About the Journal

ONLINE ISSN : 2615-1367

PRINT ISSN : 2086-4132

The Journal of Statistical Applications and Computational Statistics (Jurnal Aplikasi Statistik dan Komputasi Statistik/JASKS) is an official publication of Pusat Penelitian dan Pengabdian kepada Masyarakat (PPPM) Politeknik Statistika STIS. JASKS is dedicated to publishing original research in applied and computational statistics. This journal was first published in 2009. The publication schedule is two times a year, in June and December. 

The journal consists of two refereed sections, Applied Statistics and Computational Statistics, that are divided into the following subject areas that are related to statistics applications and their computation:

  • Official statistics – Manuscripts dealing with survey design, questionnaire design and evaluation, measurement error, estimation and inference using frequentist or Bayesian, data collection, analytical uses of data, imputation, quality aspects of official statistics production, total survey error, systems and architectures for statistics production, evaluation and identification of statistical needs, small area estimation, and other subject related to official statistics.
  • Statistical Methodology – Manuscripts dealing with new and innovative data analysis techniques and methodologies include, but are not limited to: bootstrapping, classification techniques, design of experiments, parametric and nonparametric methods, statistical genetics, outlier detection, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, and robust procedures.
  • Applied Statistics in Economics, Social and Population Studies – Manuscript dealing with econometrics, demography, spatial analysis, time series analysis, longitudinal analysis, multilevel analysis, spatio-temporal analysis, and other subjects related to Applied Statistics in Economics, Social, and Population Studies.
  • Data Science – Manuscript dealing with big data, data mining, data science, data engineering, data visualization, machine learning, and data exploration.
  • Computational Statistics – Manuscripts dealing with the use of computing in statistical methodology (e.g., statistical databases, statistical information systems, Bayesian computation, computer-intensive inferential methods, numerical and optimization methods, parallel computing), and the development, evaluation, and validation of statistical software and algorithms.

Jurnal Aplikasi Statistika dan Komputasi Statistik:

2016, Based on the LIPI No.747 / Akred / P2MI-LIPI / 04/2016, ASKS Journal was accredited by LIPI.

2018, JASKS was accredited Sinta 2 by Kementerian Riset dan Teknologi/ Badan Riset dan Inovasi Nasional. (Link SK)

2023, JASKS will process re-accreditation Sinta

2024, JASKS was accredited Sinta 4 by Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Ministry of Education, Culture, Research and Technology). (Link SK)

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Current Issue

Vol. 16 No. 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
					View Vol. 16 No. 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik

This issue consists of 7 articles contributed by 19 authors affiliated with 4 institutions from Indonesia, including Universitas Airlangga (Faculty of Science and Technology, Department of Mathematics, Statistics Program), Universitas Syiah Kuala (STEM Research Center, Faculty of Mathematics and Natural Sciences), Universitas Mataram (Department of Mathematics and Natural Sciences), and Universitas Pamulang. The research presented covers a diverse range of topics, including stunting prevalence modeling using multi-predictor truncated spline nonparametric regression by authors from Universitas Airlangga; the impact of digital literacy on poverty as mediated by education, demography, and employment by researchers from Universitas Syiah Kuala; maternal mortality rate analysis in Papua using geographically weighted Poisson regression by Universitas Airlangga authors; human development index modeling in East Java using geographically weighted logistic regression by Universitas Airlangga; regional economic modeling using Geographically Weighted Lasso by Universitas Mataram; prediction interval performance in Random Forest for poverty data analysis by authors from Universitas Pamulang and IPB University; and export-import forecasting using multi-output back-propagation deep neural networks by authors from Institut Teknologi Sepuluh Nopember and BPS-Statistics Indonesia.

Published: 2024-06-30

Full Issue

Statistika Kependudukan

Statistika Ekonomi

Komputasi Statistik

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