Komparasi Metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk Klasifikasi Kanker Payudara

IF 0.7 4区 历史学 0 ARCHAEOLOGY
J. Kusuma, B. Hayadi, Wanayumini Wanayumini, Rika Rosnelly
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引用次数: 3

Abstract

ABSTRAKPenyebab kematian utama saat ini di dunia salah satunya dikarenakan oleh penyakit kanker. Menurut data Globocan 2018, dengan tingkat kematian rerata 17 per 100.000 jiwa dan insiden sebanyak 2,1 per 100.000 jiwa untuk kanker payudara yang menyerang wanita di Indonesia. Hal ini menjadikan Indonesia menempati peringkat ke-23 di Asia dan ke-8 di Asia Tenggara. Seiring perkembangan teknologi, sistem berbantuan komputer telah membantu orang di berbagai bidang misalnya di bidang medis. Penentuan jenis kanker payudara menggunakan mechine learning dapat membantu ahli patologi melakukan pemeriksaan secara lebih konsisten dan efisien. Pada penelitian ini, akan dilakukan komparasi metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk klasifikasi kanker payudara. Adapun hasil yang didapatkan menunjukan bahwa, dalam klasifikasi metode Multi Layer Perceptron (MLP) dengan fungsi aktivasi Logistic dan fungsi optimisasi Adam memberikan nilai accuracy, precision dan recall terbaik dibandingkan Support Vector Machine yaitu sebesar 97.7%.Kata kunci: Multi Layer Perceptron (MLP), Aktivasi Logistic, Optimisasi Adam, Support Vector Machine (SVM), Kanker PayudaraABSTRACTThe leading cause of death today in the world is due to cancer. According to Globocan 2018 data, with an average mortality rate of 17 per 100,000 people and an incidence of 2.1 per 100,000 people for breast cancer that affects women in Indonesia. This makes Indonesia ranked 23rd in Asia and 8th in Southeast Asia. As technology has evolved, computer-aided systems have helped people in various fields such as in the medical field. Determination of the type of breast cancer using mechine learning can help pathologists perform examinations more consistently and efficiently. In this study, a comparison of the Multi Layer Perceptron (MLP) and Support Vector Machine (SVM) methods will be carried out for breast cancer classification. The results obtained showed that, in the classification of multi layer perceptron (MLP) methods with logistic activation function and Adam optimization function provides the best accuracy, precision and recall value compared to Support Vector Machine which is 97.7%.Keywords: Multi Layer Perceptron (MLP), Logistic Activation, Adam Optimization, Support Vector Machine (SVM), Breast Cancer
比较多层Perceptron (MLP)和支持向量机(SVM)对乳腺癌分类
当今世界的主要死因之一是癌症。根据2018年环球报,每10万人中有17人死亡,印尼乳腺癌发病率为每10万人中有2.1人死亡。这使得印尼在亚洲排名第23名,在东南亚排名第8名。随着技术的发展,计算机辅助系统在医学等领域帮助了人们。用机械学习来确定乳腺癌类型,可以帮助病理学家更一致、更有效地检查乳腺癌。在这项研究中,将对乳腺癌分类进行比较多层Perceptron (MLP)和支持矢量机(SVM)。研究结果表明,在模型多层结构(MLP)的逻辑激活和优化功能分类中,亚当的准确、精确和优化功能提供了比Vector Machine支持系统更好的准确、精确和记忆值为97.7%。关键词:多层Perceptron (MLP),逻辑激活,亚当优化,支撑矢量机(SVM),癌症导致死亡今天在世界上导致癌症。根据2018年环球数据,每10万人中就有17人死亡,每10万人中就有2人因癌症而受到影响。这使得印尼在亚洲东南部排名第23、8位。美国技术改善了,计算机系统帮助了医学领域不同领域的人们。通过mechine学习,对这种巨蟹座品种的定义可以帮助病理学家更持久、更有效地进行实验。在这项研究中,多层perlp (MLP)和支撑向量机(SVM)的方法将被视为乳房分类经典。结果表明,在模型模型中,准确、准确和准确的功能提供了最准确、精确和准确的计算方法,支持这台机器,即97.7%。多层Perceptron (MLP),逻辑激活,亚当乐观,支撑机(SVM), brecer
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来源期刊
CiteScore
1.60
自引率
0.00%
发文量
23
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