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All Journal Journal of Applied Civil and Environmental Engineering (JACEE) Geoid - Journal of Geodesy and Geomatics
Bambang Bakri
Universitas Hasanuddin
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Engineering Environmental Science Materials Science & Nanotechnology Transportation

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Sistem Informasi Geografis Kualitas Air Sumur Di Kota Makassar Kissan S.T.; Makmur Rauf; Mary Selintung; Bambang Bakri
Journal of Applied Civil and Environmental Engineering Vol. 1 No. 1 (2021): April 2021
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jacee.v1i1.2706

Abstract

Air merupakan kebutuhan pokok bagi manusia sehingga ketersedian air bersih untuk kelangsungan hidup manusia mutlak diperlukan. Kecamatan Biringkanaya sebagai salah satu kecamatan di Kota Makassar ketersedian air bersih dan air minum sangat terbatas. Hal ini ditandai dengan pelayanan PDAM yang belum terjangkau diseluruh wilayah kecamatan ini. Umumnya penduduk mengandalkan kepada sumur gali didaerahnya. Akan tetapi dengan terbatasnya informasi terkait air bersih khususnya didaerah ini maka penelitian ini bertujuan memberikan informasi tentang kualitas air berbasis Sistem Informasi Geografis. Dari 890 sumur gali di kecamatan ini diambil masing-masing satu sampel ditiap kelurahan untuk diuji dilaboratorim dan disajikan dalam bentuk SIG. Hasil menunjukkan bahwa di 7 (tujuh) kelurahan di Kecamatan Biringkanaya, kualitas air bersih tergolong dalam kategori tercemar sedang dengan nilai IP > 5 sedangkan untuk air minum kualitas air minum tergolong tercemar berat
Detection of River Change in Modeling Flood Vulnerability using Support Vector Machine (SVM) Methods in Tallo River Makassar City Atika Izzaty; Syahra Dewi Aprian; Regita Faridatunisa Wijayanti; Bambang Bakri
Geoid Vol 20 No 2 (2025)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v20i2.7536

Abstract

The transformation of river morphology and the rising frequency of flooding in urban environments have emerged as increasingly concerning environmental challenges, particularly in Makassar City. The Tallo River, one of the primary waterways traversing the city, exhibits notable dynamic changes driven by both natural processes. In the contemporary era, flooding stands as one of the most recurrent natural disasters, occurring unpredictably and posing serious risks, especially in major metropolitan areas. Such events frequently disrupt daily activities, leading to traffic congestion and obstructing ground transportation. Residential zones situated near riverbanks are particularly vulnerable to its impacts. Moreover, climate change exacerbates these conditions by contributing to increasing environmental unpredictability and need through a monitoring. The purpose of this research is to analyze river morphology changes and assess flood susceptibility in the Tallo River, Makassar City, using Support Vector Machine (SVM) classification methods. Approximately, there are 20% of the area experienced significant changes during 2018 in Tallo River. As water discharge continues to increase, the volume of water mass also rises accordingly. To detect the spatial distribution of flood vulnerability along the Tallo River, which flows through Makassar City, this study utilizes Land Use and Land Cover (LULC) data from 2017 and 2024. These datasets were classified using the Random Forest model, achieving accuracies of 0.89 and 0.87, respectively values that meet the standards for land use change accuracy. Flood vulnerability is also influenced by low elevation values, particularly areas below 0 meters, which are classified as wetland zones. In the Tallo River area, which is part of the Jeneberang Watershed, the dominant class is moderate flood vulnerability, covering approximately 138.48 hectares. Remote sensing technology combined with machine learning approaches especially supervised classification techniques widely used for both binary and multivariate classification tasks, demonstrating high accuracy in detecting and classifying flood vulnerability.
Co-Authors Atika Izzaty Kissan S.T. Makmur Rauf Mary Selintung Regita Faridatunisa Wijayanti Syahra Dewi Aprian