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Register: Jurnal Ilmiah Teknologi Sistem Informasi
Published by Universitas Pesantren Tinggi Darul ulum
ISSN : 25030477     EISSN : 25023357     DOI : https://doi.org/10.26594/register
Core Subject : Science,
Computer Science & IT
Register: Scientific Journals of Information System Technology is an international, peer-reviewed journal that publishes the latest research results in Information and Communication Technology (ICT). The journal covers a wide range of topics, including Enterprise Systems, Information Systems Management, Data Acquisition and Information Dissemination, Data Engineering and Business Intelligence, and IT Infrastructure and Security. The journal has been indexed on Scopus (reputated international indexed) and accredited with grade “SINTA 1” by the Director Decree (1438/E5/DT.05.00/2024) as a recognition of its excellent quality in management and publication for international indexed journal.
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Articles 2 Documents
 1 
Search results for , issue "Vol 6, No 1 (2020): January-June (Articles In progress 4/7)" : 2 Documents clear
INTEGRATION OF EUCS VARIABLES INTO DELONE AND MCLEAN MODELS FOR E-GOVERNMENT EVALUATION: CONCEPTUAL MODELS Sorongan, Erick; Hidayati, Qory
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 6, No 1 (2020): January-June (Articles In progress 4/7)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v6i1.1608

Abstract

This research was based on the modification of the DeLone and McLean information systems models by adding end-user computing satisfaction variables to determine the success factors for e-government systems. This model was adopted due to the aim of this study to investigate the factors responsible for the successful implementation of e-government by bringing it closer to public value. However, while the DeLone and McLean models focus more on the information system approach, the model proposed was on the premise that system quality (SQ), information quality (IQ), content (CO) and format (FO) are determinants of e-government system user satisfaction. Furthermore, the net benefits through a five-dimensional public value determinants were used to evaluate e-government websites from a community perspective. Responses from 150 communities were analyzed by smart PLS 3.0 using structural equation models to examine the relationship between the constructs of the proposed model. This study contributes to the research gap in adopting DeLone and McLean's model in the e-government due to the limitation in its validation for different contexts. The results support the effect of content variables on user satisfaction and simultaneously prove that it is possible to explain net benefits, with an r-squared value of 69.1%, using the variables in the proposed model. The five dimensions of public value adopted all proved to have a positive influence with a confidence level of 95%. The level of construct significance identified is able to help in the formulation of strategies to improve e-government services.
COMMUNITY DETECTION IN TWITTER BASED ON TWEETS SIMILARITIES IN INDONESIAN USING COSINE SIMILARITY AND LOUVAIN ALGORITHMS Irsyad, Akhmad; Rakhmawati, Nur Aini
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 6, No 1 (2020): January-June (Articles In progress 4/7)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v6i1.1595

Abstract

Twitter is now considered as one of the fastest and most popular communication media and is often used to track current events or news. Many tweets tend to contain semantically identical information. When following an activity or news, sometimes in tweeting people do it in groups. Therefore, it is necessary to have a useful technique for grouping users based on the tweets similarities. In this study, cosine similarity method is used to examine the similarity of tweets between accounts, and a graph-based approach is proposed to detect communities. Graphs are first depicted from similarities between tweets and next community detection techniques are applied in graphs to group accounts that have similar tweets. The reason for using these two methods is that compared to other methods, the accuracy of cosine similarity is higher while Louvain can result a better modularity. From this research, it was concluded that cosine similarity and Louvain algorithm could be used in community detection on social media.

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