By using big data, three student teams from IPB University won the National Data Summit Competition, held by the Digital Business Ecosystem Research Center, Telkom University, on Thursday (7/11).
The first team, consisting of Willyam, Faldi Sulistiawan, and Audhi Aprilliant, won 1st place with their project: “Potential Region Identification and Wind Power Potential Estimation in NTT using Deep Neural Network.”
Willyam explained that before identifying regions, the team collected the data they needed. They used the East Nusa Tenggara (NTT) administration border map from the Geospatial Information Agency, wind speed data from seven NTT meteorological stations (2015–2018) by NOAA, and a wind turbine dataset from Kaggle for comparison.
“We used the average annual wind speed in each regency to find the best regions for wind power in NTT,” said Willyam.
The second team won 2nd place with the project: “Region Identification in Indonesia Potential for Solar Power Development using K-Means Clustering.” The team included M. Iqbal Shiddiq, Dicky Arya Kesuma, and Naihan Nizar.
They used the K-Means Clustering method to group regions in Indonesia suitable for solar power plants (PLTS).
“Our results show areas suitable for building a solar power plant and areas that should be considered for development,” explained Iqbal. They used data from ECMWF, including wind speed, thermal radiation, solar radiation, and cloud cover.
The Best Presenter team consisted of Efrad Galio, Imam Muhajir, and Nova Novianti. Their project was “Analysis of Earthquake Station Spread and Forecasting Earthquake Strength using the SARIMA Algorithm – Sumatera Region.”
Indonesia is prone to earthquakes and tsunamis due to moving tectonic plates. Early warning is very important. Their team analyzed earthquake stations to see if they were evenly distributed and could give better early warnings. They used earthquake data from USGS (1963–2016) and InaTEWS (2009–2019).
“We analyzed the spread of earthquake stations and predicted earthquakes caused by plate movements using the SARIMA algorithm,” explained Imam.
Faldi added, “Our data analysis aims to solve problems clearly, correctly, and have a positive impact.”