Connecting Technology with Sustainable Development: AI and STEM for a Better Future

Bintaro, South Tangerang, 10/06/2024 - Technological advancements continue to have a significant impact on many aspects of our lives, from how we work to how we protect the environment. In a public lecture titled "Decoding The Future: Opportunities and Challenges of Artificial Intelligence" held at Universitas Pembangunan Jaya on Tuesday (04/06/2024), the speakers provided deep insights into how technology, especially artificial intelligence (AI) and STEM (Science, Technology, Engineering, and Mathematics), can be used to support Sustainable Development Goals (SDGs) and increase female representation in STEM fields. Much can be learned from the essence of the two presentations delivered by Dr. Zati Hakim Azizul Hasan from Universiti Malaya and Dr. Devi Fitrianah, S. Kom., M. T. I. from Binus University.

In his presentation, Dr. Zati highlighted innovative projects that use AI and IoT to support SDG 6 (Clean Water and Sanitation). One notable project is an autonomous lake-cleaning robot developed by Aqiff Mursyideen bin Shamsul Safuan. Equipped with computer vision and deep learning algorithms, this robot classifies and collects waste from the lake, operating wirelessly through a tablet for efficient cleaning.

Another project involves IoT sensors designed by Palok Biswas to predict water quality. These sensors use machine learning to detect eutrophication caused by algal blooms, enabling real-time water monitoring and reducing the need for expensive manual sampling.

Dr. Zati also discussed the application of Industry 4.0 technology in enhancing industrial processes (SDG 9: Industry, Innovation, and Infrastructure) and improving health and well-being (SDG 3: Good Health and Well-being). "AI-based smart manufacturing systems optimize production processes, while robot swarms assist in complex medical operations and emergency responses," he said.

In another session with Dr. Devi Fitrianah, she delved into the gender gap in STEM and how machine learning frameworks can help improve women's employability. Despite progress, women remain underrepresented in STEM, especially in higher academic and professional roles.

Dr. Devi developed a machine learning framework that combines clustering and classification to predict women's employability in STEM. The clustering model uses Robust Trimmed K-Means (RTKM) to handle outliers and accurately group alumni job data. The classification model uses Multi-target Random Forest to predict job waiting times and job suitability with academic backgrounds, using metrics like accuracy, precision, recall, F1-score, and Hamming loss for evaluation.

"This framework provides valuable insights into the factors that affect women's employability in STEM, demonstrating the potential of machine learning to increase gender representation in these fields," she said.

Technology has great potential to support sustainable development and address the gender gap in STEM. The projects presented by Dr. Zati Hakim Azizul Hasan and Dr. Devi Fitrianah show how AI and IoT can achieve these goals. From autonomous lake-cleaning robots to machine learning frameworks predicting women's employability, these innovations not only support the SDGs but also pave the way for a more inclusive and sustainable future.

By continuously advancing technology and ensuring broader access for everyone, we can achieve greater progress in various aspects of life, creating a better world for future generations.


By: Joshua Nathanael Zega (2022071065) Informatics Student