Setia Budi completed his academic exercise in Computer Science at the University of Tasmania, Australia.  Australia Awards Scholarships and Sense-T Elite Scholarships enabled him to get his Master and PhD qualifications. His primary research interests include optimisation problem, environmental monitoring, data science, educational data mining, and computer vision.

 

Publications

2018

Low-Cost Electronic Tagging System for Bee Monitoring (Journal Article)
Sensors (MDPI)

2018

Addressing RFID Misreadings to Better Infer Bee Hive Activity (Journal Article)
IEEE Access (IEEE)

2018

Mobile Platform Sampling for Designing Environmental Sensor Networks (Journal Article)
Environmental Monitoring and Assessment (Springer)

2017

In Search for a Robust Design of Environmental Sensor Networks (Journal Article)
Environmental Technology (Taylor and Francis)

2016

Design of Environmental Sensor Networks Using Evolutionary Algorithms (Journal Article)
Geoscience and Remote Sensing Letters (IEEE)

2015

Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration (Journal Article)
Sensors (MDPI)

2018

FACE - Face At Classroom Environment: Dataset and Exploration (Conference Paper)
Image Processing Theory and Applications (IPTA - China)

To be presented in November 2018

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2013

Multi-objective Optimization Model in Mobile Data Communication Scheduling (Conference Paper)
The Eighth International Conference on Information Technology in Asia (CITA - Malaysia)

2013

Scheduling Data Communication Based Services on the Personal Mobile Devices (Conference Paper)
15th International Conference on Enterprise Information Systems (ICEIS - France)
Teachings
Statistics
First year undergraduate
Algorithms
First year undergraduate

Prescribed textbook: Grokking Algorithm

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Data Structure
Second year undergraduate

Prescribed textbook: Data Structures and Algorithms in Python

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Awards

2013

Sense-T Elite Scholarships
PhD Scholarship

2011

Australia Awards Scholarships
Master Scholarship
[Australia Awards] Formerly known as Australian Development Scholarships (ADS), a full scholarship funded by Australian Government

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Affiliations

2005-present

Maranatha Christian University
Lecturer

2013-2018

University of Tasmania
PhD candidate; Academic tutor

2013-2017

Commonwealth Scientific and Industrial Research Organisation
Research assistant
Professional Memberships

2018

IEEE Member
Victorian Section

IEEE Member ID: 94845913

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Courses

2018

Introduction to Probability and Data
Coursera [Duke University]

This course introduces student to sampling and exploring data, as well as basic probability theory and Bayes’ rule. Student will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. Student will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

on going course

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2018

Python Data Structures
Coursera [University of Michigan]

This course introduces the core data structures of the Python programming language. The course will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.

[certificate]
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2018

Programming for Everybody (Getting Started with Python)
Coursera [University of Michigan]

This course aims to teach everyone the basics of programming computers using Python. The course covers the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.

[certificate]
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2017

Data Science in Real Life
Coursera [Johns Hopkins University]

This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.

After completing this course student will know how to:

  1. Describe the “perfect” data science experience
  2. Identify strengths and weaknesses in experimental designs
  3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls.
  4. Challenge statistical modeling assumptions and drive feedback to data analysts
  5. Describe common pitfalls in communicating data analyses
  6. Get a glimpse into a day in the life of a data analysis manager.
[certificate]
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2017

Managing Data Analysis
Coursera [Johns Hopkins University]

This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.

After completing this course student will know how to:

  1. Describe the basic data analysis iteration
  2. Identify different types of questions and translate them to specific datasets
  3. Describe different types of data pulls
  4. Explore datasets to determine if data are appropriate for a given question
  5. Direct model building efforts in common data analyses
  6. Interpret the results from common data analyses
  7. Integrate statistical findings to form coherent data analysis presentations
[certificate]
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2017

Building a Data Science Team
Coursera [Johns Hopkins University]

This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.

After completing this course student will know how to:

  1. The different roles in the data science team including data scientist and data engineer
  2. How the data science team relates to other teams in an organization
  3. What are the expected qualifications of different data science team members
  4. Relevant questions for interviewing data scientists
  5. How to manage the onboarding process for the team
  6. How to guide data science teams to success
  7. How to encourage and empower data science teams
[certificate]
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2017

A Crash Course in Data Science
Coursera [Johns Hopkins University]

This is a focused course designed to rapidly get you up to speed on doing data science in real life. The goal was to make this as convenient as possible for student without sacrificing any essential content.

After completing this course student will know how to:

  1. How to describe the role data science plays in various contexts
  2. How statistics, machine learning, and software engineering play a role in data science
  3. How to describe the structure of a data science project
  4. Know the key terms and tools used by data scientists
  5. How to identify a successful and an unsuccessful data science project
  6. The role of a data science manager
[certificate]
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Multi-objective Optimisation, Environmental Monitoring, Environmental Sensor Networks, Data Science