[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126318-id":3,"doc-seo-126318-113":30,"detail-sidebar-cat-0-id-113":92},{"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":20,"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},126318,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",54,"Penelitian & Laporan","Perbandingan Metode Machine Learning - Linear Regression, Random Forest, dan XGBoost dalam Memprediksi Kemiskinan di Jawa Tengah Tahun 2024","Kemiskinan merupakan isu pembangunan yang mengalami perubahan dari tahun ke tahun di Provinsi Jawa Tengah. Penelitian ini menggunakan pendekatan berbasis data prediktif dengan machine learning untuk meramalkan jumlah penduduk miskin pada level kabupaten/kota tahun 2024, memakai data sosial ekonomi 2019–2023 dari BPS. Tujuh indikator digunakan sebagai variabel prediktor, dinormalisasi menggunakan StandardScaler, lalu dibagi menjadi pelatihan 80% dan pengujian 20%. Perbandingan dilakukan pada Regresi Linier, Random Forest, dan XGBoost; hasil menunjukkan XGBoost paling unggul (MAE 6.665, R² 0.978).","Perbandingan Metode Machine Learning (Linear Regression,  \nRandom Forest, dan XGBoost) dalam memprediksi Kemiskinan diJawaTengah Tahun 2024  \nComparison ofMachine Learning Methods (Linear Regression, Random Forest, and XGBoost)for Predicting Poverty in Central Java in 2024  \n1Zahwa Bunga Putri Pratama, 2Yani Parti Astuti*  \n1,2Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro  \n1,2Jl. Imam Bonjol No.207, Semarang, Indonesia  \n*[e-mail:](e-mail:1zahwabngpr@gmail.com)[1](e-mail:1zahwabngpr@gmail.com)[zahwabngpr@gmail.com](e-mail:1zahwabngpr@gmail.com), [2](2yanipartiastuti@dsn.dinus.ac.id)[yanipartiastuti@dsn.dinus.ac.id](2yanipartiastuti@dsn.dinus.ac.id)*  \n(received: 3 July 2025, revised: 21 July 2025, accepted: 22 July 2025)  \nAbstrak  \nKemiskinan adalah isu besar yang dihadapi oleh Provinsi Jawa Tengah, dengan tingkat yang mengalami perubahan setiap tahun. Untuk merespons dan menangani masalah ini dengan lebih efektif, dibutuhkan pendekatan berbasis data yang bersifat prediktif. Penelitian ini menerapkan teknik machine learning untuk memprediksi jumlah penduduk miskin pada tahun 2024 di tingkat kabupaten/kota, dengan memanfaatkan data sosial ekonomi antara tahun 2019 hingga 2023 dari Badan Pusat Statistik (BPS) . Tujuh indikator digunakan sebagai variabel prediktor, termasuk gariskemiskinan, jumlah serta persentase penduduk yang hidup dalam kemiskinan, tingkat pengangguranterbuka, rata-rata lama pendidikan, indeks pembangunan manusia, dan upah minimum di tingkat kabupaten/kota. Data yang diperoleh dinormalisasi menggunakan StandardScaler dan dibagi menjadidua bagian, yaitu data pelatihan (80%) dan data pengujian (20%).Penelitian ini membandingkan tiga algoritma regresi, yakni Regresi Linier, Random Forest, dan XGBoost, untuk menilai seberapa efektif model-model tersebut dalam menangani kompleksitas data sosial ekonomi. Hasil analisis menunjukkan bahwa XGBoost menghasilkan kinerja terbaik dengan nilai MAE sebesar 6.665 dan R² sebesar 0.978, lebih unggul dibandingkan Random Forest (MAE 9.209; R² 0.947) dan Regresi Linier (MAE 10.917; R² 0.896) . Perbandingan antara ketiga model ini dilakukan untuk mengisi celah dalam literatur yang berkaitan dengan efektivitas model dalam konteks prediksi kemiskinan lokal. Hasil dari penelitian ini menunjukkan bahwa XGBoost memiliki potensi untuk menjadi instrumen pendukung kebijakan berbasis data, khususnya dalam perencanaan dan usaha pengentasan kemiskinan di tingkat daerah.  \nKata kunci: kemiskinan, prediksi, machine learning, jawa tengah  \nAbstract  \nPoverty is a major issue faced by Central Java Province, with rates fluctuating annually. To respond to and address this challenge more effectively, a predictive, data-driven approach is essential. This study applies machine learning techniques to forecast the number of people living in poverty in 2024 at the district/city level, utilizing socio-economic data from 2019 to 2023 provided by the Central Bureau of Statistics (BPS). Seven indicators are used as predictor variables, including the poverty line, the number and percentage of people living in poverty, the open unemployment rate, average years of schooling, the Human Development Index, and the regional minimum wage. The data were normalized using StandardScaler and split into training (80%) and testing (20%) sets. This study compares three regression algorithms—Linear Regression, Random Forest, and XGBoost—to evaluate their effectiveness in modeling the complexity of socio-economic data. The analysis reveals that XGBoost delivers the best performance, with a Mean Absolute Error (MAE) of 6,665 and an R² score of 0.978, outperforming Random Forest (MAE: 9,209; R²: 0.947) and Linear Regression (MAE: 10,917; R²: 0.896). By comparing these models, the study addresses a gap in the literature regarding the effectiveness of machine learning models for local-level poverty prediction. The findings suggest  \nthat XGBoost holds strong potential as a data-driven po","cbCaibF02gh9RtfJ","https://ap.wps.com/l/cbCaibF02gh9RtfJ","pdf",486570,8,1,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang kemiskinan dan kebutuhan pendekatan berbasis data\n## Rangkuman metode prediktif: Regresi Linier, Random Forest, dan XGBoost","[{\"question\":\"Penelitian ini memprediksi apa, dan pada wilayah mana?\",\"answer\":\"Penelitian ini memprediksi jumlah penduduk miskin pada tahun 2024 di tingkat kabupaten/kota di Provinsi Jawa Tengah.\"},{\"question\":\"Indikator apa saja yang digunakan sebagai variabel prediktor?\",\"answer\":\"Sebanyak tujuh indikator digunakan, termasuk garis kemiskinan, jumlah dan persentase penduduk miskin, tingkat pengangguran terbuka, rata-rata lama pendidikan, indeks pembangunan manusia, dan upah minimum kabupaten/kota.\"},{\"question\":\"Algoritma mana yang menghasilkan kinerja terbaik dan bagaimana metriknya?\",\"answer\":\"XGBoost menghasilkan kinerja terbaik dengan MAE 6.665 dan R² 0.978, lebih unggul dibanding Random Forest (MAE 9.209; R² 0.947) dan Regresi Linier (MAE 10.917; R² 0.896).\"}]","Perbandingan Metode Machine Learning - Linear Regression, Random Forest, dan XGBoost dalam Memprediksi Kemiskinan di Jawa Tengah Tahun 2024 | PDF",1785904419,12,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"comparison-of-machine-learning-methods-linear-regression-random-forest-and-xgboost-for-predicting-poverty-in-central-java-in-2024","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/id/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/id/document/comparison-of-machine-learning-methods-linear-regression-random-forest-and-xgboost-for-predicting-poverty-in-central-java-in-2024/126318/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-17","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Penelitian ini memprediksi apa, dan pada wilayah mana?","Question",{"text":76,"@type":77},"Penelitian ini memprediksi jumlah penduduk miskin pada tahun 2024 di tingkat kabupaten/kota di Provinsi Jawa Tengah.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Indikator apa saja yang digunakan sebagai variabel prediktor?",{"text":81,"@type":77},"Sebanyak tujuh indikator digunakan, termasuk garis kemiskinan, jumlah dan persentase penduduk miskin, tingkat pengangguran terbuka, rata-rata lama pendidikan, indeks pembangunan manusia, dan upah minimum kabupaten/kota.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma mana yang menghasilkan kinerja terbaik dan bagaimana metriknya?",{"text":85,"@type":77},"XGBoost menghasilkan kinerja terbaik dengan MAE 6.665 dan R² 0.978, lebih unggul dibanding Random Forest (MAE 9.209; 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