Open AccessSurvei Penggunaan Tensorflow pada Machine Learning untuk Identifikasi Ikan Kawasan Lahan Basah
Author(s) -
Nuruddin Wiranda,
Harja Santana Purba,
R. Ati Sukmawati
Publication year - 2020
Publication title -
ijeis (indonesian journal of electronics and instrumentation system)/ijeis (indonesian journal of electronics and instrumentation systems)
Language(s) - English
Resource type - Journals
eISSN - 2460-7681
pISSN - 2088-3714
DOI - 10.22146/ijeis.58315
Subject(s) - artificial intelligence , random forest , machine learning , identification (biology) , computer science , support vector machine , wetland , convolutional neural network , pattern recognition (psychology) , fish <actinopterygii> , mnist database , artificial neural network , fishery , ecology , biology
Wetlands are habitats commonly used for fish cultivation. South Kalimantan is one of the provinces that has a wetland area, which is 11,707,400ha, there are 67 rivers and an estimated 200 species of fish. This shows the abundant wealth of fish treasures and economic value. The study of fish identification is an important subject for the preservation of wetland fish. In the field of artificial intelligence, identification can be done using Machine Learning (ML). There are many libraries, a collection of functions that can be used in ML development, one of which is Tensorflow. In this paper, we survey a variety of literature on the use of Tensorflow, as well as datasets, algorithms, and methods that can be used in developing wetland area fish image identification applications.The results of the literature survey show that Tensorflow can be used for the development of fish character identification applications. There are many datasets that can be used such as MNIST, Oxford-I7, Oxford-102, LHI-Animal-Faces, Taiwan marine fish, KTH-Animal, NASNet, ResNet, and MobileNet. Classification methods that can be used to classify fish images include CNN, R-CNN, DCNN, Fast R-CNN, kNN, PNN, Faster R-CNN, SVM, LR, RF, PCA and KFA. Tensorflow provides many models that can be used for image classification, including Inception-v3 and MobileNets, and supports models such as CNN, RNN, RBM, and DBN. To speed up the classification process, image dimensions can be reduced using the MDS, LLE, Isomap, and SE algorithms.