Public Services, Public Acceptance, and Satisfaction: Macro Evaluation of Government Services in Sigi Regency

The background of this research is the justification of findings in the form of public services in Indonesia in general, which are not good and not satisfactory. The objectives of this research are; 1) testing the level of public satisfaction with the implementation of basic service programs in Sigi Regency (14 indicators), and; 2) testing the level of public acceptance of the implementation of development programs in Sigi Regency (9 indicators). We use a mix-method approach in analyzing the results of public satisfaction and public acceptance to obtain the depth of data and field results. The results showed that the index of public acceptance of public services in Sigi Regency was 3.92, which means that the majority of Sigi Regency people received local government programs simultaneously. Partially, there are three dimensions in the form of program effectiveness, program efficiency, and dimensions of trust concerning the index of public acceptance of public services in Sigi Regency which is below the average score. However, this dimension does not simultaneously affect the level of public trust in public services in Sigi Regency with a total increase of 76.02%. The implication of this research is the existence of alternative strategies for improvement to improve public acceptance (IPM) and public satisfaction (IKM), including efficiency and public trust in the public acceptance index (IPM) framework, as well as safety and comfort within the public satisfaction index framework (IKM).Keywords: public service; public acceptance index; public satisfaction index; Sigi Regency

Modified Usability Test Scenario: User Story Approach to Evaluate Data Visualization Dashboard

The data processing results are commonly displayed in a dashboard with various graphic visualization forms to deliver new knowledge easier to understand by users. However, many data analysis dashboards cannot communicate the knowledge effectively and efficiently given the unsuitable design implementation. Therefore, research to measure the interface display's effectiveness in the data analysis system is deemed necessary. This research proposed a scenario modification in the usability test with a user story approach to measuring the system interface display in delivering the information to users. The approach of a usability test with the user story is expected to be capable of helping the researcher in understanding the user habits indirectly. There were 20 participants to validate the proposed method. Participants were asked to use the system and answer several questions to develop their user experience. After developing user experience for each user, the System Usability Scale (SUS) was conducted. SUS score results obtained from this research was 75.25. Besides, the researcher also measured the understanding level among the users using questionnaires. The questionnaire results were converted into numbers and resulted in a mean value of 91.8. Those two values indicate the users' ability to use the system well and obtain the new knowledge displayed in the data analysis dashboard

Ensemble missing data techniques for software effort prediction

Constructing an accurate effort prediction model is a challenge in software engineering. The development and validation of models that are used for prediction tasks require good quality data. Unfortunately, software engineering datasets tend to suffer from the incompleteness which could result to inaccurate decision making and project management and implementation. Recently, the use of machine learning algorithms has proven to be of great practical value in solving a variety of software engineering problems including software prediction, including the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper proposes a method for improving software effort prediction accuracy produced by a decision tree learning algorithm and by generating the ensemble using two imputation methods as elements. Benchmarking results on ten industrial datasets show that the proposed ensemble strategy has the potential to improve prediction accuracy compared to an individual imputation method, especially if multiple imputation is a component of the ensemble