Andri Pranolo

Analysis of Student Satisfaction Toward Quality of Service Facility

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.

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

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. This shows that there is an improvement of forecasting error rate data.

Simple additive weighting method on intelligent agent for urban forest health monitoring

Urban forest is a land where the trees grow cohesively and tightly in urban area, which is defined as urban forest by the competent authority. The main functions of urban forest are to keep and restore the micro climate and aesthetic value; keep water; create balance and harmony of the urban physical environment, also support the biodiversity conservation of Indonesia. Its making keeps the sustainability of the forest which becomes important. Forest health monitoring is one of ways to keep urban forest sustainable. It is used to determine the precaution that can be done. This study aims to provide the analysis and design of intelligent agent as a decision maker in the urban forest health monitoring, with the utilization of Simple Additive Weighting (SAW) method. The result is the model of intelligent agents who can assist in determining the health level of urban forest.