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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Parulian Josaphat, Bony
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Computer Science & IT

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Classification of Urban and Rural Villages with Machine Learning on Satellite Image Data and Points of Interest Parulian Josaphat, Bony; Syukur Rahmat Zega, Alvandi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.495

Abstract

An evaluation of the Sustainable Development Goals with data disaggregated by residential area, namely urban and rural areas, is essential. This study proposes the use of satellite imagery and point of interest (POI) data with machine learning methods to classify urban and rural villages, specifically in North Sumatra Province. The data used includes satellite imagery from various sources, such as NOAA-20, Sentinel-2, Sentinel-5P, and Terra, as well as Google Maps, covering various variables including NTL, NDVI, NDBI, NDWI, NO?, CO, and LST, along with POIs categorized under education, economy, health, and entertainment. The machine learning methods used were Decision Tree and Support Vector Machine, with data imbalance addressed through resampling techniques such as Random Under sampling (RUS). The results of the study show that the Support Vector Machine model with RUS produced the best weighted average F1-score of 87.74% for the classification of urban and rural villages, with NTL being the most important feature in the model formation. This study is expected to be an alternative for BPS in the classification of urban and rural villages.
Forecasting Composite Stock Price Index on Indonesia Stock Exchange Using Extreme Learning Machine Parulian Josaphat, Bony; Hutajulu, Dhevri Leonardo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.496

Abstract

Technological advances have driven active participation in digital economic activities, including capital market investment. Stocks remain a dominant instrument, with the Composite Stock Price Index or Indeks Harga Saham Gabungan (IHSG) serving as a primary benchmark for investment decisions in Indonesia. However, its high volatility—driven by economic, political, global, and market sentiment factors—demands accurate forecasting methods. Traditional approaches such as ARIMA and linear regression are limited in capturing the non-linear and complex patterns of stock market data. This study proposes the use of the Extreme Learning Machine (ELM), an artificial intelligence method considered more adaptive to market dynamics. To enhance prediction accuracy, hyperparameter optimization was performed using the grid search method. The research forecasts IHSG performance by incorporating exogenous variables, namely gold prices, the US dollar to rupiah exchange rate, and a COVID-19 dummy variable. The optimal model utilized a hidden layer configuration of nine neurons. Evaluation results indicate that the ELM models effectively perform multi horizon forecasting (t+1 to t+5), as evidenced by low MAE, MAPE, and RMSE values across horizons. The five-day IHSG forecasts are 7,242.28, 7,228.42, 7,211.02, 7,192.67, and 7,174.06, demonstrating the model’s potential in supporting investment decision-making with high accuracy.
Co-Authors Hutajulu, Dhevri Leonardo Syukur Rahmat Zega, Alvandi