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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 14 Documents
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Search results for , issue "Vol 6, No 1 (2020): January" : 14 Documents clear
Combination of fast hybrid classification and k value optimization in k-nn for video face recognition Septiana, Nuning; Suciati, Nanik
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 6, No 1 (2020): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v6i1.1668

Abstract

Nowadays, the need for face recognition is no longer include images only but also videos. However, there are some challenges associated with the addition of this new technique such as how to determine the right pre-processing, feature extraction, and classification methods to obtain excellent performance. Although nowadays the k-Nearest Neighbor (k-NN) is widely used, high computational costs due to numerous features of the dataset and large amount of training data makes adequate processing difficult. Several studies have been conducted to improve the performance of k-NN using the FHC (Fast Hybrid Classification) method by optimizing the local k values. One of the disadvantages of the FHC Method is that the k value used is still in the default form. Therefore, this research proposes the use of k-NN value optimization methods in FHC, thereby, increasing its accuracy. The Fast Hybrid Classification which combines the k-means clustering with k-NN, groups the training data into several prototypes called TLDS (Two Level Data Structure). Furthermore, two classification levels are applied to label test data, with the first used to determine the n number of prototypes with the same class in the test data. The second classification using the optimized k value in the k-NN method, is employed to sharpen the accuracy, when the same number of prototypes does not reach n. The evaluation results show that this method provides 86% accuracy and time performance of 3.3 seconds.
Butterfly identification using gray level co-occurrence matrix (glcm) extraction feature and k-nearest neighbor (knn) classification Andrian, Rico; Maharani, Devi; Muhammad, Meizano Ardhi; Junaidi, Akmal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 6, No 1 (2020): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v6i1.1602

Abstract

Gita Persada Butterfly Park is the only breeding of engineered in situ butterflies in Indonesia. It is located in Lampung and has approximately 211 species of breeding butterflies. Each species of Butterflies has a different texture on its wings. The Limited ability of the human eye to distinguishing typical textures on butterfly species is the reason for conducting a research on butterfly identification based on pattern recognition. The dataset consists of 600 images of butterfly’s upper wing from six species: Centhosia penthesilea, Papilio memnon, Papilio nephelus, Pachliopta aristolochiae, Papilio peranthus and Troides helena. The pre-processing stage is conducted using scaling, segmentation and grayscale methods. The GLCM method is used to recognize the characteristics of butterfly images using pixel distance  and Angular direction 0o, 45o, 90o and 135o. The features used is angular second moment, contrast, homogeneity and correlation. KNN classification method in this study uses k values1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 based on the Rule of Thumb. The result of this study indicate that Centhosia penthesilea and Papilio nephelus classes can be classified properly compared to the other 4 classes and require a classification time of 2 seconds at each angular orientation. The highest accuracy is 91.1% with a value of  in the angle of 90o and error rate8.9%. Classification error occured because the value of the test data features is more dominant with the value of the training image features in different classes than the supposed class.  Another reason is because of imperfect test data.
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
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.
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
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.

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