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Published By Indonesian Institute Of Sciences

2301-8593, 0125-9008

2020 ◽  
Vol 41 (2) ◽  
pp. 227
Author(s):  
Risqi Putri Wulandari ◽  
Kiki Fauziah

2020 ◽  
Vol 41 (2) ◽  
pp. 289
Author(s):  
Fidan Safira ◽  
Tamara Adriani Salim ◽  
Rahmi Rahmi ◽  
Mad Khir Johari Abdullah Sani

2020 ◽  
Vol 41 (2) ◽  
pp. 203
Author(s):  
Rika Wulandari ◽  
Emma Rochima ◽  
Yan Rianto ◽  
Cipta Endyana

2020 ◽  
Vol 41 (2) ◽  
pp. 193
Author(s):  
Lia Umaroh ◽  
Machsun Rifauddin

This study aims to explain to use a VPN in the UNISMA Library. The research method used is descriptive-qualitative and data was obtained through interviews with five informants, observation, and documentation. Data analysis techniques by collecting data, data reduction, data presentation and drawing conclusions. While the validity of the data was obtained through triangulation. The results showed that the use of VPN in the UNISMA Library to speed up internet connection and data privacy. UNISMA library uses a proxy server router operating system for VPN networks. To be able to make Mikrotik a VPN server, configuration is required which includes IP pool configuration, IP router configuration, PPP configuration, DHCP server configuration, NAT by pass firewall configuration and IP security configuration. The library selection of VPN products considers the aspects of strong authentication, encryption that is strong enough, meets standards, integration with other field network services.


2020 ◽  
Vol 41 (2) ◽  
pp. 215
Author(s):  
Tupan Tupan ◽  
Kamaludin Kamaludin

The study aims to determine: (1) the number of open access resources for research data management publications indexed by Scopus, including the year of publication, source of publication, authors, institutions, countries, types of documents and funding agencies; (2) mapping research data management based on keywords. The results of the study showed that the number of open access resources for research data management publications has started since 1981 and the number has continued to increase starting in 2014 and the highest number occurred in 2019, namely 49 publications. The most publicized journals that open access to research data management was the Data Science Journal, which was 11 publications. The most productive author of conducting research data management publications was Cox, A.M. and Pinfield, S. The largest institutions contributing to the publication of open access research data management were the University of Toronto and New York University. The countries that contributed the most were the United States with 50 publications, then China with 38 publications. The most open access research data management in the form of articles as many as 107 and 37 conference paper publications. The institutions that provided the most funding sponsors were the Deutsche Forschungsgemeinschaft and the National Science Foundation. The results of keyword mapping with VOSViewer showed that big data, research data management, information management, data management, medical research topics, software, information processing, and metadata were the most researched topics.


2020 ◽  
Vol 41 (2) ◽  
pp. 133
Author(s):  
Ariani Indrawati ◽  
Hendro Subagyo ◽  
Andre Sihombing ◽  
Wagiyah Wagiyah ◽  
Sjaeful Afandi

The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with imbalanced data in the real situation. To handling imbalanced data issues, the most popular technique is resampling the dataset to modify the number of instances in the majority and minority classes into a standard balanced data. Many resampling techniques, oversampling, undersampling, or combined both of them, have been proposed and continue until now. Resampling techniques may increase or decrease the classifier performance. Comparative research on resampling methods in structured data has been widely carried out, but studies that compare resampling methods with unstructured data are very rarely conducted. That raises many questions, one of which is whether this method is applied to unstructured data such as text that has large dimensions and very diverse characters. To understand how different resampling techniques will affect the learning of classifiers for imbalanced data text, we perform an experimental analysis using various resampling methods with several classification algorithms to classify articles at the Indonesian Scientific Journal Database (ISJD). From this experiment, it is known resampling techniques on imbalanced data text generally to improve the classifier performance but they are doesn’t give significant result because data text has very diverse and large dimensions.


2020 ◽  
Vol 41 (2) ◽  
pp. 169
Author(s):  
Hermin Triasih ◽  
Rahmi Rahmi ◽  
Katrin Setio Devi

This study aims to analyse the implementation of RDM at PDDI-LIPI and to assess its staff’s understanding about RDM services. This article also discusses the challenges and obstacles PDDI faces in providing RDM services. The data was collected via an online survey from 28 July to 7 August 2020. The survey consisted of 35 questions and was shared with 36 respondents via social media. The results identified categories such as research data management services, data management planning services, data archiving services, funding, and staff competency and training needs. In addition, this article also discusses the approach and assessment of RDM services, challenges in providing RDM services, and plans for further developing RDM services at PDDI-LIPI. The results showed that the PDDI staff's understanding of RDM services is adequate. As a new service, the implementation of RDM at PDDI-LIPI continues to develop toward optimisation. RIN is a platform used by PDDI to support this goal. The three biggest obstacles faced by PDDI-LIPI in developing RDM services are limited human resources, competence and budget.  Various trainings related to RDM, both sending staff off campus and inviting trainers to campus, were carried out by PDDI to overcome these obstacles. It is recommended to conduct further research on the mapping and upskilling of staff in charge of RDM services.


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