Statistics Disaster Managemen Laboratory

The Statistics Disaster Management Laboratory (SDM) is the home for statistics and the academic community, especially in the field of disaster. This scientific study covers Theory of Spatial Data, Geographic Information Systems, Remote Sensing, and Application of Spatial Statistics for Disaster Data (natural, non-natural, and social disasters) and non-disaster data, Risk Analysis, and Actuarial Sciences. The SDM laboratory provides support for SSP-UII (Statistical Study Program, UII) academic catur dharma activities.

Achmad Fauzan, S.Pd., M.Si

Carry out practicum learning courses, conduct the compulsory/elective courses (which need lab activities), facilitate research, and support community service in the fields of statistics in disaster management, spatial data, and actuarial sciences.

The facilities are the laboratory room and its equipment: the comfortable discussion room, some projectors, sound system, fingerprint, etc. The software for analyzing data includes open-source (R, Rstudio, python, etc.) and licensed (SPSS, Minitab, Tableau, etc.). The modules of practicum in SDM are provided in the printed or digital e-book (limited to some modules which have e-ISBN/ISSN).

Systems for analyzing data and modules. The following table shows some samples of research in Statistics Disaster Management by SDM Lab.

NUM TITLE AUTHOR YEAR PUBLICATION’S URL
1 Developing deep learning architecture for image classification using convolutional neural network (CNN) algorithm in forest and field images Meiga Isyatan Mardiyah, Tuti Purwaningsih 2020 Click
2 Application of Clustering Algorithm and Spatial Analysis for Industrial Optimization Achmad Fauzan, Ginanjar Wiro Sasmito, Sekti Kartika Dini 2020 Click
3 Modeling the Number of Tuberculosis (TB) Cases in Indonesia using Negative Binomial Regression Rahmadi Yotenka 2020 Click
4 Application of Spatial Regression Model for Modeling Measles Case in Indonesia Tuti Purwaningsih 2018 Click
5 The Logistic Regression Analysis with Nonparametric Approach based on Local Scoring Algorithm (Case Study: Diabetes Mellitus Type II Cases in Surabaya of Indonesia) Marisa Rifada, Suliyanto, Eko Tjahjono, and Ayundyah Kesumawati 2018 Click

Gallery

Below are some photos of the Statistics Disaster Management Laboratory (SDM).