[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124904-id":3,"doc-seo-124904-113":31,"detail-sidebar-cat-0-id-113":85},{"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},124904,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",53,"Layanan Kesehatan","Implementasi Machine Learning dalam Deteksi Risiko Tinggi Diabetes Melitus pada Kehamilan - KNN","Diabetes dalam kehamilan meningkatkan risiko bagi ibu dan neonatus akibat gangguan homeostasis glukosa, yang dapat memicu malformasi kongenital, keguguran, risiko preeklampsia, CPD, kelahiran prematur, kelainan letak, plasenta previa, serta hipoglikemia neonatus. Penelitian ini memanfaatkan teknologi machine learning untuk mendeteksi risiko tinggi diabetes melitus menggunakan data ibu hamil seperti usia, umur kehamilan, gravida-para, riwayat kehamilan/penyakit, faktor risiko, dan riwayat persalinan dari Puskesmas Lawang (Jan–Agt 2021). Model menggunakan 16 atribut dan algoritme K-Nearest Neighbor (KNN) dengan akurasi tertinggi 78.38% pada k=4.","IMPLEMENTASI MACHINE LEARNING DALAM DETEKSI RISIKO TINGGI DIABETES MELITUS PADA KEHAMILAN  \nAryo Pinandito*1, Satrio Agung Wicaksono2, Satrio Hadi Wijoyo3  \n1, 2, 3Universitas Brawijaya, Malang  \n[Email:](Email:1aryo@ub.ac.id)[1](Email:1aryo@ub.ac.id)[aryo@ub.ac.id](Email:1aryo@ub.ac.id), [2](2satrio@ub.ac.id)[satrio@ub.ac.id](2satrio@ub.ac.id), [3](3satriohadi@ub.ac.id)[satriohadi@ub.ac.id](3satriohadi@ub.ac.id)  \n*Penulis Korespondensi  \n(Naskah masuk: 29 Desember 2022, diterima untuk diterbitkan: 25 Juli 2023)  \nAbstrak  \nDiabetes dalam kehamilan dapat meningkatkan berbagai risiko, baik pada maternal maupun neonatus. Adanya gangguan homeostasis glukosa dalam kehamilan dapat meningkatkan terjadinya malformasi kongenital, keguguran, risiko preeklampsia, Cepalo Pelvik Dispropotion (CPD), kelahiran prematur, kelainan letak, plasenta previa dan hipoglikemia neonatus. Oleh karena itu, diperlukan perhatian dan penanganan menyeluruh bagi ibuhamil yang menderita diabetes. Teknologi machine learning dapat dimanfaatkan dalam berbagai hal di bidang kesehatan. Salah satu dari pemanfaatan machine learning di bidang kesehatan adalah kemampuannya untuk mendeteksi risiko tinggi diabetus mellitus pada ibu hamil melalui berbagai data dan informasi seperti nama, usia, umur kehamilan, gravida, para, riwayat kehamilan, riwayat penyakit yang pernah diderita, faktor risiko, dan riwayat persalinan yang lalu. Proses persalinan ibu hamil dipengaruhi oleh faktor fisiologis dan faktor-faktor risikolain yang dapat mempengaruhinya. Penelitian ini menggunakan dataset yang diperoleh dari data pemeriksaan ibuhamil di Puskesmas Lawang dari bulan Januari 2021 sampai dengan Agustus 2021 dan menggunakan16 atribut penting, yaitu: tanggal pengkajian, nama, tanggal HPHT, tanggal HPL, umur, GPA, usia kehamilan, KSPR, keterangan KSPR, IMT, kategori IMT, reduksi, albumin, hepatitis, HIV, dan IMS. Penelitian ini telah melakukantahapan pengumpulan data, perancangan, implementasi, pengujian dan analisis data yang mengimplementasikanteknik machine learning K-Nearest Neighbor (KNN) . Nilai akurasi tertinggi pada skenario pengujian pertama dengan atribut k=2 adalah 70.27%. Nilai akurasi tertinggi pada skenario pengujian kedua dengan k=3 adalah 75.68% . Nilai akurasi tertinggi pada skenario pengujian ketiga dengan k=4 adalah 78.38%. Sedangkan Nilai akurasi tertinggi pada skenario pengujian keempat dengan k=5 adalah 77.03%. Nilai akurasi tertinggi dicapai padarasio sebesar 7:3 antara data latih dengan data testing.  \nKata kunci: akurasi, diabetes melitus, machine learning, kehamilan, KNN  \nMACHINE LEARNING IMPLEMENTATION IN DETECTING HIGH RISK OF DIABETES MELLITUS IN PREGNANCY  \nAbstract  \nDiabetes in pregnancy can increase various risks, both maternal and neonatal. Disorders of glucose homeostasis in pregnancy can increase the occurrence of congenital malformations, miscarriage, risk of preeclampsia, Cepalo Pelvic Dispropotion (CPD), premature birth, malformations, placenta previa and neonatal hypoglycemia. Thorough attention and treatment is needed for pregnancy with diabetes. Machine learning technology can be used to detect the risk of diabetes mellitus in pregnancy. Several data such as name, age, gestational age, gravida, para, past pregnancy history, past medical history, risk factors, and past birth history were used in the risk detection. Delivery process in pregnancy is affected by thephysiologic of prospective mother and several other risk factors. The dataset used in this study was 248 examination data of pregnancy check up at the Lawang Health Center from January 2021 to August 2021. The study used 16 attributes in determining the risk, i.e., date of assessment, name of mother, date of HPHT, date of HPL, age, GPA, gestational age, KSPR, KSPR information, BMI, BMI category, reduction, albumin, hepatitis, HIV, and STIs. This study has follow the implementation stages of data collection, design, implementation, testing and data analysis. The highest accuracy for th","cbCaimvveNRJQjaV","https://ap.wps.com/l/cbCaimvveNRJQjaV","pdf",626277,6,1,8,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang diabetes melitus pada kehamilan\n## Dampak komplikasi pada ibu dan kematian ibu\n## Tren prevalensi diabetes di Indonesia\n# Metode Penelitian\n## Dataset dan sumber data\n## Atribut penelitian\n## Tahapan implementasi dan pengujian\n# Hasil dan Analisis\n## Skenario pengujian KNN\n## Perbandingan akurasi tiap nilai k\n## Rasio data latih dan data testing","[{\"question\":\"Atribut apa saja yang digunakan untuk model, dan apa karakteristik jumlah atributnya?\",\"answer\":\"Model menggunakan 16 atribut penting, termasuk tanggal pengkajian, nama, tanggal HPHT, tanggal HPL, umur, GPA, usia kehamilan, KSPR serta informasi dan atribut klinis lain seperti IMT, reduksi, albumin, hepatitis, HIV, dan IMS.\"}]","Implementasi Machine Learning dalam Deteksi Risiko Tinggi Diabetes Melitus pada Kehamilan - KNN | PDF",1785895315,12,{"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":80,"head_meta":82,"extra_data":84,"updated_unix":29},"machine-learning-implementation-for-detecting-high-risk-of-diabetes-mellitus-in-pregnancy-knn","",{"@graph":37,"@context":79},[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/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/layanan-kesehatan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/machine-learning-implementation-for-detecting-high-risk-of-diabetes-mellitus-in-pregnancy-knn/124904/",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","2026-08-18","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73],{"name":74,"@type":75,"acceptedAnswer":76},"Atribut apa saja yang digunakan untuk model, dan apa karakteristik jumlah atributnya?","Question",{"text":77,"@type":78},"Model menggunakan 16 atribut penting, termasuk tanggal pengkajian, nama, tanggal HPHT, tanggal HPL, umur, GPA, usia kehamilan, KSPR serta informasi dan atribut klinis lain seperti IMT, reduksi, albumin, hepatitis, HIV, dan IMS.","Answer","https://schema.org",{"og:url":53,"og:type":81,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":83,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":86},[87,92,96,100,104,106,110,114,118,122,126],{"id":88,"doc_module":4,"doc_module_name":47,"category_name":89,"show_sort_weight":90,"slug":91},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":93,"doc_module":4,"doc_module_name":47,"category_name":94,"show_sort_weight":90,"slug":95},48,"Cerita & Novel","story-novel",{"id":97,"doc_module":4,"doc_module_name":47,"category_name":98,"show_sort_weight":90,"slug":99},56,"Gaya Hidup","lifestyle",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":90,"slug":103},51,"Komik","comic",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":90,"slug":105},"healthcare",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":90,"slug":109},54,"Penelitian & Laporan","research-report",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":90,"slug":113},49,"Sastra","literature",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":90,"slug":117},52,"Teknologi","technology",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":90,"slug":121},50,"Ujian","exam",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":90,"slug":125},57,"Umum","general",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":4,"slug":129},181,"Formulir","formulir"]