scholarly journals Prediction Of Use Of Electric Kwh Using Support Vector Regression (SVR) Method (Case Study: Pt Pln (Persero) Rayon Seririt)

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 525
Author(s):  
Rima Mediana ◽  
Setio Basuki ◽  
Nur Hayatin
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Radial Basis

AbstrakPeranan listrik sangat penting bagi kehidupan masyarakat, begitu pentingnya peranan listrik tentu saja berdampak pada kebutuhan listrik yang begitu besar, maka PT. PLN (Persero) Rayon Seririt sebagai penyedia tenaga listrik harus bisa memprediksi besarnya peggunaan listrik rumah tangga setiap harinya. Selain itu menyebabkan semakin besar pula pemakian kwh listik, apabila pemakaian kwh listrik tidak diolah dengan baik akan menimbulkan beban energi listrik yang tidak terbendung. Dengan permasalahan yang telah diuraikan, penelitian ini menerapkan algoritma Support Vector Regression dalam Prediksi Pemakain KWH Listrik untuk mengetahui besarnya pemakaian kwh listrik yang akan datang. Berdasarkan hasil pengujian yang dilakukan hasil nilai akurasi terbaik Mean Absolute Error (MAE) sebesar 133560,1, Root Mean Squared Error (RMSE) sebesar 167664,1, dan Koefisien Korelasi sebesar 84,0 pada kernel polynomial. Sehingga algoritma Support Vector Regression dan fungsi kernel Radial Basis Function (RBF) cocok digunakan dalam memprediksi pemakaian kwh listrik.AbstractThe role of electricity is really significant for societies' live and it brings the huge impacts on the needs of electricity. This circumstance makes PT. PLN (Persero) Rayon Seririt as the provider of electricity must be able to predict the amount of household electricity usage steadily. This also causes the greater use of kwh electricity, if the use of kwh electricity is not treated properly, it will cause the burden of electrical energy is unstoppable.  Through the problems that have been elaborated, this study implements the Support Vector Regression algorithm in the prediction of kwh electricity usage to know the amount of  kwh electricity usage that will come.Based on the results of tests that have been conducted,  the result of best accuracy value Mean Absolute Error (MAE) equal to 133560,1, Root Mean Squared Error (RMSE) equal to 133560,1, and Correlation Coefficient equal to 84,0 at Radial Base Function kernel. It means, the Support Vector Regression algorithm and Radial Basis Function kernel function (RBF) are suitable to predict the use of kwh electricity.

scholarly journals Model Spasial untuk Prediksi Konsentrasi Polutan Kabut Asap Kebakaran Lahan Gambut Menggunakan Support Vector Regression

2019 ◽  
Vol 5 (2) ◽  
pp. 119 ◽  
Author(s):  
Muhammad Asyhar Agmalaro ◽  
Imas Sukaesih Sitanggang ◽  
Lailan Sahrina Hasibuan ◽  
Muhammad Murtadha Ramadhan
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Mean Squared Error ◽  
Parameter Tuning ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Radial Basis

<p>Kabut asap dari kebakaran lahan gambut mengandung berbagai macam polutan seperti CO dan CO<sub>2</sub>. Polutan tersebut dapat berimplikasi buruk pada kesehatan masyarakat sekitar peristiwa itu terjadi yang berupa Infeksi Saluran Pernafasan Atas (ISPA). Penelitian ini bertujuan untuk membuat model spasial<em> </em>untuk prediksi<em> </em>konsentrasi polutan kabut asap yang berupa CO dan CO<sub>2</sub> dari kebakaran lahan gambut di Sumatra tahun 2015. Model spasial dibentuk menggunakan algoritme <em>support vector regression</em> (SVR) dengan kernel <em>radial basis function </em>(RBF) dengan melihat konsentrasi polutan dari beberapa titik tetangga. <em>Parameter tuning </em>dilakukan untuk mendapatkan nilai parameter paling optimal dari SVR. Hasil penelitian menunjukkan bahwa model spasial prediksi konsentrasi CO terbaik didapatkan pada <em>gamma </em>dengan nilai 20 yang menghasilkan <em>root mean squared error </em>(RMSE) dan nilai koefisien korelasi sebesar 1,174242×10<sup>-8 </sup>dan 0,5879287. Model spasial prediksi konsentrasi CO<sub>2</sub> terbaik dibentuk pada <em>gamma </em>dengan nilai 10 yang  menghasilkan RMSE dan nilai koefisien korelasi sebesar 9,843717×10<sup>-8 </sup>dan 0,6058418. Hasil prediksi dari model yang dibentuk telah dapat mengikuti pola nilai aktual konsentrasi polutan.</p><p>Kata Kunci: CO, CO<sub>2</sub>, kabut asap, model spasial, support vector regression.</p>

scholarly journals Radial basis function neural network for 2 satisfiability programming

2020 ◽  
Vol 18 (1) ◽  
pp. 459 ◽  
Author(s):  
Shehab Alzaeemi ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam ◽  
Mustafa Mamat
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Squared Error ◽  
Training Technique ◽  
Radial Basis ◽  
Hidden Neurons

<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>

scholarly journals Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

2021 ◽  
Vol 11 (8) ◽  
pp. 3705
Author(s):  
Jie Zeng ◽  
Panayiotis C. Roussis ◽  
Ahmed Salih Mohammed ◽  
Chrysanthos Maraveas ◽  
Seyed Alireza Fatemi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Particle Velocity ◽  
Basis Function ◽  
Absolute Error ◽  
Peak Particle Velocity ◽  
Support Vector ◽  
Effective Parameters ◽  
Experimental Database ◽  
Interaction Detection ◽  
Radial Basis

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

scholarly journals Classificação do Risco de Quedas em Idosos com Least Squares Support Vector Regression Utilizando Sinais Eletromiográficos e Dinamométricos

2021 ◽  
Author(s):  
Daniele Alves Silva ◽  
Laiana Sepúlveda de Andrade Mesquita ◽  
Luan Marinho Morais Pereira ◽  
Nayra Ferreira Lima Castelo Branco ◽  
Hermes Manoel Galvão Castelo Branco ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Support Vector Regression ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis ◽  
Leave One Out

A determinação do risco de cair é de suma importância na assistência à saúde do idoso, pois a ocorrência de quedas nessa população trazem consequências em vários aspectos. Ferramentas de aprendizado de máquinas têm sido cada vez mais empregadas com este fim. Portanto, o objetivo deste estudo foi investigar a viabilidade da utilização de sinais eletromiográficos e dinamométricos na classificação do risco de quedas em idosos via modelo least squares support vector regression (LSSVR). Trinta e um voluntários idosos foram avaliados com a Escala de Equilíbrio de Berg (EEB), eletromiografia e dinamometria do membro inferior dominante. Para o modelo LSSVR foram utilizados kernels do tipo linear, polinomial e radial basis function (RBF), além de validações cruzadas pelos métodos leave one out e K-fold. Ambos os sinais apresentaram erros médios baixos na maioria das execuções realizadas. Dessa forma, verificou-se que é possível classificar o risco de quedas em idosos por meio de sinais eletromiográficos e dinamométricos aplicados ao modelo LSSVR.

PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS

2018 ◽  
Vol 14 (2) ◽  
pp. 137
Author(s):  
Haerul Fatah ◽  
Agus Subekti
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Search Algorithm ◽  
Absolute Error ◽  
Nearest Neighbour ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Nearest Neighbours

Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.

COVID-19 Spread Pattern Using Support Vector Regression

2020 ◽  
Vol 8 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Herlawati Herlawati
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Soft Computing ◽  
Basis Function ◽  
Rapid Development ◽  
Support Vector ◽  
Growth Simulation ◽  
Real Condition ◽  
Radial Basis ◽  
Spread Pattern

Pandemics are rare and happen in about 100 years period. Current pandemic, COVID-19, occurs in the industrial 4.0 era where there is a rapid development computation. Yet, the scientists in every country face difficulty in predicting the growth simulation of this pandemic. The paper tries to use a soft computing algorithm to predict the pattern of the COVID-19 pandemic in Indonesia. Support Vector Regression was used in Google Interactive Notebook with some kernels for comparison, i.e. radial basis function, linear and polynomial. The testing results showed that radial basis function outperformed other kernels as a regressor with some parameters should follows the real condition, i.e. gamma, c, and epsilon.

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