Deskripsi dan Potensi Cendawan Makro Asal Hutan Adat Penembahen, Desa Juhar, Kabupaten Tanah Karo, Sumatera Utara

Sibero, Mada Triandala; Putra, Ivan Permana; Murwani, Retno

doi:10.46638/jmi.v5i1.164

Cited by 1 publication

(1 citation statement)

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“…Mushroom is one of the types of plants that can easily grow in Indonesia because Indonesia is a tropical country with enough rainfall and sunlight [1]. Some kinds of edible mushrooms even contain important substances for medication [2,3].…”

Section: Introductionmentioning

confidence: 99%

The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach

Hermawan,

Wibowo,

Setiawan Wijaya

2022

matrik

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An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks

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Section: Introductionmentioning

confidence: 99%