[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127975-en":3,"doc-seo-127975-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127975,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Developing Machine Learning Algorithms for Predicting House Prices in Surabaya Using IBM SPSS Modeler","Using PHP and IBM SPSS Modeler, the authors collect and preprocess housing data from Rumah123 in Surabaya for data mining and analytics. A dataset of 10,336 records is split into two clusters, then three machine learning models are trained and evaluated using 2,460 records per model. Results compare ANN, SVM, and CART across training and testing scenarios. ANN delivers the most consistent correlations, SVM performs worst, while CART fits better for the larger cluster and less for the smaller cluster. ANN and CART are presented as practical tools for predicting housing prices, with ANN offering higher accuracy.","# AI for valuation\n\nby Perpustakaan Referensi  \nSubmission date:31-Aug-202410:18AM (UTC+0700)  \nSubmission ID:2437197000  \nFile name:2024-08-28_Paper_Hansel_Davin_Sugiarto.docx(1.04M)  \nWord count:2587  \nCharacter count:14893  \n## Developing Machine Learning Algorithms for PredictingHouse Prices in Surabaya Using IBM SPSS Modeler\n\nHansel Davin Sugiarto10009-007-3670-6848,Doddy Prayogo2000-000-5319-36251,and Njo  \nAnastasia₃00-0003-4480-9365]  \n1.2Civil Engineering and Design,Petra Christian University,Surabaya,Indonesiahanseldavins@gmail.com3School of Businessand Management,Petra Christian University,Surabaya,Indonesia  \nAbstract.Using PHP and IBM's SPSS Modeler for data mining and analytics,the authors collected and processed housing data from Rumah123 in Surabaya.A total of 10,336 data points were divided into twoclusters.Three machine learn-ing(ML)models were developed using SPSS Modeler for 2,460 data points.Theresults indicate that the Artificial Neural Network(ANN)model provided themost consistent correlation across all scenarios,while the Support Vector Ma-chine(SVM)model performed the worst.The Classification and Regression Tree(CART)model showed good performance in both training and testing for thelarger cluster but did not perform as well with the smaller cluster.Overall,ANNand CART models can be used to predict housing prices,with ANN offeringhigher accuracy.  \nKeywords:Data Analysis,Web Scraping,Artificial Neural Network,SupportVector Machine,Classification And Regression Tree,Linear Regression.  \n## 1 Introduction\n\nThe growing demand for housing in Indonesia underscores the essential nature of resi-dential properties [1][2].Surabaya,a key economic hub in East Java,is recognized forits significant real estate potential in the Asia-Pacific region [3][4].The city's rapideconomic growth and urbanization have led to rising housing prices,highlighting theneed for accurate property valuation [5][6].Surabaya has seen the highest propertyprice increase in Indonesia,with some areas experiencing a 34.88%rise,necessitatingcareful consideration of various factors in property purchases [7][8][9].Accurate as-sessments of property value and lifespan are crucial to avoid speculative pricing andunmet objectives [10][11].  \nMachine learning(ML)algorithms offer a solution by improving the accuracy ofprice predictions,addressing thecomplexities ofunique locations [10].Previous studieshave successfully applied ML and statistical methods to predict property prices,suchas using artificial neural networks(ANN)in Italy and Spain to assess environmentaland location factors [12][13],and the Random Forest(RF)algorithm in China for ac-curate price predictions [14].  \n\n| 2  \u003Cbr>This study aims to utilize AI algorithms and Linear Regression(LR)methods to  \u003Cbr>enhance property valuation techniques in Surabaya,contributing to more reliable and  \u003Cbr>advanced predictive models.  \u003Cbr>2 Methodology  \u003Cbr>2.1 Data Collection  \u003Cbr>The data collection process begins with searching for publicly available online real es-  \u003Cbr>tate platforms on the internet.Data collection was decided to be performed using web  \u003Cbr>scraping on the \"Rumah123\"website (https://www.rumah123.com).Rumah123 is an  \u003Cbr>Indonesia online real estate platform that provides information about various types of  \u003Cbr>residential properties,including new homes,resale homes,and second-hand homes.  \u003Cbr>Rumah123 offers extensive and additional information about each property,related to  \u003Cbr>the attributes and characteristics of the properties,such as building age,listing descrip-  \u003Cbr>tion,certification(SHM,SHGB,etc.),and more.To perform scraping on the Ru-  \u003Cbr>mah123 site,a web scraping process plan is required to assist in the automatic infor-  \u003Cbr>mation retrieval process using PHP programming language(see Figure 1).  \u003Cbr>Start  \u003Cbr>1  \u003Cbr>subdistrikts=1  \u003Cbr>1.aemrowa  \u003Cbr>Qea the jio ue Ssting that a  \u003Cbr>i=1  \u003Cbr>saved be fore  \u003Cbr>3.bubutan  \u003Cbr>FaterUR PeP  \u003Cbr>,ah¹23om/jial  \u003Cbr>6.gay","cbCaiaB9qHpyayZB","https://ap.wps.com/l/cbCaiaB9qHpyayZB","pdf",1220320,5,1,11,"English","en",105,"# Developing Machine Learning Algorithms for Predicting House Prices in Surabaya Using IBM SPSS Modeler\n## 1 Introduction\n## 2 Methodology\n### 2.1 Data Collection","[{\"question\":\"What data source and collection method are used to build the housing dataset?\",\"answer\":\"Housing data are collected from Rumah123 in Surabaya using web scraping implemented with PHP.\"},{\"question\":\"How is the dataset prepared before training machine learning models?\",\"answer\":\"The collected dataset contains 10,336 data points and is divided into two clusters; model training and evaluation use 2,460 data points.\"},{\"question\":\"Which machine learning model performs best for predicting house prices?\",\"answer\":\"The Artificial Neural Network (ANN) model provides the most consistent correlation across scenarios, while SVM performs worst.\"}]","Developing Machine Learning Algorithms for Predicting House Prices in Surabaya Using IBM SPSS Modeler | PDF",1785943507,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"developing-machine-learning-algorithms-for-predicting-house-prices-in-surabaya-using-ibm-spss-modeler","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/developing-machine-learning-algorithms-for-predicting-house-prices-in-surabaya-using-ibm-spss-modeler/127975/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What data source and collection method are used to build the housing dataset?","Question",{"text":77,"@type":78},"Housing data are collected from Rumah123 in Surabaya using web scraping implemented with PHP.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is the dataset prepared before training machine learning models?",{"text":82,"@type":78},"The collected dataset contains 10,336 data points and is divided into two clusters; 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