The seminar featured two experienced speakers from FTUI: Dr. Arian Dhini from the Department of Industrial Engineering, FTUI, who delivered the first session titled Data Understanding, and Dr. Mia Rizkinia from the Department of Electrical Engineering, FTUI, who presented the second session on Data Preprocessing for Analytics. The event was moderated by Dr. Intan Clarissa Sophiana from the Department of Chemical Engineering, FTUI, who guided the discussion and Q&A session.
In the first session, Dr. Arian Dhini discussed a deep understanding of data, including techniques for data collection and exploration required in research. In his presentation, Dr. Dhini explored various aspects of data understanding, including Data Acquisition, Data Description, and Data Exploration.
“Data Acquisition involves collecting data from primary sources (such as surveys and laboratory experiments) and secondary sources (like social media and IoT) to ensure data completeness. Data Description is how we present data through descriptive statistics to describe dataset characteristics. Finally, Data Exploration is where we investigate relationships between variables through visualization techniques such as correlation matrices and bivariate analysis,” explained Dr. Arian Dhini.
Dr. Dhini also highlighted data visualization techniques, from simple graphs like bar charts to complex visualizations in dashboards, which are used to identify patterns or trends quickly and intuitively.
In the second session of the seminar, Dr. Eng. Mia Rizkinia introduced another important topic, Data Preprocessing, which is the process of transforming raw data into data that is ready for analysis. Data preprocessing is a crucial process because around 60-80% of a data scientist’s time is spent ensuring good data quality. Dr. Mia explained various data preprocessing techniques, such as cleaning, selecting, and transforming data to improve analysis quality.
“Data Preparation is a crucial stage in data analysis that involves selecting, cleaning, and transforming raw data into high-quality data ready for modeling. Without good data preparation, analytical models are prone to errors and bias, making it difficult to generate accurate and reliable insights. This stage ensures that the data used truly reflects the reality we aim to understand,” said Dr. Eng. Mia Rizkinia.
Prof. Dr. Ir. Heri Hermansyah, S.T., M.Eng., IPU., Dean of FTUI, stated, “This FTUI Seminar Series not only emphasizes the importance of understanding and processing data but also provides essential practical skills in the field of data science. This event is expected to equip participants, especially students and practitioners in the fields of engineering and data science, with the ability to optimize data for research needs and decision-making.”
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