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Journal : Jurnal Teknik Informatika (JUTIF)

Development of WebGIS for Street Light Mapping Using Geospatial Tools Anisya, Anisya; Fajrin, Fajrin; warman, Indra; Minarni, Minarni; Syahrani, Anna; Nugroho, Fajar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4777

Abstract

Padang City, as one of the cities the largest on the west coast of Sumatra Island, plays a strategic role in the economy and government. One of the vital infrastructures that supports public activities is the street lighting system. However, the monitoring and maintenance of streetlights still face obstacles, especially in North Padang District, which is the busiest area due to the presence of numerous educational facilities, government offices, and economic centers. This research aims to develop a WebGIS application that facilitates the monitoring and management of street lighting more efficiently. Our research contributes by introducing a new approach to spatial-based streetlight management strategies. This approach is based on a methodology for field data collection and spatial database development to manage all stages of streetlight infrastructure management. This application integrates geospatial technology by utilizing GeoServer, QGIS, and PostgreSQL for visualization and spatial data management. With this system, information about the location and condition of streetlights can be accessed in real-time, thereby facilitating better planning and maintenance of street lighting infrastructure. The result of this study is a WebGIS application capable of mapping and monitoring streetlight points interactively. The implementation of this system is expected to assist relevant authorities in improving the effectiveness of street lighting management in Padang City and contribute to the development of geospatial technology-based solutions for urban infrastructure.
Image-Based Classification of Rice Field Conversion: A Comparison Between MLP and SVM Using Multispectral Features Anisya, Anisya; Sumijan, Sumijan; Syahrani, Anna
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5179

Abstract

The conversion of farmland into non-agricultural purposes has emerged as a pressing concern in many urban regions, including Koto Tangah District, Padang City. In this area, agricultural land experienced a 4% shift in land use between 2022 and 2024. If this trend continues, it could lead to a notable decline in rice production and ultimately threaten food security. This research focuses on examining spatial transformations of rice fields from 2022 to 2024 by utilizing Sentinel-2 satellite imagery along with advanced classification techniques. Vegetation and moisture features were extracted using NDVI, NDWI, texture analysis through GLCM, and Principal Component Analysis (PCA). To classify land cover changes and assess model accuracy, two machine learning approaches were applied: Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The findings reveal a considerable reduction in dense vegetation, indicated by the downward shift of NDVI values in 2024. MLP achieved an accuracy of 82%, outperforming SVM, which reached 71%. Furthermore, MLP obtained a higher F1-score for non-rice field detection (0.75 vs. 0.74) and produced more realistic delineations of rice field boundaries during spatial validation. These outcomes highlight the potential of MLP in monitoring land use conversion, supporting agricultural land conservation, and guiding sustainable urban planning. Moreover, the study contributes to computer science by advancing the use of machine learning for spatio-temporal analysis and reinforcing the role of non-linear models in satellite image classification.