About SQE Lab

Discover our journey in advancing System Quality Engineering through excellence in research, education, and innovation

What is SQE Lab?

"In God We Trust, All Others Must Bring Data"

Ever wondered:

  • How come YouTube recommends videos that interest me?
  • How come Google Maps knows the fastest route for that day?
  • How come the weather forecast is (most of the time) accurate?
  • How come the elected president can be predicted using quick count?

All of these rely on data which are collected, analyzed, and transformed into insights. Data is information (in facts or numbers) collected to support better decision-making. In today's world, data is everywhere and it's becoming essential in every field.

But data alone is not enough. That's where quality comes in.

In the world of industrial engineering, quality isn't just about perfection, it's about consistency, reliability, and meeting customer expectations. Quality saves time, reduces waste, builds trust, and enables continuous improvement. With that, the SQE Lab is where students start learning how to deliver it, powered by data.

To bring these principles to life, the SQE Lab offers a variety of programs and initiatives that give students the opportunity to apply data and quality concepts in meaningful ways.

These include:

Academic Practicums

For courses related to statistics and quality

Training & Consultation

Statistics, Data Analysis, Quality Engineering, and Reliability for internal and external audiences

Engineering Research

Manufacturing, process, and service industry using statistical and data analytic-driven approaches

Non-Academic Activities

Alumni Sharing, Company Visit, Excursion, etc.

Our History

Discover our history in statistic and quality engineering.

Foundation

2008

Established in 2008 based on the Dean of Engineering Faculty Decree Number 1158/D/SK/FTUI/VIII/2008, SQE acts as a prominent laboratory within the Department of Industrial Engineering Universitas Indonesia.

Key Milestones Timeline

2008

Laboratory establishment

2010

First research publications

2015

Industry partnership programs

2020

Digital transformation initiatives

2024

Current research excellence

SQE Laboratory establishment and early days
SQE Laboratory research activities
SQE Laboratory team and collaborations

Vision & Mission

Our guiding principles that drive excellence in research, education, and service

Our Vision

To become Indonesia's leading statistics and quality engineering laboratory by contributing to industry development and society through various excellent learning and research activities.

Our Mission

1.

Conduct learning activities including practicum, internal training, and assistance to support research and external activities.

2.

Involved in grants, projects and external training as a way to contribute to the industry development and society.

3.

Ensure the quality of researches, theses, and grants by continuously being up-to-date with the latest industrial issues related to statistics and quality engineering.

Scope of Research & Focus Areas

Our research spans across multiple domains, utilizing advanced statistical methods and quality engineering principles

Quality & Reliability Engineering

Analysis methods to ensure the goods and services produced meet the customer's standards.

Tools Example:

Statistical Process Control (SPC)

Applied Statistics & Data Mining

Essential frameworks to understand data. Two categories of statistical learning are supervised learning and unsupervised learning.

Business Intelligence

Strategies, techniques, and technologies to transform raw data into actionable data for business decision making.

Research Roadmap 2023-2035

GOAL: Efficiency Improvement Solutions through Data Analytics

Research Roadmap 2023-2035
1

2023-2026

Phase 1

Quality and Efficiency Improvement using Machine Learning Techniques

Methods/Tools:
Quality Engineering Reliability Engineering Multivariate Statistics Optimization
Sector/Industries:
Health Care
Business, Finance, & Insurance
Government & Public Services
Sustainability
2

2027-2031

Phase 2

AI Integration for Efficiency and Optimization

Methods/Tools:
Decision, Risks and Uncertainty NLP Data Mining Ensemble Approach
Sector/Industries:
Infrastructure & Hospitality
Mining & Quarrying
Retail & Consumer Goods
Disaster Management
3

2031-2035

Phase 3

Embrace Efficiency with AI-driven, Personalized, and Automated System Design

Methods/Tools:
Generative AI Computer Vision Deep Learning Spatial Analysis
Sector/Industries:
Manufacturing
Education
Transportation & Logistics
Energy & Utilities