Inferensi is an open-access and peer-reviewed journal publishing advanced results in the fields of statistics and data science, as mentioned explicitly in the scope of the journal. The journal is geared toward the dissemination of original research and practical contributions by both scientists and engineers, from both academia and industry.
Inferensi is published by the Department of Statistics ITS in cooperation with Forum Pendidikan Tinggi Statistika (FORSTAT). This journal publishes three issues a year in March, July, and November. Inferensi has been accredited by the Ministry of Education Culture, Research, and Technology, Republic of Indonesia based on Decree 152/E/KPT/2023 as Sinta 2 started from Vol.6 No. 1 (2023).
Department of Statistics
Faculty of Science and Data Analytics
Institut Teknologi Sepuluh Nopember (ITS)
Kampus ITS Keputih Sukolilo Surabaya Indonesia 60111
The aim of Inferensi is to publish original articles concerning statistical theories and novel applications in diverse research fields related to statistics and data science. The objective of papers should be to contribute to the understanding of the statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims; and any approach in data science. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where the original methodology is involved and original contributions to the foundations of statistical science.
It also sometimes publishes review and expository articles on specific topics, which are expected to bring valuable information for researchers interested in the fields selected. The journal contributes to broadening the coverage of statistics and data analysis in publishing articles based on innovative ideas. The journal is also unique in combining traditional statistical science and relatively new data science. All articles are refereed by experts.
