[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123069-id":3,"doc-seo-123069-113":31,"detail-sidebar-cat-0-id-113":96},{"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},123069,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",54,"Penelitian & Laporan","OPTIMASI AKURASI MODEL PEMBELAJARAN MESIN UNTUK KLASIFIKASI TUMOR OTAK DENGAN ANALISIS KOMPONEN UTAMA","Masalah utama klasifikasi tumor otak terletak pada ketepatan dan kecepatan diagnosis berbasis citra medis. Penelitian ini bertujuan meningkatkan akurasi model pembelajaran mesin melalui Principal Component Analysis (PCA) untuk reduksi dimensi. Prosedur meliputi preprocessing gambar, penskalaan fitur, penerapan PCA, lalu pengujian beberapa algoritma seperti Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Naive Bayes. Dataset berisi 3.264 citra. Hasil menunjukkan PCA meningkatkan akurasi SVM (81%→83%) dan KNN (68%→71%) tetapi menurunkan akurasi Logistic Regression (77%→69%) dan Naive Bayes (49%→42%). Evaluasi menggunakan Confusion Matrix dan AUC-ROC, serta mendorong pemilihan algoritma dan metode preprocessing yang sesuai karakter data.","OPTIMIZATION OF MACHINE LEARNING MODEL ACCURACY FOR BRAIN TUMOR CLASSIFICATION WITH PRINCIPAL COMPONENT ANALYSIS  \nIndra Maulana1, Amril Mutoi Siregar*2, Rahmat3, Ahmad Fauzi4  \n1,2,3,4Informatic Departement, Faculty Of Computer Science, Universitas Buana Perjuangan Karawang, Indonesia  \n[Email:](Email:1If20.indramaulana@mhs.ubpkarawang.ac.id)[1](Email:1If20.indramaulana@mhs.ubpkarawang.ac.id)[If20.indramaulana@mhs.ubpkarawang.ac.id](Email:1If20.indramaulana@mhs.ubpkarawang.ac.id), [2](2amrilmutoi@ubpkarawang.ac.id)[amrilmutoi@ubpkarawang.ac.id](2amrilmutoi@ubpkarawang.ac.id), [3](3rahmat@ubpkarawang.ac.id)[rahmat@ubpkarawang.ac.id](3rahmat@ubpkarawang.ac.id), [4](4ahmadfauzi@ubpkarawang.ac.id)[ahmadfauzi@ubpkarawang.ac.id](4ahmadfauzi@ubpkarawang.ac.id)  \n(Article received: May 07, 2024; Revision: May 30, 2024; published: June 11, 2024)  \nAbstract  \nThe main issue in brain tumor classification is the accuracy and speed of diagnosis through medical imaging. This study aims to improve the accuracy of machine learning models for brain tumor classification by using Principal Component Analysis (PCA) for dimensionality reduction. The research methods include image preprocessing, feature scaling, PCA application, and the implementation of machine learning algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes. The dataset consists of 3,264 images divided into training and testing sets. The results show that the use of PCA has varying impacts on different algorithms. PCA increases the accuracy of the SVM algorithm from 81% to 83% and KNN from 68% to 71%, but decreases the accuracy of Logistic Regression from 77% to 69% and Naive Bayes from 49% to 42%. Evaluation is performed using the Confusion Matrix and AUC-ROC to measure model performance. In conclusion, selecting the appropriate algorithm and preprocessing method is crucial in medical image classification, and the use of PCA should be considered based on the characteristics of the data and the algorithms used. This study also encourages the exploration of alternative dimensionality reduction methods for medical image analysis.  \nKeywords: Brain tumor classification, Component Analysis, KNN, Machine learning, Medical image, Naïve Bayes, Principal, Random Forest, SVM.  \nOPTIMASI AKURASI MODEL PEMBELAJARAN MESIN UNTUK KLASIFIKASI TUMOR OTAK DENGAN ANALISIS KOMPONEN UTAMA  \nAbstrak  \nMasalah utama dalam klasifikasi tumor otak adalah ketepatan dan kecepatan diagnosis menggunakan citra medis. Penelitian ini bertujuan meningkatkan akurasi model pembelajaran mesin untuk klasifikasi tumor otak dengan menggunakan Principal Component Analysis (PCA) untuk reduksi dimensi. Metode penelitian meliputi preprocessing gambar, scaling fitur, penerapan PCA, dan implementasi algoritma pembelajaran mesin seperti Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Naive Bayes. Dataset terdiri dari 3264 gambar yang dibagi menjadi set pelatihan dan pengujian. Hasil menunjukkan bahwa penggunaan PCA memiliki dampak berbeda pada setiap algoritma. PCA meningkatkan akurasi SVM dari 81% menjadi 83% dan KNN dari 68% menjadi 71%, tetapi menurunkan akurasi Logistic Regression dari 77% menjadi 69% dan Naive Bayes dari 49% menjadi 42% . Evaluasi dilakukan menggunakan Confusion Matrix dan AUCROC untuk mengukur kinerja model. Kesimpulannya, pemilihan algoritma dan metode preprocessing yang tepat sangat penting dalam klasifikasi citra medis, dan penggunaan PCA harus dipertimbangkan berdasarkankarakteristik data dan algoritma yang digunakan. Penelitian ini juga mendorong eksplorasi metode reduksi dimensi alternatifuntuk analisis citra medis.  \nKata kunci: Citra medis, Klasifikasi tumor otak, KNN, Naive Bayes, Pembelajaran mesin, Principal Component Analysis, Random Forest, SVM.  \n1. PENDAHULUAN  \nTumor adalah massa jaringan ekstra seluleryang mengarah pada pembentukan tumor. Mungkin  \nada tumor jinak ata","cbCaihK5smybneXN","https://ap.wps.com/l/cbCaihK5smybneXN","pdf",1161984,4,1,13,"Indonesian","id",113,"# PENDAHULUAN\n## Masalah klasifikasi tumor otak dan kebutuhan akurasi\n## Deteksi tumor otak dengan CT scan dan MRI\n## Metode yang diusulkan dan ruang lingkup penelitian","[{\"question\":\"Penelitian ini bertujuan meningkatkan apa pada klasifikasi tumor otak?\",\"answer\":\"Penelitian ini bertujuan meningkatkan akurasi model pembelajaran mesin untuk klasifikasi tumor otak dengan bantuan PCA untuk reduksi dimensi.\"},{\"question\":\"Bagaimana PCA diterapkan dalam alur metode penelitian?\",\"answer\":\"Metode mencakup preprocessing gambar, scaling fitur, penerapan PCA, lalu menjalankan algoritma pembelajaran mesin seperti Logistic Regression, Random Forest, SVM, KNN, dan Naive Bayes.\"},{\"question\":\"Bagaimana pengaruh PCA terhadap akurasi tiap algoritma?\",\"answer\":\"PCA menaikkan akurasi SVM (81% ke 83%) dan KNN (68% ke 71%), tetapi menurunkan akurasi Logistic Regression (77% ke 69%) dan Naive Bayes (49% ke 42%).\"},{\"question\":\"Bagaimana kinerja model dievaluasi?\",\"answer\":\"Evaluasi dilakukan menggunakan Confusion Matrix dan AUC-ROC untuk mengukur performa model.\"}]","OPTIMASI AKURASI MODEL PEMBELAJARAN MESIN UNTUK KLASIFIKASI TUMOR OTAK DENGAN ANALISIS KOMPONEN UTAMA | 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ini bertujuan meningkatkan apa pada klasifikasi tumor otak?","Question",{"text":76,"@type":77},"Penelitian ini bertujuan meningkatkan akurasi model pembelajaran mesin untuk klasifikasi tumor otak dengan bantuan PCA untuk reduksi dimensi.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana PCA diterapkan dalam alur metode penelitian?",{"text":81,"@type":77},"Metode mencakup preprocessing gambar, scaling fitur, penerapan PCA, lalu menjalankan algoritma pembelajaran mesin seperti Logistic Regression, Random Forest, SVM, KNN, dan Naive Bayes.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana pengaruh PCA terhadap akurasi tiap algoritma?",{"text":85,"@type":77},"PCA menaikkan akurasi SVM (81% ke 83%) dan KNN (68% ke 71%), tetapi menurunkan akurasi Logistic Regression (77% ke 69%) dan Naive Bayes (49% ke 42%).",{"name":87,"@type":74,"acceptedAnswer":88},"Bagaimana kinerja model dievaluasi?",{"text":89,"@type":77},"Evaluasi dilakukan menggunakan Confusion Matrix dan AUC-ROC untuk mengukur performa model.","https://schema.org",{"og:url":53,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,103,107,111,115,119,121,125,129,133,137],{"id":99,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":101,"slug":106},48,"Cerita & Novel","story-novel",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":101,"slug":110},56,"Gaya Hidup","lifestyle",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":101,"slug":114},51,"Komik","comic",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":101,"slug":118},53,"Layanan 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