Article Per Year (5 Year)
p-Index From 2018 - 2023
1.853
P-Index
This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Adi Widya : Jurnal Pengabdian Masyarakat JMM (Jurnal Masyarakat Mandiri) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Community Empowerment Prima Abdika: Jurnal Pengabdian Masyarakat
Arista, Artika
Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Arts Humanities Computer Science & IT Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Sinkron : Jurnal dan Penelitian Teknik Informatika

 1 
Comparison Decision Tree and Logistic Regression Machine Learning Classification Algorithms to determine Covid-19 Arista, Artika
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2022): Article Research Volume 7 Issue 1: January 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i1.11243

Abstract

Many people today are unsure whether they have COVID-19. The frequent fever, dry cough, and sore throat are all signs and symptoms of COVID-19. If a person has signs or symptoms of coronavirus disease 2019 (COVID-19), he/she should see the doctor or go to a clinic as soon as possible. As a result, it's vital to learn and comprehend the fundamental differences. COVID-19 can cause a wide range of symptoms. The experiments were carried out using two Machine Learning Classification Algorithms, namely Decision Tree (DT) and Logistic Regression (LR). Both algorithms were written and analyzed using the Python program in Jupyter Notebook 6.4.5. From the results obtained in the experiments of covid symptoms dataset, on average, the DT model has obtained the best cross-validation average and the testing performance average compared to the LR machine learning models. For cross-validation results, the DT model has achieved an accuracy of 98.0%. For performance testing, the DT model has achieved an accuracy of 98.0%. The LR has obtained the second-best result on the average of cross-validation performance and the testing results. For cross-validation results, the LR model has achieved an accuracy of 96.0%. For performance testing, the LR model has achieved an accuracy of 97.0%. Consequently, the DT for the COVID-19 symptoms dataset is outperforming the LR for cross-validation and testing results.
Information System for State-owned inventories Management at the Faculty of Computer Science Tjahjanto Tjahjanto; Artika Arista; Ermatita Ermatita
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2022): Article Research: Volume 7 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11678

Abstract

Information technology development brings a very broad impact on human life. With this Model View Controller (MVC) concept, information system development could be developed in accordance needs of the user’s information system development and be able to adapt the development in the organization. Needs of fast and precise and adaptive information system information is needed in this modern era when everything needs to be fast. Faculty of computer science (FIK) Universitas Pembangunan Nasional Veterans Jakarta (UPNVJ) as one work unit under the government agency, has State-owned inventories to support all activities to carry out the Tri Dharma or the higher education pillars, especially in the field of education. The state-owned inventories or devices are available in FIK such as monitors, printers, LCD, mouse, etc. For continuity of this educational process, the state-owned inventories must be maintained. Therefore, in this research, the researchers will develop an information system to document the state-owned inventory records for helping the management of the state-owned inventories in the faculty of computer science at UPN Veteran Jakarta. The information system was built using the waterfall method and using the PHP programming language and MySQL database running on a server. This information system provides features to manage users, manage components, access system logs, access system settings, manage devices, manage the location, and access reports. The system that has been developed has also passed the eligibility assessment for all aspects of both visual communication aspects, software engineering aspects, and usability aspects.
Co-Authors Anita Muliawati Bahtiar Saleh Abbas Ermatita Ermatita Ika Nurlaili Isnainiyah Ika Nurlaili Isnainiyah Khairun Nisa Meiah Ngafidin Kraugusteeliana Kraugusteeliana Muhammad Eka Purbaya Muhammad Eka Purbaya Noor Falih Rona Nisa Sofia Amriza Rudhy Ho Purabaya Rudhy Ho Purabaya Sarika Sarika Tjahjanto Tjahjanto Tri Rahayu Zaidiah, Ati