?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=ANALISIS+KLASIFIKASI+KANKER+PAYUDARA+DARI+CITRA%0D%0AMAMMOGRAM+MENGGUNAKAN+TRANSFER+LEARNING+VGG19&rft.creator=OKTINA+LATHIFAH%2C+HANIM&rft.subject=500+ilmu+pengetahuan+alam+dan+matematika&rft.subject=510+Matematika&rft.description=Penggunaan+deep+learning+dapat+diaplikasikan+pada+berbagai+jenis+pekerjaan%0D%0Aseperti+memprediksi+peluang+dan+kejadian%2C+pengenalan+objek%2C+dan+diagnosis%0D%0Apenyakit.+Namun%2C+deep+learning+memerlukan+data+besar+dan+sumber+daya%0D%0Akomputasi+yang+signifikan+untuk+melatih+model+dari+awal.+Hal+ini+dapat+diatasi%0D%0Adengan+transfer+learning.+Salah+satu+pengembangan+model+deep+learning+dan%0D%0Atransfer+learning+adalah+VGG19%2C+terutama+dalam+pengenalan+gambar.+VGG19%0D%0Adigunakan+sebagai+basis+model+yang+dapat+mencapai+kinerja+tinggi+tanpa%0D%0Amemerlukan+pelatihan+dari+awal%2C+yang+menghemat+waktu+dan+sumber+daya%0D%0Akomputasi+seperti+dalam+aplikasi+yang+memerlukan+pengenalan+pola+visual+yang%0D%0Akompleks+yaitu+pada+bidang+medis+untuk+mendeteksi+penyakit+dari+gambar+medis%0D%0Aseperti+citra+mammogram+kanker+payudara.+Penelitian+ini+bertujuan+untuk%0D%0Amengimplementasikan+metode+deep+learning+yaitu+transfer+learning+VGG19+untuk%0D%0Amengklasifikasikan+citra+mamogram+kanker+payudara.+Data+yang+digunakan+dalam%0D%0Apenelitian+ini+adalah+data+sekunder+yang+diperoleh+dari+Pilot+European+Image%0D%0AProcessing+Archive+yaitu+data+The+Mini-MIAS+database+of+mammograms.+Jumlah%0D%0Adata+Breast+Cancer+yaitu+sebanyak+322+data+dengan+resolusi+sebesar+1024%C3%971024%0D%0Apiksel.+Hasil+penelitian+ini+menunjukkan+bahwa+model+yang+dibangun+dengan%0D%0Atransfer+learning+dengan+base+model+VGG-19+dapat+digunakan+untuk+melakukan%0D%0Aklasifikasi+kanker+payudara+dengan+pembagian+data+85%25+data+modelling+dan+15%25%0D%0Adata+testing.+Selanjutnya%2C+data+modelling+dibagi+lagi+menjadi+data+trainning+sebesar%0D%0A85%25+dan+data+validasi+sebesar+15%25+menghasilkan+model+klasifikasi+dan+hasil%0D%0Aterbaik+untuk+melakukan+klasifikasi+citra+mammografi+kanker+payudara+dibuktikan%0D%0Adengan+nilai+akurasi+sebesar+93%2C35%25+serta+dilihat+dari+nilai+spesifisitas+dan%0D%0Asensitivitas+yaitu+sebesar+95%2C25%25+dan+90%2C19%25.%0D%0A%0D%0AKata+kunci%3A+kanker+payudara%2C+klasifikasi%2C+transfer+learning%2C+VGG19%0D%0A%0D%0AThe+use+of+deep+learning+can+be+applied+to+various+types+of+tasks+such+as+predicting%0D%0Aprobabilities+and+events%2C+object+recognition%2C+and+disease+diagnosis.+However%2C+deep%0D%0Alearning+requires+large+datasets+and+significant+computational+resources+to+train%0D%0Amodels+from+scratch.+This+can+be+addressed+with+transfer+learning.+One+of+the%0D%0Adevelopments+in+deep+learning+and+transfer+learning+models+is+VGG19%2C+particularly%0D%0Ain+image+recognition.+VGG19+is+used+as+a+base+model+that+can+achieve+high%0D%0Aperformance+without+the+need+for+training+from+scratch%2C+saving+time+and%0D%0Acomputational+resources.+This+is+especially+useful+in+applications+that+require+the%0D%0Arecognition+of+complex+visual+patterns%2C+such+as+in+the+medical+field+for+detecting%0D%0Adiseases+from+medical+images+like+breast+cancer+mammograms.+This+research+aims%0D%0Ato+implement+a+deep+learning+method%2C+specifically+transfer+learning+with+VGG19%2C%0D%0Ato+classify+breast+cancer+mammogram+images.+The+data+used+in+this+study+is%0D%0Asecondary+data+obtained+from+the+Pilot+European+Image+Processing+Archive%2C%0D%0Aspecifically+the+Mini-MIAS+database+of+mammograms.+The+dataset+consists+of+322%0D%0Abreast+cancer+images+with+a+resolution+of+1024%C3%971024+pixels.+The+results+of+this%0D%0Astudy+show+that+a+model+built+using+transfer+learning+with+the+VGG19+base+model%0D%0Acan+be+used+to+classify+breast+cancer+with+a+data+split+of+85%25+for+modelling+and+15%25%0D%0Afor+testing.+Furthermore%2C+the+modelling+data+is+split+again+into+85%25+for+training+and%0D%0A15%25+for+validation%2C+resulting+in+the+best+classification+model+for+breast+cancer%0D%0Amammography+images%2C+as+evidenced+by+an+accuracy+rate+of+93.35%25%2C+with%0D%0Aspecificity+and+sensitivity+values+of+95.25%25+and+90.19%25%2C+respectively.%0D%0A%0D%0AKeywords%3A+breast+cancer%2C+classification%2C+transfer+learning%2C+VGG19&rft.publisher=FAKULTAS+MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM&rft.date=2024-07-10&rft.type=Skripsi&rft.type=NonPeerReviewed&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F82593%2F1%2F1817031016%2520-%2520Oktina%2520-%2520ABSTRAK%2520-%2520Oktina%2520Lathifah%2520Hanim.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F82593%2F2%2F1817031016%2520-%2520Oktina%2520-%2520SKRIPSI%2520FULL%2520TANPA%2520LAMPIRAN%2520-%2520Oktina%2520Lathifah%2520Hanim.pdf&rft.format=text&rft.identifier=http%3A%2F%2Fdigilib.unila.ac.id%2F82593%2F3%2F1817031016%2520-%2520Oktina%2520-%2520SKRIPSI%2520TANPA%2520BAB%2520PEMBAHASAN%2520%2526%2520TANPA%2520LAMPIRAN%2520-%2520Oktina%2520Lathifah%2520Hanim.pdf&rft.identifier=++OKTINA+LATHIFAH%2C+HANIM++(2024)+ANALISIS+KLASIFIKASI+KANKER+PAYUDARA+DARI+CITRA+MAMMOGRAM+MENGGUNAKAN+TRANSFER+LEARNING+VGG19.++FAKULTAS+MATEMATIKA+DAN+ILMU+PENGETAHUAN+ALAM%2C+UNIVERSITAS+LAMPUNG.+++++&rft.relation=http%3A%2F%2Fdigilib.unila.ac.id%2F82593%2F