Background: High number of complaints that have been filed about the performance of online taxi services has prompted research on customer satisfaction factor analysis. Substantial research has addressed customer satisfaction factors in online taxi services, but none of them investigated the satisfaction in using the mobile apps.Objective: This study aims to find out the level of customer satisfaction and customer satisfaction factors in the online taxi mobile app services.Methods: This study is quantitative in nature, using questionnaires and purposive sampling method. The Customer Satisfaction Index (CSI) and Important-Performance Analysis (IPA) were used to determine the customer satisfaction factors, with the variables being route detection, connection, interaction, content, and service quality; as well as customer satisfaction, customer’s complaint, and customer loyalty. The data was processed using SPSS software.Results: The results showed that the level of customer satisfaction was 76.117% and fell into Cause of Concern category. This means that the system performance did not meet customer expectations. The results also showed that the best three factors in online taxi mobile apps are route detection, interaction, and content quality. Meanwhile, the factors that caused customer dissatisfaction were connection and service quality. The variables that led to satisfaction need to be maintained and the variables that did not were in Quadrant 1.Conclusion: The customer satisfaction was low so it is advisable that the companies immediately take an action to improve their performance and revise their strategic planning. In doing so, they must prioritize the attributes which have the biggest gap because these are the ones that will improve customer satisfaction.

Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results:  The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.