[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120526-id":3,"doc-seo-120526-113":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},120526,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",54,"Penelitian & Laporan","Komparasi Model pada Machine Learning untuk Prediksi Tingkat Kanker Paru - Analisis Perbandingan Algoritma","Studi ini bertujuan membandingkan kinerja empat algoritma machine learning—Random Forest, Support Vector Machine, Logistic Regression, dan K-Nearest Neighbors—dalam memprediksi tingkat keparahan kanker paru berbasis data medis pasien. Dataset memuat informasi klinis dengan target tiga kelas: rendah, sedang, tinggi, menggunakan skema train-test split 80:20 tanpa feature scaling. Hasil menunjukkan akurasi RF 100%, LR 99%, KNN 82%, dan SVM 43%, dengan metrik recall, precision, serta F1-score mempertegas dominasi RF dan LR. Temuan merekomendasikan RF sebagai model utama dan LR sebagai pengontrol pada sistem pendukung diagnosis klinis untuk mendukung terapi dini yang lebih personalisasi dan peningkatan prognosis.","Komparasi Model pada Machine Learning untuk Prediksi  \nTingkat Kanker Paru  \nComparison ofMachine Learning Models for Predicting Lung Cancer  \nSeverity  \n1Ninik Lestari, 2Erliyan Redy Susanto*  \n1,2Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia  \n1,2Jl. Z.A. Pagar Alam No. 9-11 Labuhan Ratu, Kota Bandar Lampung, Provinsi Lampung, Indonesia  \n*e-mail: [erliyan.redy@teknokrat.ac.id](erliyan.redy@teknokrat.ac.id)  \n(received: 30 April 2025, revised: 30 July 2025?, accepted: 31 July 2025)  \nAbstrak  \nStudi ini bertujuan untuk membandingkan kinerja empat algoritma machine learning, yaitu Random Forest, Support Vector Machine (SVM), Logistic Regression, dan K-Nearest Neighbors (KNN), dalam memprediksi tingkat keparahan kanker paru berdasarkan data medis pasien. Dataset yang digunakan mencakup informasi medis dari pasien dengan variabel target berupa tingkat keparahankanker (rendah, sedang, tinggi) . Eksperimen dilakukan dengan train-test split 80:20 tanpa feature scaling. Hasil menunjukkan RF mencapai akurasi 100 %, LR 99%, KNN 82%, dan SVM 43% . Keunggulan Random Forest berasal dari ensemble pohon keputusan yang menekan overfitting padafitur numerik berdimensi menengah, sementara SVM (kernel = RBF, C = 1.0, gamma = ‘scale’) gagalmenyesuaikan diri karena tidak adanya penskalaan dan tuning hiper-parameter. Recall, precision, dan F1-score mengonfirmasi dominasi RF dan LR. Studi ini memberikan wawasan tentang efektivitasalgoritma machine learning dalam diagnosis kanker paru dan kontribusi penggunaan pendekatan multi-algoritma. Hasil studi menyarankan penggunaan RF sebagai model utama dan LR sebagai pengontrol dalam sistem pendukung diagnosis klinis, memungkinkan dokter menentukan terapi dini yang lebih personalisasi dan meningkatkan prognosis pasien kanker paru.  \nKata Kunci: prediksi kanker paru, machine learning, random forest, diagnosa dini  \nAbstract  \nThis study aims to compare the performance of four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN)—in predicting lung cancer severity based on patient medical data. The dataset includes clinical information with the target variable categorized into three severity levels: low, medium, and high. Experiments were conducted using an 80:20 train-test split without feature scaling. The results show that RF achieved 100% accuracy, LR 99%, KNN 82%, and SVM 43%. The superior performance of Random Forest can be attributed to its ensemble of decision trees, which mitigates overfitting in medium-dimensional numerical features, whereas SVM (kernel = RBF, C = 1.0, gamma = \"scale\") failed to adapt due to the absence of scaling and hyperparameter tuning. Recall, precision, and F1-score further confirm the dominance of RF and LR. This study provides insights into the effectiveness of machine learning algorithms in lung cancer diagnosis and highlights the contribution of a multialgorithm approach. The findings recommend using RF as the primary model and LR as a complementary control within clinical decision support systems, enabling physicians to make earlier, more personalized treatment decisions and ultimately improve lung cancer patient prognosis. Keywords: lung cancer prediction, machine learning, random forest, early diagnosis  \n1 Pendahuluan  \nKanker merupakan salah satu penyakit yang paling mematikan di dunia saat ini[1] . Menurut World Health Organization WHO (2018), penyebab utama kedua kematian di dunia dengan jumlahkematian 9,6 juta kematian adalah kanker[2] . Menurut perkiraan American Cancer Society untukkanker paru di Amerika Serikat pada tahun 2022 adalah sekitar 236.740 kasus baru kanker parudimana 117.910 pada pria dan 118.830 pada perempuan dan sekitar 130.180 kematian akibat kanker  \nparu dimana 68.820 pada pria dan 61.360 pada perempuan. Beberapa faktor resiko yang dapat menjadi penyebab kanker paru pada orang yang tidak merokok diantaranya asbestos, radon dan polusiudara[3] . ","cbCait7tN24PgW3j","https://ap.wps.com/l/cbCait7tN24PgW3j","pdf",644798,3,1,14,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang dan tantangan diagnosis dini\n## Potensi machine learning untuk diagnosis\n# Tujuan studi","[{\"question\":\"Studi ini membandingkan algoritma machine learning apa saja untuk memprediksi kanker paru?\",\"answer\":\"Empat algoritma yang dibandingkan adalah Random Forest, Support Vector Machine (SVM), Logistic Regression (LR), dan K-Nearest Neighbors (KNN).\"},{\"question\":\"Bagaimana skema eksperimen dilakukan dalam studi ini?\",\"answer\":\"Eksperimen menggunakan train-test split 80:20 dan tidak memakai feature scaling. Model dievaluasi menggunakan akurasi, recall, precision, dan F1-score.\"},{\"question\":\"Algoritma mana yang menunjukkan kinerja terbaik dan bagaimana rekomendasinya?\",\"answer\":\"Random Forest menjadi yang terbaik dengan akurasi 100%, disusul Logistic Regression. Studi merekomendasikan RF sebagai model utama dan LR sebagai pengontrol dalam sistem pendukung diagnosis klinis untuk membantu keputusan terapi dini yang lebih personal.\"}]","Komparasi Model pada Machine Learning untuk Prediksi Tingkat Kanker Paru - Analisis Perbandingan Algoritma | PDF",1785730503,22,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"comparison-of-machine-learning-models-for-predicting-lung-cancer-severity-algorithm-comparison-analysis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/comparison-of-machine-learning-models-for-predicting-lung-cancer-severity-algorithm-comparison-analysis/120526/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-18","2026-08-03",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},"Studi ini membandingkan algoritma machine learning apa saja untuk memprediksi kanker paru?","Question",{"text":76,"@type":77},"Empat algoritma yang dibandingkan adalah Random Forest, Support Vector Machine (SVM), Logistic Regression (LR), dan K-Nearest Neighbors (KNN).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana skema eksperimen dilakukan dalam studi ini?",{"text":81,"@type":77},"Eksperimen menggunakan train-test split 80:20 dan tidak memakai feature scaling. Model dievaluasi menggunakan akurasi, recall, precision, dan F1-score.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma mana yang menunjukkan kinerja terbaik dan bagaimana rekomendasinya?",{"text":85,"@type":77},"Random Forest menjadi yang terbaik dengan akurasi 100%, disusul Logistic Regression. Studi merekomendasikan RF sebagai model utama dan LR sebagai pengontrol dalam sistem pendukung diagnosis klinis untuk membantu keputusan terapi dini yang lebih personal.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]