DINAMIC MODELING OF SOCIO – ECONOMIC ACTIVITY CHANGES AROUND THE PANDAAN – MALANG TOLLL GATE USING NIGHT TIME LIGHT AND ANALYSIS MACHINE LEARNING

Authors

  • Ester Parmanes Institut Teknologi Nasional Malang
  • Agustina Nurul Hidayati Institut Teknologi Nasional Malang
  • Agung Witjaksono Institut Teknologi Nasional Malang
  • Firman Afrianto Ikatan Ahli Perencanaan Jawa Timur

DOI:

https://doi.org/10.32493/jtsi.v8i2.54049

Keywords:

Machine Learning, Night-Time Light (NTL), Random Forest, Toll Road Infrastructure

Abstract

The study of the Pandaan–Malang Toll Road development is relevant for understanding the impact of infrastructure on socio-economic changes based on spatial data. To date, quantitative approaches that combine satellite imagery data with artificial intelligence algorithms have rarely been used comprehensively to assess regional dynamics. This study develops a machine learning-based prediction model to examine the relationship between infrastructure development and the intensity of community activities. The primary data analyzed is Night-Time Light (NTL) from satellite observations, which is treated as an indicator of the level of economic and social activity on the earth's surface. The research stages include the process of extracting and transforming spatial data, temporal analysis of night light intensity from 2013 to 2023, and the application of the Random Forest algorithm to predict trends until 2040. The findings indicate that areas adjacent to the toll gate show a consistent increase in night light intensity, reflecting faster growth in economic activity and population density compared to other areas. The developed prediction model demonstrates high performance with a low mean squared error (MSE) value. The integration of NTL data and the machine learning approach has proven to be able to describe spatial dynamics more objectively and precisely. Scientifically, this study introduces a replicable analytical framework to support evidence-based decision-making in infrastructure planning and sustainable regional development.

References

This shows that the growth in activity density will spread following the development of urban sprawl and transportation connectivity. LL Village remains isolated in the northern part away from the main transport node.

4. Conclusion

This study proves that the image of Night-Time Light (NTL) is effectively used as a proxy for socio-economic activities to analyze the impact of the construction of the Pandaan-Malang Toll Road. The results of the NTL Intensity Accumulation analysis showed a significant increase in nighttime activity in villages that have high accessibility to toll gate points. Villages such as Sawojajar, Madyopuro, Banjararum, Pandaan, Petungasri, and Mergosono consistently became the areas with the highest light intensity values, both in total (SUM) as NTL Accumulation and density (MEAN) as NTL Intensity. Correlation analysis shows that the relationship between distance to toll gates and new activity growth appears strong in post-operational years, with greater positive correlation values in 2023. LISA's analysis confirms this finding through a pattern of High-High (HH) clusters that are growing from 2013 to 2023, even dominating the central-south region in the 2030 and 2040 predictions. This shows that toll roads not only increase activity in total, but also trigger the concentration, densification and expansion of new growth clusters along the regional corridor.

Random Forest's prediction model shows good performance, demonstrated by low error and spatially consistent prediction patterns. The predictions show that areas with high accessibility will continue to grow until 2040, while areas with low access tend to remain in the category of low activity (LL). Overall, this study confirms that the existence of toll infrastructure plays a strong role in driving the dynamics of spatial-based socio-economic growth, which is clearly recorded through changes in the intensity of night light.

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Published

2026-02-12

How to Cite

Parmanes, E., Hidayati, A. N., Witjaksono, A., & Afrianto, F. (2026). DINAMIC MODELING OF SOCIO – ECONOMIC ACTIVITY CHANGES AROUND THE PANDAAN – MALANG TOLLL GATE USING NIGHT TIME LIGHT AND ANALYSIS MACHINE LEARNING. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 8(2). https://doi.org/10.32493/jtsi.v8i2.54049

Issue

Vol. 8 No. 2 (2025): Jurnal Teknologi Sistem Informasi dan Aplikasi

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Article

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Copyright (c) 2025 Ester Parmanes, Agustina Nurul Hidayati, Agung Witjaksono, Firman Afrianto

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