02805nas a2200481   4500000000100000000000100001008004100002260005900043653001900102653001000121653003800131653003800169653000800207653003600215653001600251653003300267653003400300653001000334653001800344653002600362653001400388653002400402653002100426653002800447653002100475653003100496653002200527653002200549653003000571100001500601700001700616700002000633700001300653700001700666700001400683700001700697700001500714700001700729245011600746856015500862520128101017020002502298       2021                            d  bInstitute of Electrical and Electronics Engineers Inc.10aAuthentication10aBatik10aBatik Authenticity Classification10aBatik authenticity classification10aCNN10aClassification (of information)10aConvolution10aconvolutional neural network10aConvolutional neural networks10aCrime10aDeep learning10aHistoric preservation10aIndonesia10aLearning algorithms10amachine learning10aMachine learning models10aMachine-learning10aNeural networks algorithms10aTransfer learning10aTransfer learning10aTransfer learning methods1 aF.A. Putra1 aD.A.C. Jamil1 aB.A. Prabandanu1 aS. Faruq1 aF.A. Pradana1 aR.F. Alya1 aH.A. Santoso1 aF. Al Zami1 aF.O. Saputra00aClassification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123708966&doi=10.1109%2fICIC54025.2021.9632937&partnerID=40&md5=a64c9a322e89c87b8eb4be43c070db4c3 aBatik is one of Indonesia s cultural heritages that UNESCO has recognized as an Intangible Cultural Heritage, so we should be proud and preserve it. However, there are problems in the batik industry related to the labelling of traditional and modern batik products. The prevalence of fraud in printed batik, which is given a price equivalent to written batik, which is much more expensive, and public ignorance of the aesthetic value and authenticity of written batik, can disrupt the traditional batik industry in Indonesia. Based on these problems, the authors innovate to develop a machine learning model that aims to classify the authenticity of batik using the Convolutional Neural Network Algorithm with Transfer Learning Method. The classification process consists of several stages: collecting datasets, preprocessing data, developing CNN models with transfer learning, and compiling and training models. The development of the machine learning model that has been trained produces an accuracy of 96.91\%. The author hopes that this research can make it easier for people to distinguish between written and printed batik, minimize the existence of batik price fraud, and increase consumer confidence in batik transactions by ensuring the originality of batik products.  a9781665421553 (ISBN)