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Digital Certificate Authority with Blockchain Cybersecurity in Education Giandari Maulani; Gunawan Gunawan; Leli Leli; Efa Ayu Nabila; Windy Yestina Sari
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 1 No. 1 (2021): April
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v1i1.40

Abstract

In the 21st century the development of internet technology has experienced explosive developments with complex threats in Higher Education. Cyber ​​security is a priority issue for all countries by using data and communication technology in various aspects of life. In order to advance and improve the digital economy in the implementation of a comfortable and reliable electronic system with methods of increasing competitiveness, cyber innovation also builds understanding and sensitivity to national security and resilience. Cybersecurity is constantly changing and learning providers often do not have the authority or facilities and capacity in various activities that connect students, in ensuring their knowledge and expertise. In carrying out the provision and validation of qualifications that are tried exclusively at the centralized management of learning institutions or employing institutions, these days have more ownership of learning experiences with results without risking safety, security and accessibility. This validation provision is not prolonged because learning is far more international than before and education continues to frequently use online platforms. Education providers offer degree internships that represent a new method of expertise and provide a route of advancement in increasing employability prospects. Blockchain is a digital and decentralized ledger, encompassing a set of interlocking technologies. Blockchain is designed to transform the centralized as well as validation model to a decentralized ledger from a secure database. These databases are shared, replicated, and synchronized for validation at universities, legal or regulatory agencies and industrial bodies across the internet.
Use of Data Warehouse and Data Mining for Academic Data : A Case Study at a National University Primasatria Edastama; Amitkumar Dudhat; Giandari Maulani
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 1 No. 2 (2021): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v1i2.55

Abstract

If an organisation has full, rapid, exact, and accurate information, it may perform better in terms of evaluation, planning, and decision-making. The essential information can be derived from operational data maintained in an integrated database by a university that already has an information system. This research looks at how to collect operational data into a data warehouse and then apply data mining techniques to analyse the data. This study yielded a comprehensive data warehouse with a web-based information reporting application. Furthermore, data mining techniques are used to analyse the data warehouse that has been created. The result of the application of data mining is the generation of characteristic patterns of students who take certain specialization programs.
The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia Martono, Aris; Henderi, Henderi; Maulani, Giandari
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1234.240-249

Abstract

This study aims to measure the prediction of survival of covid-19 patients with the best algorithm based on RMSE(Root Mean Square Error). The Covid-19 pandemic has lasted from December 2019 until now and is full of uncertainty about when this pandemic will end, so this research was carried out. In this study, the knowledge discovery database method was used by extracting data sets from Covid-19 patients from March 2020 to March 2021 for each province in Indonesia (Dataset from Kawal Covid-19 SintaRistekbrin) to predict survival during this pandemic as measured by the best algorithms include k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), and/or Deep Learning. The measurement results using cross-validation and the optimal number of folds is 3 in the form of RSME, showing that the k-NN algorithm is an algorithm with RSME 0.101 +/-0.23 where the error rate is the lowest compared to the two algorithms above. Therefore, the k-NN algorithm was chosen as the algorithm for the predictive measurement of surviving Covid-19 patients.