[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117450-id":3,"doc-seo-117450-113":31,"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":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},117450,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",54,"Penelitian & Laporan","Analisis Komparatif Algoritma Klasifikasi Machine Learning untuk Memprediksi Diabetes - Comparative Study","Diabetes Mellitus merupakan penyakit kronis yang umum dan menjadi perhatian besar dalam isu kesehatan masyarakat global. Penelitian ini menerapkan pendekatan Machine Learning untuk membantu memprediksi diabetes pada masyarakat dengan menggunakan studi komparatif beberapa algoritma klasifikasi, yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, dan Decision Tree (C4.5). Fitur prediksi meliputi jenis kelamin, usia, riwayat hipertensi, riwayat penyakit jantung, riwayat merokok, BMI, HbA1c, serta kadar glukosa darah. Hasil uji menunjukkan akurasi tertinggi pada SVM sebesar 94,5%.","Algoritma: Jurnal Ilmu Komputer Informatika e-ISSN 2598-6341  \nVolume: 9, Nomor: 1, April 2025: 26-35  \nDOI: 10.30829/algoritma.v9i1.23794  \n[https://jurnal.uinsu.ac.id/index.php/algoritma](https://jurnal.uinsu.ac.id/index.php/algoritma)  \nAnalisis KomparatifAlgoritma Klasifikasi Machine Learning untuk Memprediksi Diabetes  \nComparative Analysis of Classification Algorithms in Machine Learning for Predicting  \nDiabetes  \nAlfa Saleh*1, Ria Eka Sari2, Ramadani3, Fujiati4, Ratna Lestari5 1Informatika, Universitas Samudra  \n2Sistem Informasi, Universitas Potensi Utama  \n3Informatika, Universitas Muhammadiyah Asahan  \n4Kewirausahaan, Universitas Satya Terra Bhinneka  \n5Biologi, Universitas Samudra  \n[E-mail:](E-mail:1alfasaleh@unsam.ac.id)[1](E-mail:1alfasaleh@unsam.ac.id)[alfasaleh@unsam.ac.id](E-mail:1alfasaleh@unsam.ac.id), [2](2ladiespure@gmail.com)[ladiespure@gmail.com](2ladiespure@gmail.com), [3](3ramadans.ordinary@gmail.com)[ramadans.ordinary@gmail.com](3ramadans.ordinary@gmail.com), [4](4fujiati@satyaterrabhinneka.ac.id)[fujiati@satyaterrabhinneka.ac.id](4fujiati@satyaterrabhinneka.ac.id), [5](5ratnalestari@unsam.ac.id)[ratnalestari@unsam.ac.id](5ratnalestari@unsam.ac.id)  \nAbstrak  \nDiabetes Mellitus merupakan salah satu penyakit kronis yang paling umum dijumpai, penyakit ini juga menjadi perhatian utama dalam isu kesehatan masyarakat secara global. pada penelitian ini, dilakukan pendekatan Machine Learning untuk membantu memprediksi penyakit diabetes dimasyarakat. Machine learning sangat berguna dalam menganalisis data kesehatan sebab kemampuannya yang baik untuk mengolah data dalam jumlah yang besar. Studi komparatif dengan beberapa algoritma klasifikasi machine learning seperti K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes dan Decision Tree (C4.5) telah dilakukan untukmenentukan algoritma mana yang memberikan hasil terbaik dalam hal memprediksi penyakit diabetes. Dimana, fitur yang digunakan dalam memprediksi penyakit diabetes meliputi jenis kelamin, usia, riwayat hipertensi, riwayat penyakit jantung, riwayat merokok, BMI, level dari HbA1c dan kadar glukosa dalam darah. Dari hasil penelitian ini, diperoleh tingkat akurasi prediksipenyakit diabetes untuk algoritma K-Nearest Neighbors (KNN) sebesar 92,5 %, selanjutnya algoritma Support Vector Machine (SVM) sebesar 94,5%, kemudian algoritma Naive Bayes sebesar 90% dan yang terakhir algoritma Decision Tree (C4.5) sebesar 93,5% . Sehingga, dari hasil pengujian beberapa algoritma klasifikasi machine learning tersebut dapat disimpulkan bahwa Algoritma Support Vector Machine (SVM) merupkan algoritma yang paling optimal dalam hal memprediksi penyakit diabetes.  \nKata kunci: Machine Learning, KNN, SVM, Naive Bayes, Decision Tree, Diabetes  \nAbstract  \nDiabetes Mellitus is one of the most common chronic diseases, this disease is also a major concern in global public health issues. in this study, a Machine Learning approach was carried out to help predict diabetes in the community. Machine learning is very useful in analyzing health data because of its good ability to process large amounts of data. A comparative study with several machine learning classification algorithms such asK-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes and Decision Tree (C4.5) has been conducted to determine which algorithm gives the best results in terms of predicting diabetes. Where, the features used in predicting diabetes include gender, age, history of hypertension, history of heart disease, 26  \n© 2025 The Author(s). Published by ALGORITMA JOURNAL. This is an open access article under the CC BY-SA license  \n([http://creativecommons.org/licenses/by-sa/4.0/](http://creativecommons.org/licenses/by-sa/4.0/))  \nAlgoritma: Jurnal Ilmu Komputer Informatika e-ISSN 2598-6341  \nVolume: 9, Nomor: 1, April 2025: 26-35  \nDOI: 10.30829/algoritma.v9i1.23794  \n[https://jurnal.uinsu.ac.id/index.php/algoritma](https://jurnal.uinsu.ac.id/index.php/algoritma)  \nhistory of smoki","cbCaivlIGPzmpy2H","https://ap.wps.com/l/cbCaivlIGPzmpy2H","pdf",385344,4,1,10,"Indonesian","id",113,"# Abstrak\n# Kata kunci\n# PENDAHULUAN\n## Latar belakang diabetes dan urgensi prediksi\n## Peran Machine Learning dalam analisis data kesehatan\n## Fokus algoritma klasifikasi yang digunakan","[{\"question\":\"Algoritma klasifikasi apa saja yang dibandingkan untuk memprediksi diabetes?\",\"answer\":\"Penelitian membandingkan K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, dan Decision Tree (C4.5).\"},{\"question\":\"Fitur apa yang digunakan dalam proses prediksi diabetes?\",\"answer\":\"Fitur yang digunakan meliputi jenis kelamin, usia, riwayat hipertensi, riwayat penyakit jantung, riwayat merokok, BMI, level HbA1c, dan kadar glukosa darah.\"},{\"question\":\"Algoritma mana yang paling optimal berdasarkan hasil akurasi?\",\"answer\":\"Algoritma Support Vector Machine (SVM) memberikan akurasi tertinggi sebesar 94,5%.\"}]","Analisis Komparatif Algoritma Klasifikasi Machine Learning untuk Memprediksi Diabetes - 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