[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123263-en":3,"doc-seo-123263-105":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},123263,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Comparative Analysis of Machine Learning Algorithms - on Family Wellness Classification - Performance comparison","Family welfare reflects happiness, an adequate quality of life, and sufficient fulfillment of primary and secondary needs. Per capita expenditure is a key factor used to assess whether families are prosperous across Indonesian provinces. The study compares KNN, random forest, and naive Bayes classifiers using 2017–2021 demographic and social statistics (170 records). After PCA reduces 9 predictors into four components, model quality is evaluated with accuracy, precision, recall, and F1-score, showing random forest as the most suitable approach.","Comparative Analysis of Machine Learning Algorithmson Family  \nWellness Classification  \nRetno Budiarti*, Febri Hemarani, Mohammad Reza, Rindi Melati Mulyasari  \n1 Department of Mathematics, Institut Pertanian Bogor, Indonesia Email: [retnobu@apps.ipb.ac.id](retnobu@apps.ipb.ac.id)  \nABSTRACT  \nFamily welfare is a state in which a family can experience happiness, have a decent quality of life, and be sufficient in meeting primary and secondary needs in family life. One factor that influences family welfare is the amount of per capita expenditure. This study aims to compare the performance of three machine learning algorithms, namely KNN (K-Nearest Neighbors), random forest, and naive Bayes, in classifying the status of families per province in Indonesia as prosperous or not prosperous. The data used in this study are 170 demographic and social statistics data from the years 2017-2021, obtained from the bps.go.id website. The first statistical analysis conducted is principal component analysis (PCA) with 9 predictor variables. PCA produces four principal components which are then used in the KNN, random forest, and naive bayes methods. The analysis results from the KNN yield an 63.46% accuracy, 62.07% precision, 69.23% recall, and 65.46% F1-score. The analysis results from the random forest yield an 69.23% accuracy, 70.83% precision, 65.38% recall, and 68.00% F1-score. The analysis results from the KNN yield an 57.69% accuracy, 54.35% precision, 96.15% recall, and 69.44% F1-score. Based on the values shown, the Random Forest method is the most suitable for classifying prosperous families.  \nKeywords: consumption expenditure; family welfare; KNN; naive bayes; random forest  \nCopyright © 2024 by Authors, Published by CAUCHY Group. This is an open access article under the CC BY-SA License ([https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/))   \nINTRODUCTION  \nA family is a group of individuals within a small social system where each member depends on and influences one another. Every family has the same goal, which is to achieve well-being. Family well-being is a condition where a family can experience happiness, have a decent quality of life, and be sufficient in meeting the primary and secondary needs in family life [1] .  \nDifferences in family well-being are influenced by the quality of human resources. The level of education, the family's economic condition, and access to facilities and infrastructure are some factors that affect the quality of human resources [2] . Family well-being is also influenced by the family's economic condition, which is reflected in the head of the family's income, the family's social condition, depicted by the head of the family's education level and occupation, as well as the family's living conditions [3] . Sources of lighting, household consumption, the ability to use transportation facilities, and access to healthcare services are also factors that affect family well-being [4] .  \nBased on these factors, family well-being can be determined by creating a predictive model. This model will be used to classify families as either well-off or not well-off. The methods used to classify the level of family well-being in this research are the K-Nearest Neighbors (KNN), K-Fold Cross Validation, and Bootstrap methods. The KNN method is chosen because the data used is secondary data, and the main purpose of this method is to classify new objects based on existing attributes and the training sample [5] . The KNN method predicts the category of new data that is added, in this case, families based on provinces, and determines whether they are closer to the well-off category or not. This KNN method is highly effective, productive, and easy to use for classifying data [6]. Additionally, this method is easy to implement, effective on large datasets, and fast in processing training data [7]. The Random Forest method is chosen because it can improve accuracy in situat","cbCaiuB6dEk0vnpx","https://ap.wps.com/l/cbCaiuB6dEk0vnpx","pdf",944260,1,15,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Data and Research Stages","[{\"question\":\"Which machine learning algorithms are compared for family welfare classification?\",\"answer\":\"The study compares KNN (K-Nearest Neighbors), random forest, and naive Bayes to classify families as prosperous or not prosperous by province.\"},{\"question\":\"What dataset and preprocessing steps are used in the study?\",\"answer\":\"It uses 170 demographic and social statistics records from 2017–2021 from bps.go.id. PCA is applied to 9 predictor variables, producing four principal components for the classifiers.\"},{\"question\":\"How are the models evaluated, and which method performs best?\",\"answer\":\"Performance is measured using accuracy, precision, recall, and F1-score. The results indicate that random forest is the most suitable method for classifying prosperous families.\"}]","Comparative Analysis of Machine Learning Algorithms - on Family Wellness Classification - Performance comparison | PDF",1785815544,38,{"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},"comparative-analysis-of-machine-learning-algorithms-on-family-wellness-classification-performance-comparison","",{"@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/comparative-analysis-of-machine-learning-algorithms-on-family-wellness-classification-performance-comparison/123263/",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","2026-08-04",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},"Which machine learning algorithms are compared for family welfare classification?","Question",{"text":75,"@type":76},"The study compares KNN (K-Nearest Neighbors), random forest, and naive Bayes to classify families as prosperous or not prosperous by province.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and preprocessing steps are used in the study?",{"text":80,"@type":76},"It uses 170 demographic and social statistics records from 2017–2021 from bps.go.id. PCA is applied to 9 predictor variables, producing four principal components for the classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated, and which method performs best?",{"text":84,"@type":76},"Performance is measured using accuracy, precision, recall, and F1-score. 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