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All Journal Geoplanning : Journal of Geomatics and Planning Jurnal Aplikasi Statistika & Komputasi Statistik
Marcelinus Alfafisurya Setya Adhiwibawa
Agricultural Data System Scientist, PCTC, Mondelez International
Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Mathematics

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INTERPOLASI POLUTAN NITROGEN DIOKSIDA (NO2) DENGAN PENDEKATAN ORDINARY KRIGING DAN INVERSE DISTANCE WEIGHTED (STUDI KASUS DI KOTA YOGYAKARTA) Muthia Citra Safira; Achmad Fauzan; Marcelinus Alfafisurya Setya Adhiwibawa
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 14 No 2 (2022): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v14i2.359

Abstract

Permasalahan yang kerap terjadi di kota-kota besar adalah pencemaran udara. Nitrogen Dioksida (NO2) merupakan salah satu zat pencemar udara berbahaya yang berkontribusi besar terhadap pencemaran udara. Dalam rangka pemantauan kualitas udara ambien, maka Dinas Lingkungan Hidup Kota Yogyakarta melakukan pengukuran di beberapa titik lokasi untuk mengetahui konsentrasi dari zat pencemar tersebut. Namun karena pengukuran memerlukan proses yang panjang dan terhalang dana yang besar, maka pengukuran tidak dilakukan di semua titik lokasi. Oleh karena itu, diperlukan suatu metode interpolasi spasial untuk mengestimasi konsentrasi NO2 di Kota Yogyakarta yang lokasinya tidak dilakukan pengukuran. Metode yang digunakan adalah Ordinary Kriging (OK) dan Inverse Distance Weighted (IDW). Dari hasil analisis diperoleh bahwa metode yang paling akurat untuk estimasi konsentrasi NO2 di Kota Yogyakarta adalah OK. Hal ini dikarenakan hasil perhitungan nilai Root Mean Square Error (RMSE) pada OK lebih kecil, yaitu 0.4847 dibanding 0.5224 pada IDW
Spatial Classification of Sentinel-2 Satellite Images with Machine Learning Approach Dea Ratu Nursidah; Achmad Fauzan; Marcelinus Alfafisurya Setya Adhiwibawa
Geoplanning: Journal of Geomatics and Planning Vol 12, No 2 (2025)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.12.2.253-266

Abstract

Urban expansion and land use change are increasingly critical issues in developing regions, where rapid development often leads to unplanned growth and environmental challenges. Accurate and timely classification of built-up and non-built-up areas is essential for supporting sustainable spatial planning and resource management. This study aims to classify built-up and non-built-up areas from Sentinel-2 satellite imagery using a machine learning approach and to analyze their spatial distribution around the Universitas Islam Indonesia (UII) campus. Three machine learning algorithms—Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT)—were applied to perform the classification, and their performances were evaluated using four metrics: accuracy, sensitivity, specificity, and Area Under the Curve (AUC). Among these, the SVM method demonstrated the best performance based on the highest average accuracy, the smallest variance difference between training and testing datasets, and consistent results across multiple iterations. Using the classification results from the best-performing model, a spatial density proportion analysis was conducted. The findings revealed a clear spatial trend: areas closer to the UII campus exhibited a higher proportion of built-up land, while areas located farther from the center had a greater share of non-built-up land. These results confirm the effectiveness of the SVM algorithm for land cover classification using Sentinel-2 imagery and offer valuable insights into urban development patterns in the study area. The outcomes of this research can inform urban planners and policymakers in developing data-driven strategies for sustainable land use, infrastructure development, and campus-centered regional growth planning.
Co-Authors ACHMAD FAUZAN Dea Ratu Nursidah Muthia Citra Safira