[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125047-en":3,"doc-seo-125047-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125047,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","MACHINE LEARNING TO IDENTIFY SLUM SETTLEMENTS BASED ON SATELLITE IMAGERY - CASE STUDY: PADANG CITY","Mapping slums in Indonesia often relies on survey-based methods that require substantial time and budget, and can be risky during rapid or disruptive conditions while also producing inconsistent results due to differences in surveyors’ interpretations. This study proposes remote-sensing slum identification using machine learning with High-Resolution Satellite Imagery (CSRT) and a support vector machine (SVM) approach. Results for Padang City show predominantly light slum categories, with slum areas identified across 45 villages totaling 129.16 hectares, supported by spatial development gaps with hinterland areas.","Sumatra Journal of Disaster, Geography and Geography Education, June, 2024 Vol.8, No.1, pp.126-132  \nDISASTER, GEOGRAPHY, GEOGRAPHY EDUCATION [http://sjdgge.ppj.unp.ac.id/index.php/Sjdgge](http://sjdgge.ppj.unp.ac.id/index.php/Sjdgge)  \n[ISSN : 2580-4030](ISSN : 2580-4030) ( Print ) 2580-1775 ( Online), Indonesia  \nMACHINE LEARNING TO IDENTIFY SLUM SETTLEMENTS BASED ON SATELLITE IMAGERY CASE STUDY: PADANG CITY  \n*Risky Ramadhan1, Azhari Syarief1  \n1 Departement of Geography – Universitas Negeri Padang  \nEmail: [riskyramadhan@fis.unp.ac.id](riskyramadhan@fis.unp.ac.id)  \n*Corresponding Author, Received: May 10,2024, Revised: May 28, 2024, Accepted: June 28, 2024  \nABSTRACT: Mapping of slums in Indonesia uses a survey-based mapping method. This method requires alot of time and money, especially when updating data, especially during the current pandemic, it will be risky. Based on the Minister of PUPR Number 14/PRT/M/2020 . Over the last 6 years (2014-2020), the mapping of slums cost 382 billion. In addition, the main disadvantage of the survey method is the inconsistency of the results due to the interpretation of surveyors in different fields. To overcome this problem, remote sensing-based slum identification with a machine learning approach with the help of HighResolution Satellite Imagery (CSRT) and support vector machine (SVM) features can quickly and cheaply calculate slum areas. Therefore, the objectives ofthis study are (1) Identifying the distribution of slums using machine learning methods based on Satellite Imagery in Padang City. Based on the description above, it can be concluded that the level of slums in the identified areas of slum settlements in Padang City is predominantly light category. There is only one ward, Teluk Kabung Selatan, which is at the medium category. Slums not always squatter which is proven by the ownership status of the land certificate. Pasar Ambacang Ward has the most families in slum settlements (1.112 families) . Meanwhile, Tarantang Ward has the smallest slum families in the identified slum area (25 families) . Based on the results of the analysis using support vector machine and field verification, slums were identified in 45 (forty-five) villages spread across 11 sub-districts with a total area of 129.16 hectares. This condition is supported by the centralization of activities in the core village of Padang City, which creates a development gap with the hinterland area.  \nKeywords: Slum Area, Machine Learning, Support Vector Machine, Padang  \n1. INTRODUCTION  \nHandling slum settlements became an international agenda campaigned on Millenium Development Goals (MDGs) [1] and Sustainable Development Goals (SDGs) [2] . Mapping slum settlements in Indonesia using survey methods (survey-based mapping) which is based on physical and social criteria [3] . Based on PUPR Ministerial Regulation Number 14/PRT/M/2020 [4], over the last 6 years (2014-2020), slum mapping has cost 382 billion. Apart from that, the main drawback of the survey method is that there are inconsistencies in area results due to different interpretations of surveyors in the field.  \nIn 2018, the total area of slum areas in Indonesia was 87,000 ha, reported by 390 cities/regencies in Indonesia [5] . Padang City according to Regional Regulation No. 501 of 2019 has a residential area of 122, 83 ha and 2,224 houses are unfit for habitation [6] . In line with the publication of the 2020-2024 RPJMN [7] This needs to be handled quickly, both in identifying the location of slum settlements.  \nTo overcome this problem, a remote sensing-  \nbased slum settlement identification approach is proposed by machine learning. Machine Learning is one part of artificial intelligence which focuses on the classification of an object based on image processing. Feature extraction and classification via image processing methods has become very popular, especially based on the distance between named points support vector machines (SVM) [8]  \n[9] [10] . SVM","cbCaidUKRJJphp5u","https://ap.wps.com/l/cbCaidUKRJJphp5u","pdf",748747,1,7,"English","en",105,"# Introduction\n# Research Methods","[{\"question\":\"Why are survey-based slum mapping methods considered problematic?\",\"answer\":\"They require significant time and cost for updates, and they can produce inconsistent area results because surveyors interpret physical and social criteria differently.\"},{\"question\":\"What machine learning approach is used to identify slum settlements?\",\"answer\":\"The study applies remote sensing-based classification using High-Resolution Satellite Imagery (CSRT) and features processed with a support vector machine (SVM) model.\"},{\"question\":\"What were the main findings about slum levels and identified locations in Padang City?\",\"answer\":\"Most identified areas fall in the light slum category, with one ward (Teluk Kabung Selatan) in the medium category. Slums were identified in 45 villages across 11 sub-districts covering 129.16 hectares.\"}]","MACHINE LEARNING TO IDENTIFY SLUM SETTLEMENTS BASED ON SATELLITE IMAGERY - CASE STUDY: PADANG CITY | PDF",1785896347,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-to-identify-slum-settlements-based-on-satellite-imagery-case-study-padang-city","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-to-identify-slum-settlements-based-on-satellite-imagery-case-study-padang-city/125047/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are survey-based slum mapping methods considered problematic?","Question",{"text":75,"@type":76},"They require significant time and cost for updates, and they can produce inconsistent area results because surveyors interpret physical and social criteria differently.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approach is used to identify slum settlements?",{"text":80,"@type":76},"The study applies remote sensing-based classification using High-Resolution Satellite Imagery (CSRT) and features processed with a support vector machine (SVM) model.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings about slum levels and identified locations in Padang City?",{"text":84,"@type":76},"Most identified areas fall in the light slum category, with one ward (Teluk Kabung Selatan) in the medium category. 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