Measuring Social Vulnerability using Factor and Cluster Analysis

By forming a social vulnerability analysis you will have more information regarding which areas are more vulnerable, so that risk management planning with development programs can be more appropriate and optimal.

  • Schedule

    29 November – 2 December 2022

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 1.500.000

Course Summary

DKI Jakarta Province is the province with the highest number of positive COVID-19 cases in Indonesia. Although it is recognized that the COVID-19 virus can infect anyone, some groups of people have a higher level of risk for exposure. The level of social vulnerability of the community has been widely carried out through the social vulnerability index. Regional social vulnerability describes the social fragility of an area due to the influence of a hazard. Social vulnerability factors are crucial in the midst of the COVID-19 pandemic. This is because the combination of these social vulnerability factors can increase the risk of getting COVID-19.

This 3-day online workshop is a beginner-friendly introduction to Factor and Cluster analysis to find out social vulnerabilities due to Covid-19. By forming a social vulnerability analysis per sub-district in DKI Jakarta, you will have more information regarding which areas are more vulnerable and also information about the more dominant factors in every sub-district. So that risk management planning with development programs can be more appropriate and optimal.

Throughout the online course, we will provide participants with a rich interactive experience. One Instructor and two Teaching Assistants will help participants to troubleshoot or help with any difficulties encountered by participants.

NOTE: This workshop will be delivered in Bahasa Indonesia.


Upon completion of this workshop, you will be able to:

  • Work with R and tidyverse for data cleansing and manipulation processes.
  • Understand the Factor Analysis and Factors Formation of Social Vulnerability in DKI Jakarta Province
  • Visualize the geographical map using choropleth in R
  • Understand the FCM clustering method to find out the variables that affect each cluster


  • Introduction to R for data science
  • Working with RStudio Environment
  • Basic Control Statement in R
  • Data manipulation and processing with R dplyr
  • Introduction to Unsupervised learning (PCA and Factor Analysis)
  • Factor analysis assumptions
    • Multicollinearity test
    • Kaiser–Meyer–Olkin (KMO) – Measure of Sampling Adequacy (MSA)
    • Find the optimal number of factor using  
  • Eigenvalue or SS Loadings
  • Index aggregation using the value from proportion explained
  • Min-Max normalization
  • Elbow Method (Choose the optimum value of k)
  • Soft and hard clustering
  • Kruskall-Wallis test
  • Exploratory data analysis
  • Factor analysis in R using method `fa()` from library `psych`
  • Find the optimal number of factor using  `fa.parallel()`
  • Perform extraction by generating latent variables using `fa.diagram()`


This testimonial video is taken after our previous Online Data Science Series: Time Series Analysis for Business Forecasting.


Our learning format is online-interactive, you will feel the interactive experience as if you were present in a physical classroom. You can access the class using your Zoom account on pre-defined dates.


    Zoom recording, course Books (PDF & HTML files), the dataset for practice, reference notes, and working files are accessible through our Learning Management System account.


    Show current and prospective employers of your mastery in computer vision with a signed certificate of completion.


    Be a part of our data-passionate community with 5000+ members and 1000+ alumni.


Workshops in this series are tailored to casual programmers and non-programmers that are taking their first steps into data science. It assumes no prior knowledge or academic background, and attendees will be introduced to the beautiful art of writing R / Python code to produce data visualization and build machine learning models. The workshop has a gentle learning slope that is designed with non-technical professionals and academics in mind.

Yes, you can still attend the workshop as it is a beginner-friendly workshop.

Our system will send you an email containing a link and details to join a Google Classroom.

Online learning will be conducted via, Link to join the Zoom Class will be announced via Google Classroom.

Learning materials can be obtain via Google Classroom

Yes, you will receive a certificate of completion.


Rany Dwi Cahyaningtyas

Rany Dwi Cahyaningtyas is a Data Science Instructor at Algoritma Data Science School with expertise in Statistics and Machine Learning implementation. Rany has been involved in numerous mentoring, data science projects, and consultative data science training for Bank Indonesia and Direktorat Jenderal Pajak (DJP).