Classifying Student’s Duration of Study in Faculty of Science and Technology UNAIR Using Naïve Bayes and Neural Network Classifiers

Ulyah, Siti Maghfirotul; Rifada, Marisa; Ana, Elly

doi:10.2991/assehr.k.201010.022

Cited by 2 publications

(1 citation statement)

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“…The training dataset was from July 31, 2019 to May 18, 2020 and the testing dataset used the BRI's financial statement from May 19, 2020 to September 30, 2020. This proportion of split was adapted from the work by Ulyah et al [25] who compared some ratios of training and testing data and concluded that the highest accuracy of ANN was obtained in the ratio of 70:30.…”

Section: Methodsmentioning

confidence: 99%

Comparison study of transfer function and artificial neural network for cash flow analysis at Bank Rakyat Indonesia

Faricha¹,

Ulyah

Susanti

et al. 2022

IJECE

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<span lang="EN-US">The cash flow analysis is essential to examine the economic flows in the financial system. In this paper, the financial dataset at Bank Rakyat Indonesia was used, it recorded the sources of cash inflow and outflow during a particular period. The univariate time series model like the autoregressive and integrated moving average is the common approach to build the prediction based on the historical dataset. However, it is not suitable to estimate the multivariate dataset and to predict the extreme cases consisting of nonlinear pairs between independent-dependent variables. In this study, the comparison of using two types of models i.e., transfer function and artificial neural network (ANN) were investigated. The transfer function model includes the coefficient of moving average (MA) and autoregressive (AR), which allows the multivariate analysis. Furthermore, the artificial neural network allows the learning paradigm to achieve optimal prediction. The financial dataset was divided into training (70%) and testing (30%) for two types of models. According to the result, the artificial neural network model provided better prediction with achieved root mean square error (RMSE) of 0.264897 and 0.2951116 for training and testing respectively.</span>

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Section: Methodsmentioning

confidence: 99%