Task Force (SATGAS COVID-19) on 19 April 2020. Provinces in Indonesia were grouped based on the data of confirmed, death, and recovered cases of COVID-19. This was performed using the K-Means Clustering method. Clustering generated 3 provincial groups. The results of the provincial clustering are expected to provide input to the government in making policies related to restrictions on community activities or other policies in overcoming the spread of COVID-19. Provincial Clustering based on the COVID-19 cases in Indonesia is an attempt to determine the closeness or similarity of a province based on confirmed, recovered, and death cases. Based on the results of this study, there are 3 clusters of provinces.

No abstract

No abstract

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. In the simulation data is obtained that the data contained AO initial models are ARIMA (2,0,0) with MSE = 36,780, after the detection and correction of data obtained by the iteration of the model ARIMA (2,0,0) with the coefficients obtained from the regression       1 2 1 2 3 0,106 0, 204 0, 401 329 115 35,9 t t t Z Z Z X t X t X t         and MSE = 19,365. This shows that there is an improvement of forecasting error rate data.

No abstract