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Manajemen yang efektif menuntut deteksi dini dan intervensi tepat waktu. Penelitian ini mengembangkan model prediksi menggunakan Random Forest, Regresi Logistik, dan Decision Tree dengan dataset Pima Indians Diabetes (768 data) melalui normalisasi MinMax Scaler serta validasi cross-validation. Kinerja diukur memakai akurasi, presisi, recall, F1-score, dan analisis ROC; Logistic Regression unggul pada akurasi, Random Forest pada AUC, dan Decision Tree pada recall.","Prediksi Penyakit Diabetes Melitus Menggunakan Algoritma  \nMachine Learning  \nEgi Safitria1, Dani Rofiantob2, Neni Purwatic3, Hendra Kurniawana4, Sri Karnilaa5 aDepartemen Sains Data,Fakultas Ilmu Komputer Institut Informatika dan Bisnis darmajaya Jl. ZA Pagar Alam No.93 Labuhan Ratu Bandar Lampung  \n[1](1 egisafitri@darmajaya.ac.id)[ egisafitri@darmajaya.ac.id](1 egisafitri@darmajaya.ac.id)  \n[4](4 hendrakurniawan@darmajaya.ac.id)[ hendrakurniawan@darmajaya.ac.id](4 hendrakurniawan@darmajaya.ac.id)  \n[5](5 srikarnila@darmajaya.ac.id)[ srikarnila@darmajaya.ac.id](5 srikarnila@darmajaya.ac.id)  \nbDepartemen Teknologi Rekayasa Perangkat Lunak, Fakultas Teknologi Informasi, Politeknik Negeri Lampung Jl. Soekarno Hatta No.10, Rajabasa Raya, Bandar Lampung  \n[2](2 danirofianto@polinela.ac.id)[ danirofianto@polinela.ac.id](2 danirofianto@polinela.ac.id)  \nbDepartemen Sistem Informasi, Fakultas Sains dan Teknologi, Universitas YPPI rembang Jl. Raya Rembang-PamotanKM 4 Rembang  \n[3](3 nenipurwati@uyr.ac.id)[ nenipurwati@uyr.ac.id](3 nenipurwati@uyr.ac.id)  \nAbstrak  \nDiabetes melitus adalah penyakit kronis dengan prevalensi global yang meningkat pesat, mempengaruhi sekitar 422 juta orang, terutama di negara-negara berpenghasilan rendah dan menengah. Manajemen diabetes yang efektif memerlukan deteksi dini dan intervensi yang tepat waktu. Penelitian ini bertujuan untuk mengembangkan model prediksi yang akurat untuk diabetes melitus menggunakan tiga algoritma machine learning: Random Forest, Regresi Logistik, dan Decision Tree. Dataset Pima Indians Diabetes, yang terdiri dari 768 catatan pasiendengan berbagai indikator kesehatan, digunakan untuk pelatihan dan evaluasi model. Analisis data eksploratif menunjukkan korelasi yang signifikan antara kadar glukosa, BMI, usia, dan risiko diabetes. Dataset dibagi menjadi 80% data pelatihan (614 data) dan 20% data pengujian (154 data) . Data dinormalisasi menggunakan metode MinMax Scaleruntuk memastikan semua fitur berada pada skala yang sama. Model divalidasi menggunakan metode cross-validation dan dievaluasi berdasarkan akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Regresi Logistik mencapai akurasi tertinggi (75%) dan kinerja yang seimbang dalam mengidentifikasikasus positif dan negatif. Decision Tree unggul dalam recall, sementara Random Forest menunjukkan keseimbangan yang sedikit lebih rendah antara presisi dan recall. Analisis kurva ROC mengungkapkan bahwa Random Forest memiliki AUC tertinggi (0.82), diikuti oleh Regresi Logistik (0.81) dan Decision Tree (0.73) . Penelitian ini menegaskan bahwa algoritma machine learning dapat secara efektif memprediksi diabetes, menyediakan alat yang berharga untuk deteksi dini dan intervensi, yang pada akhirnya dapat mengurangi beban global diabetes melitus.  \nKata kunci: Diabetes; Random Forest; Regresi Logistik; Decision Tree   \nDiabetes Mellitus Disease Prediction using Machine Learning  \nAlgorithms  \nAbstract  \nDiabetes mellitus is a chronic disease with a rapidly increasing global prevalence, affecting around 422 million people, predominantly in low- and middle-income countries. Effective management of diabetes requires early detection and timely intervention. This study aims to develop an accurate predictive model for diabetes mellitus using three machine learning algorithms: Random Forest, Logistic Regression, and Decision Tree. The Pima Indians Diabetes dataset, comprising 768 patient records with various health indicators, was utilized for model training and evaluation. Exploratory data analysis revealed significant correlations between glucose levels, BMI, age, and diabetes risk. The dataset was divided into 80% training data (614 data) and 20% testing data (154 data) . The data was normalized using the Min-Max Scaler method to ensure all features were on the same scale. The model was validated using the cross-validation method and evaluated based on accuracy, precision, recall, and F1-score. Results indicated that Lo","cbCaijzqlaU07tro","https://ap.wps.com/l/cbCaijzqlaU07tro","pdf",591921,5,1,7,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang dan urgensi\n## Tujuan penelitian\n# Metodologi\n## Dataset dan pembagian data\n## Pra-pemrosesan dan normalisasi\n## Model dan validasi\n## Metode evaluasi\n# Hasil dan Pembahasan\n## Perbandingan akurasi dan metrik\n## Analisis kurva ROC\n# Kesimpulan","[{\"question\":\"Mengapa deteksi dini diabetes melitus penting?\",\"answer\":\"Deteksi dini diperlukan agar intervensi dapat dilakukan lebih cepat dan akurat, sehingga risiko komplikasi serta beban sistem kesehatan dapat ditekan.\"},{\"question\":\"Algoritma machine learning apa saja yang digunakan untuk prediksi?\",\"answer\":\"Penelitian menggunakan tiga algoritma: Random Forest, Regresi Logistik, dan Decision Tree.\"},{\"question\":\"Bagaimana model dievaluasi dalam penelitian ini?\",\"answer\":\"Model divalidasi menggunakan cross-validation dan dinilai melalui akurasi, presisi, recall, F1-score, serta analisis kurva ROC (AUC).\"},{\"question\":\"Model mana yang menunjukkan performa terbaik berdasarkan metrik tertentu?\",\"answer\":\"Regresi Logistik mencapai akurasi tertinggi (75%), Random Forest memperoleh AUC tertinggi (0.82), sedangkan Decision Tree unggul dalam recall.\"}]","Prediksi Penyakit Diabetes Melitus - Menggunakan Algoritma Machine Learning | PDF",1785728221,11,{"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":92,"head_meta":94,"extra_data":96,"updated_unix":29},"diabetes-mellitus-disease-prediction-using-machine-learning-algorithms","",{"@graph":37,"@context":91},[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/diabetes-mellitus-disease-prediction-using-machine-learning-algorithms/120105/",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","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83,87],{"name":74,"@type":75,"acceptedAnswer":76},"Mengapa deteksi dini diabetes melitus penting?","Question",{"text":77,"@type":78},"Deteksi dini diperlukan agar intervensi dapat dilakukan lebih cepat dan akurat, sehingga risiko komplikasi serta beban sistem kesehatan dapat ditekan.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Algoritma machine learning apa saja yang digunakan untuk prediksi?",{"text":82,"@type":78},"Penelitian menggunakan tiga algoritma: Random Forest, Regresi Logistik, dan Decision Tree.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana model dievaluasi dalam penelitian ini?",{"text":86,"@type":78},"Model divalidasi menggunakan cross-validation dan dinilai melalui akurasi, presisi, recall, F1-score, serta analisis kurva ROC (AUC).",{"name":88,"@type":75,"acceptedAnswer":89},"Model mana yang menunjukkan performa terbaik berdasarkan metrik tertentu?",{"text":90,"@type":78},"Regresi Logistik mencapai akurasi tertinggi (75%), Random Forest memperoleh AUC tertinggi (0.82), sedangkan Decision Tree unggul dalam recall.","https://schema.org",{"og:url":53,"og:type":93,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":95,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":98},[99,104,108,112,116,118,122,126,130,134,138],{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":102,"slug":103},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":102,"slug":107},48,"Cerita & Novel","story-novel",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":102,"slug":111},56,"Gaya Hidup","lifestyle",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":102,"slug":115},51,"Komik","comic",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":102,"slug":117},"healthcare",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":102,"slug":121},54,"Penelitian & Laporan","research-report",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":102,"slug":125},49,"Sastra","literature",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":102,"slug":129},52,"Teknologi","technology",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":102,"slug":133},50,"Ujian","exam",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":102,"slug":137},57,"Umum","general",{"id":139,"doc_module":4,"doc_module_name":47,"category_name":140,"show_sort_weight":4,"slug":141},181,"Formulir","formulir"]