Jurnal Aplikasi Statistik & Komputasi Statistik (JASKS) is a peer-reviewed open-access international scientific journal dedicated to the latest outstanding developments in the field of official statistics, statistical methodology, applied statistics, and data science, as well as computational statistics. The journal is dedicated to promoting the advancement of statistics and computational statistics by providing a platform for researchers, scientists, and academics to publish their research findings and share their knowledge with the broader scientific community. JASKS welcomes submissions from researchers, scientists, and academics from all over the world on topics including:

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.
Statistical Computing: 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.