About the Journal
| Journal title | Jurnal Aplikasi Sains Data |
| Initials | JASID |
| Abbreviation | J.Apk.Impl.Sada |
| Frequency | 2 issues per year (April & October) |
| DOI | Prefix 10.33005 by Crossref |
| e-ISSN | 3108-947X |
| p-ISSN | - |
| Editor-in-chief | Amri Muhaimin, S.Stat, M.Stat, M.S. |
| Publisher | Universitas Pembangunan Nasional "Veteran" Jawa Timur |
Jurnal Aplikasi Sains Data (JASID) is a peer-reviewed scientific journal published by the Data Science Study Program at Universitas Pembangunan Nasional "Veteran" East Java (UPN "Veteran" Jatim). Serving as a dedicated platform, JASID facilitates the dissemination of knowledge and practical experiences among researchers, practitioners, and academics in the field of data science applications.
The journal covers a broad spectrum of topics within data science, including but not limited to machine learning, data mining, data analysis, data visualization, and natural language processing. It also emphasizes real-world applications of data science across diverse sectors such as business, finance, healthcare, and education.
Through a rigorous peer review process, JASID upholds high standards of quality and originality in its published works. The journal aims to be a valuable resource for data science professionals and enthusiasts in Indonesia, fostering interdisciplinary collaboration and enhancing public understanding of the transformative potential of data science applications.
Current Issue
Vol. 1 No. 1 (2025): Journal of Data Science Applications.
This issue of Jurnal Aplikasi Sains Data (JASID) features research on diverse applications of data science methodologies. The first article compares ARIMA and SARIMA models for forecasting non-oil and gas exports in East Java. Next, a content-based filtering approach is applied to a tourist destination recommendation system in Central Java. Another study evaluates the performance of stochastic gradient descent and adaptive moment estimation optimizers in audio-based emotion classification using convolutional neural networks. Additionally, a fuzzy inference system has been developed for the quality assessment of formula milk in a stunting prevention program. Lastly, the clustering of diabetes risk factors is performed using the K-Prototypes Clustermix algorithm. Collectively, these studies demonstrate the relevance of data science techniques in economic forecasting, healthcare, machine learning, and recommendation systems.
Published:
2025-05-28
