Open Government Data Initiatives: Open by Default or Publishing with Purpose

Over the last decade, after a set of Open Government Data (OGD) principles were developed, governments around the world started to radically change their culture on data governance. However, at the implementation stage of OGD initiatives governments needed to consider whether publishing the massive quantities of open datasets did meet public needs for use and re-use, in view of the enormous investment and resources put into the production of publishable OGD. This research-in-progress adopts an exploratory case study approach combining it with a narrative literature review to investigate how the “Open by default” principle and the “Publishing with purpose” strategy were involved in facilitating OGD usage and public participation. The study’s goal is to overview the current implementation of OGD initiatives and to explore best practices when working with open data. We expect to present a new logic model or to show the modification of existing government organisational logic models by analysing the findings on the nature of the New Zealand government effort in opening data up relates with the possible advantage experienced by the government and the public at large

Value-creating Roles Played by the Actors in Open Government Data: A Systematic Literature Review

Government-held data are immensely valuable; as well as providing the information required to carry out tasks related to internal governance procedures, additional value may be generated by publishing and making accessible Open Government Data (OGD). The paper aims to identify the underlying factors that influence or are influenced by value creation, from the perspective of the OGD actors engaged in value-creating roles. A systematic literature review was conducted in order to study OGD value creation from the perspective of OGD actors engaged in value-creating roles. The findings of the metasynthesis approach indicate that the value-creating roles of the actors in the OGD ecosystem initiate the value co-creation necessary to turning data value into social and economic benefits for stakeholders, as the outcome of all actors’ efforts and government commitment to OGD initiatives

Speech to text for Indonesian homophone phrase with Mel Frequency Cepstral Coefficient

In this study, speech to text system for homophone phrases in Indonesian was designed using an extraction method which featured Mel Frequency Cepstral Coefficient (MFCC). Feature extraction results were classified by comparing the two classifiers of Backpropagation Neural Network (BPNN) and KNearest (KNN). The input data used were the recordings of each of 3 male and female respondents. The recording process was conducted for 5 seconds at a sampling frequency of 16 kHz and at channel mono. Classification results with test data to BPNN showed accuracy rates of 96.67% and 90% respectively for male and female respondents. Moreover, the level of accuracy obtained on KNN amounted to 83.33% for males and 73.33% for females