Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Utami, Tri Mar'ati Nur; Novitasari, Dian Candra Rini; Setiawan, Fajar; Ulinnuha, Nurissaidah; Farida, Yuniar

doi:10.15294/sji.v8i2.28906

Cited by 4 publications

(4 citation statements)

References 23 publications

(25 reference statements)

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“…Utilizing cross-validation techniques, such as Leave-One-Out (LOO) method, internal validation is carried out using a subset of the data for validation and the remaining portion for training [39]. The model performance on the training and testing sets is measured using the RMSE metric, which can be seen in Figure 2 [40].…”

Section: Model Validationmentioning

confidence: 99%

Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets

Budi

Akrom

Trisnapradika

et al. 2023

SJI

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Purpose: Experimental studies are usually costly, time-consuming, and resource-intensive when it comes to investigating prospective corrosion inhibitor compounds. Machine learning (ML) based on the quantitative structure-property relationship model (QSPR) has become a massive method for testing the effectiveness of chemical compounds as corrosion inhibitors. The main challenge in the ML method is to design a model that produces high prediction accuracy so that the properties of a material can be predicted accurately. In this study, we examine the performance of polynomial functions in the ML-based NuSVR algorithm in evaluating the regression dataset of corrosion inhibition efficiency of pyridine-quinoline compounds.Methods: Polynomial functions for NuSVR algorithm-based ML.Result: The outcomes demonstrate that the NuSVR model's prediction ability is greatly enhanced by the application of polynomial functions. Originality: The combination of polynomial functions and deep machine learning based NuSVR algorithms to increase the accuracy of predictive models.

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Section: Model Validationmentioning

confidence: 99%

Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets

Budi

Akrom

Trisnapradika

et al. 2023

SJI

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“…Solusi masalah ini biasanya ditemukan melalui pendekatan pemrograman kuadratik. Untuk mencapai aproksimasi yang optimal, digunakan formulasi yang telah dimodifikasi [17]. Selain itu, Support Vector Regression (SVR) juga mampu mengatasi masalah overfitting dan underfitting yang sering terjadi pada model regresi tradisional.…”

Section: Support Vector Regression (Svr)unclassified

Prediksi Jumlah Perceraian Menggunakan Metode Support Vector Regression (SVR)

Indra Septiawati,

Budianita,

Insani

et al. 2023

JoSYC

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The increasing number of divorces poses an increasingly significant social challenge in Indonesia, including in the city of Pekanbaru. The impact of these divorces on the adolescent population can have negative effects on their emotional and psychological well-being, as well as their ability to interact socially and engage in the learning process. This study utilizes monthly divorce data from 2015 to April 2023 to conduct time series analysis and applies the Support Vector Regression (SVR) method to predict the number of divorces in the city of Pekanbaru. Three types of SVR kernels, namely linear, polynomial, and radial basis function (RBF), are evaluated and compared to find the kernel with the best Mean Squared Error (MSE) results. Through grid search analysis, optimal parameter values for each kernel are determined. The test results indicate that the SVR model with a polynomial kernel provides more accurate predictions with an MSE of 0.010228, compared to the linear kernel (MSE = 0.012767) and the RBF kernel (MSE = 0.010812).

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“…𝐴(𝑥) is a nonlinear mapping that maps the x vector input to the vector space, so linear regression in high dimensional vector space can be performed [16]. The regression function can be seen in Equation (1) [17].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Travel Time Estimation Using Support Vector Regression on Model with 8 Features

Kosasih

Mardhiyah²

2022

SJI

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Purpose: In travelling, we need to predict travel time so that itinerary is as expected. This paper proposes Support Vector Regression (SVR) to build a prediction model. In this case, we will estimate travel time in the Bali area. We propose to use a regression model with 8 features, i.e., time, weather, route, wind speed, day, precipitation, temperature and humidity information.Methods: In this study, we collect real-time data from Global Positioning System (GPS) and weather applications. We divide our data into two types: training dataset consisting of 177 data and testing dataset comprising 51 data. The Support Vector Regression (SVR) method is used in the training stage to build a model representing data. To validate the model, error measurements were carried out by calculating the values of R2, Accuracy, MAE (Mean Absolute Error), RMSE (Root Mean Square Error) and Accuracy.Result: From the research results, the model obtained is the SVR model with parameters γ=0.125, ε=0.1 and C = 1000, which has a value of R2= 0.9860528612283006. Later, we predict travel times on testing data using the SVR model that has been obtained. Based on the result of the research, our model has a 0.8008 MAE (Mean Absolute Error), 1.2817 RMSE (Root Mean Square Error) and 95.3369% Accuracy.Novelty: In this study, we use 8 features to estimate travel time in the Bali area. Furthermore, we will compare the KNN regression method (previous studies) with Support Vector Regression (SVR) (proposed method) on a model with 8 features to estimate travel time.

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Tide Prediction in Prigi Beach using Support Vector Regression (SVR) Method

Cited by 4 publications

References 23 publications

Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets

Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets

Prediksi Jumlah Perceraian Menggunakan Metode Support Vector Regression (SVR)

Travel Time Estimation Using Support Vector Regression on Model with 8 Features

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