[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120616-en":3,"doc-seo-120616-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120616,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","MRI Classification of Brain Tumors Using EfficientNetB0 Feature Extraction and Machine Learning Methods","Brain tumor classification based on MRI images plays a central role in modern diagnostics by enabling faster, more accurate disease detection support. The study introduces a workflow that extracts image features with EfficientNet B0 and then applies conventional machine learning models for classification. After preprocessing and resizing to EfficientNet B0 input dimensions, feature vectors undergo PCA for dimensionality reduction and SMOTE for class balancing. Results indicate strong performance, with Support Vector Machine reaching about 96% accuracy and XGBoost/LightGBM around 94%.","MRI Classification of Brain Tumors Using EfficientNetB0 Feature Extraction and Machine Learning Methods  \nFirza Findia Jiven 1*, Rumini 2*  \n*Informatika, Universitas Amikom Yogyakarta  \n[firzafj@students.amikom.ac.id](firzafj@students.amikom.ac.id1)[1](firzafj@students.amikom.ac.id1), [rumini@amikom.ac.id](rumini@amikom.ac.id2)[2](rumini@amikom.ac.id2)  \nArticle history:  \nReceived 2025-07-21 Revised 2025-11-17 Accepted 2025-11-22  \nKeyword:  \nBrain Tumor, Feature Extraction, Machine Learning, MRI Classification  \nBrain tumor classification using MRI images plays a crucial role in modern medical diagnostics, offering fast and accurate support for disease detection. This study proposes a classification approach that combines feature extraction using EfficientNet B0 with conventional machine learning algorithms. MRI brain images are preprocessed and resized to match EfficientNet B0 input dimensions. Feature vectors are extracted and subsequently processed using PCA for dimensionality reduction and SMOTE for class balancing. The resulting data are classified using various machine learning algorithms including Support Vector Machine, XGBoost, LightGBM, and others. Experimental results show that Support Vector Machine achieved the highest accuracy of 96%, followed by XGBoost and LightGBM at 94% . The combination of EfficientNet B0 feature extraction and lightweight classifiers proved to be effective, matching the performance of more complex deep learning models. This study does not focus on measuring computational cost directly, but rather demonstrates that combining EfficientNetB0 feature extraction with machine learning algorithms can achieve performance comparable to deep learning approaches. This highlights that lightweight models remain competitive in terms of accuracy without requiring highly complex architectures. Future work can explore this method on other medical imaging datasets and enhance model interpretability for clinical adoption.  \nThis is an open access article under the CC–BY-SA license.  \nArticle Info ABSTRACT  \nI. PENDAHULUAN  \nKlasifikasi citra medis dalam deteksi tumor otak menggunakan citra MRI, mempunyai peran penting dalam dunia kedokteran modern, karena dapat mendukung prosesdiagnosa yang lebih cepat dan akurat serta membantu perencanaan pengobatan. Perkembangan teknologi pemrosesan citra sangat pesat, dengan penerapan teknik deep learning seperti Convolution Neural Networks (CNN) yang telah terbukti efektif dalam analisis citra medis[1] . Model CNN yang populer seperti ResNet50 dan VGG16 telahdigunakan untuk mendeteksi berbagai jenis kelainan dalamgambar medis[2] . Meskipun akurasi tinggi, metode ini sering memerlukan komputasi yang sangat besar, hal ini bisamenjadi tantangan dalam penerapan klinis yang memerlukanefisiensi dan kecepatan[3] . Oleh karena itu, penting untuk terus mengembangkan penelitian yang bertujuan  \nmeningkatkan efisiensi dalam klasifikasi citra medis dengan mempertahankan akurasi yang optimal[4] .  \nDalam studi yang dilakukan oleh sen dkk., penerapan CNN dengan arsitektur ResNet50 dan MobileNetV2 berhasil mencapai akurasi sekitar 96% pada klasifikasi tumor otak berbasis MRI. Walaupun performanya cukup baik, keduaarsitektur tersebut memiliki tingkat kompleksitas yang tinggi karena jumlah parameternya besar dan membutuhkan komputasi intensif. Di sisi lain, EfficientNetB0 mampu menghasilkan akurasi hingga 97% dengan jumlah parameter lebih kecil, menjadikannya lebih efisien namun tetap bersaing[5] . Pertimbangan ini menjadi dasar penggunaan EfficientNetB0 dalam penelitian ini.  \nMeskipun Penerapan deep learning dalam klasifikasi citramedis terbukti efektif, namun masih terdapat celah dalam penggunaan model EfficientNet untuk citra MRI tumor otak. Banyak penelitian yang hanya memanfaatkan EfficientNet  \nuntuk ekstraksi fitur, kemudian melanjukan proses klasifikasidengan menggunkan deep learning seperti CNN[3] . Namun, belum banyak yang meneliti penerapan EfficientNet B0 untuk ekstraksi f","cbCaigl8rbIA8alA","https://ap.wps.com/l/cbCaigl8rbIA8alA","pdf",890190,1,11,"English","en",105,"# PENDAHULUAN\n## Latar Belakang dan Motivasi Penelitian\n## Gap Penelitian dalam Penggunaan EfficientNet untuk Ekstraksi Fitur\n## Tujuan Pendekatan yang Diusulkan","[{\"question\":\"Pendekatan apa yang diusulkan untuk klasifikasi tumor otak berbasis MRI?\",\"answer\":\"Pendekatan menggabungkan ekstraksi fitur menggunakan EfficientNet B0 dengan algoritma machine learning untuk klasifikasi.\"},{\"question\":\"Langkah apa saja yang dilakukan pada data sebelum klasifikasi?\",\"answer\":\"Citra MRI dipreproses dan diubah ukurannya sesuai input EfficientNet B0, lalu diekstraksi menjadi vektor fitur. Selanjutnya PCA dipakai untuk reduksi dimensi dan SMOTE untuk menyeimbangkan kelas.\"},{\"question\":\"Algoritma mana yang menghasilkan akurasi tertinggi dan berapa nilainya?\",\"answer\":\"Support Vector Machine (SVM) mencapai akurasi tertinggi sekitar 96%, sedangkan XGBoost dan LightGBM sekitar 94%.\"}]","MRI Classification of Brain Tumors Using EfficientNetB0 Feature Extraction and Machine Learning Methods | PDF",1785730915,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mri-classification-of-brain-tumors-using-efficientnetb0-feature-extraction-and-machine-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/mri-classification-of-brain-tumors-using-efficientnetb0-feature-extraction-and-machine-learning-methods/120616/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Pendekatan apa yang diusulkan untuk klasifikasi tumor otak berbasis MRI?","Question",{"text":75,"@type":76},"Pendekatan menggabungkan ekstraksi fitur menggunakan EfficientNet B0 dengan algoritma machine learning untuk klasifikasi.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Langkah apa saja yang dilakukan pada data sebelum klasifikasi?",{"text":80,"@type":76},"Citra MRI dipreproses dan diubah ukurannya sesuai input EfficientNet B0, lalu diekstraksi menjadi vektor fitur. Selanjutnya PCA dipakai untuk reduksi dimensi dan SMOTE untuk menyeimbangkan kelas.",{"name":82,"@type":73,"acceptedAnswer":83},"Algoritma mana yang menghasilkan akurasi tertinggi dan berapa nilainya?",{"text":84,"@type":76},"Support Vector Machine (SVM) mencapai akurasi tertinggi sekitar 96%, sedangkan XGBoost dan LightGBM sekitar 94%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]