Inflation Rate Prediction in Indonesia using Optimized Support Vector Regression Model

Oktanisa, Irvi; Mahmudy, Wayan Firdaus; Maski, Ghozali

doi:10.25126/jitecs.202051173

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…SVM maps each input and output data to represent the similarity vector [32]. Vector mapping can be either linear or non-linear [33]. For non-linear functions, a kernel is required in the mapping process.…”

Section: Classificationmentioning

confidence: 99%

A Study of Stressed Facial Recognition Based on Histogram Information

Prasetio

Widasari

Bachtiar

2022

IJCAI

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Stress represents our subconscious emotions. The majority of the unconscious content is unacceptable or unpleasant such as pain, anxiety, or conflict. Most individuals do not realize that they are experiencing stress. Prolonged stressful experiences are likely to lead to health problems and affect one's facial appearance, specifically wrinkles shown in the face. This paper discussed the introduction of facial stress with histogram information. There are three stages in recognizing the stress pattern on the face of the registered image, feature extraction and classification. The registered image process takes three important parts of the face, i.e. the eyes, nose, and mouth. The feature extraction process was performed using the histogram method, i.e. Gabor filter and HOG feature. Each extracted feature was used as the model input to determine whether or not an individual is suffering from stress. Two classification methods were applied to learn stress patterns from the extracted feature. The classification process was performed using SVM with six kernel functions and a Tree algorithm with three numbers of split. Each model is trained using ten cross-fold validation strategies. The test results showed that the Gabor filter and HOG feature accuracy were 55% and 65%, respectively. The effectiveness of the proposed method is evaluated by comparing it with the existing methods in term of accuracy. Povzetek: Predstavljena je študija ugotavljanja stresa iz obraznih mimik.

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Section: Classificationmentioning

confidence: 99%

A Study of Stressed Facial Recognition Based on Histogram Information

Prasetio

Widasari

Bachtiar

2022

IJCAI

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“…In forecasting research that used Neural Networks, there are many methods used in the following examples. In the research of Alfiyatin [2] and Oktanisa [12] discusses inflation forecasting in Indonesia. For E research using the method (ELM optimization with the PSO method), and for the I method (SVR optimization with the GA method) The results in research E forecasting with ELM and ELM optimized with PSO, the results are almost the same with the difference of error 0.0000019.…”

Section: Related Workmentioning

confidence: 99%

Website Visitors Forecasting using Recurrent Neural Network Method

Arya

Mahmudy

Basuki

2021

JITeCS

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Abstract. The number of visitors and content accessed by users on a site shows the performance of the site. Therefore, forecasting needs to be done to find out how many users a website will come. This study applies the Long Short Term Memory method which is a development of the Recurrent Neural Network method. Long Short Term Memory has the advantage that there is an architecture of remembering and forgetting the output to be processed back into the input. In addition, the ability of another Long Short Term Memory is to be able to maintain errors that occur when doing backpropagation so that it does not allow errors to increase. The comparison method used in this study is Backpropagation. Neural Network method that is often used in various fields. The testing using new visitor data and first time visitors from 2018 to 2019 with vulnerable time per month. The computational experiment prove that the Long Short Term Memory produces better result in term of the mean square error (MSE) comparable to those achieved by Backpropagation Neural Network method.

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