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All Journal Jurnal Pengembangan Kota Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi Indonesian Journal of Energy East Java Economic Journal Journal of Infrastructure Policy and Management (JIPM)
Afrianto, Firman
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences

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DINAMIC MODELING OF SOCIO – ECONOMIC ACTIVITY CHANGES AROUND THE PANDAAN – MALANG TOLLL GATE USING NIGHT TIME LIGHT AND ANALYSIS MACHINE LEARNING Parmanes, Ester; Hidayati, Agustina Nurul; Witjaksono, Agung; Afrianto, Firman
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 8 No. 2 (2025): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v8i2.54049

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.
Co-Authors Adityasna, Hardi Agung Witjaksono Agustina Nurul Hidayati Annisaa Hamidah Imaduddina Ardiyanto Maksimilianus Gai Dwi Handoko Endratno Budi Santoso Graha, Dimas Tri Rendra Hariyanto, Annisa Dira Ibnu Sasongko Mohammad Reza Muhammad Sani Roychansyah Novedy, Anita Claudia Parmanes, Ester Pusporini, Nuryantiningsih Rendra Graha, Dimas Tri Salsabillah, Andini Putri Setiawan, Alifianto Shalsabilla, Syahira Aulia Subnafeu, Eglantyne Lidya Titik Poerwati Yori Herwangi, Yori