This study examines the effect of Information Quality, Systems Quality and Service Quality on the user intention to reuse Employee Management Information System (SIMPEG) in University in the city of Surabaya, based on the theoretical foundation of DeLone and McLane Information Systems Success (ISS) Model. The distribution of questionnaire was conducted to 120 employees of different universities by means of stratified random sampling. The results showed that: (1) there is a significant positive effect of the System Quality on the Quality of Information, (2) there is a significant positive effect of the Information Quality on the Intention to Reuse, information related to the fulfillment of the user's needs; (3) there is a significant positive effect of the Quality of the Intention on system re-use, the system related to the fulfillment of the needs of users; (4) there is no effect of the Quality of Service to the Intention to Reuse. In the end, the results of this study provide an analysis and advice to The University officials that can be used as a consideration for Information Technology/Information System investment and development in accordance with the Success of Information System and Intention to Reuse model. Keywords: information system success model, intentions to reuse, information technology/information system.

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Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.

In a learning environment, emotional factors influence student motivation. Students emotion have an important role in students' capability to learn. The tendency of the students emotion are not easily recognizable in a short time. Twitter is a popular micro-blogging system especially for students. Students post tweet about activities, experiences, their feelings anywhere, anytime and in real time. Sentiment analysis on twitter produce content sentiment that represents the feelings and emotions of students. Sentiment analysis system was built using backpropagation method at the stage of classification. In this research backpropagation network and the classification results were tested using WEKA with multilayer perceptron classifier. The results of sentiment analysis with 30 student respondents are 33.33% tendency of positive emotions, neutral emotions tendency 53.33% and 13:33% negative emotional tendencies. The results are used as reference in providing the appropriate treatment of the students during the process of learning.