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Data mining is the process of finding patterns or interesting information in selected data by using a particular technique or method. Utilization of data mining one of which is forecasting. Various forecasting methods have progressed along with technological developments. Support Vector Regression (SVR) is one of the forecasting methods that can be...
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... study uses the data of Consumer Price Index (CPI) as much as 98 data period January 2010 to February 2018. For example, as shown in Table 1. PSO SVR PSO SVR PSO SVR The data is divided into two namely the training data and test data, with a ratio of 50% each. Next is to forecast the inflation rate using SVR and SVR-WAPSO methods. ...
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... study uses the data of Consumer Price Index (CPI) as much as 98 data period January 2010 to February 2018. For example, as shown in Table 1. PSO SVR PSO SVR PSO SVR The data is divided into two namely the training data and test data, with a ratio of 50% each. Next is to forecast the inflation rate using SVR and SVR-WAPSO methods. ...
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... Proses perhitungan yang dilakukan yaitu perkalian antara nilai hasil peramalan dengan nilai selisih nilai aktual tertinggi (maxv) dikurangi nilai aktual terendah (minv). Hasil tersebut kemudian dikurangi dengan nilai data aktual terendah (minv) untuk mengembalikan nilai hasil peramalan ke dalam rentang semula (Priliani et al., 2018). Metode seleksi fitur yang digunakan adalah SFS (Sequential Forward Selection). ...
Kelangsungan hidup di dunia pasti tidak akan bisa terlepas dari penggunaan air. Seiring dengan berkembangnya zaman tentu akan diikuti dengan bertambahnya jumlah penduduk yang juga akan berdampak terhadap meningkatnya kebutuhan air bersih, salah satunya di Kota Denpasar. PDAM merupakan salah satu instansi yang melayani ketersediaan air bersih. Tujuan diadakannya penelitian ini untuk melakukan peramalan terhadap jumlah distribusi air di PDAM Kota Denpasar dengan membandingkan metode ANN (Artificial Neural Network) serta CNN (Convolutional Neural Network) yang juga melibatkan proses seleksi fitur menggunakan metode SFS (Sequential Forward Selection). Pendekatan kuantitatif digunakan untuk melakukan proses peramalan terhadap jumlah distribusi air (Y) dengan melibatkan fitur jumlah produksi (X1), kebocoran (X2), pembelian (X3), dan pelanggan air (x4). Hasil seleksi fitur dengan metode SFS menujukkan bahwa jumlah produksi air (X1) dan kebocoran air (X2) dengan tingkat kesalahan sebesar 0.031069 tepat digunakan untuk membentuk model peramalan jumlah distribusi air dengan metode ANN yang menghasilkan nilai MSE dan MAPE berturut-turut sebesar 0.040 serta 2.02%. Berdasarkan hasil yang diperoleh, model yang dikembangkan termasuk model peramalan yang sangat baik, sehingga tepat digunakan untuk melakukan proses peramalan jumlah distribusi air.
... Several researchers have used SVR for time series data. For example, Hong (2009) developed the SVR model for forecasting electrical loads with the immune algorithm (IA) to determine parameters (σ, C, ε); Pai et al. (2010) developed the seasonal SVR (SSVR) model for seasonal time series forecasting problems; Purnama and Setianingsih (2020) applies SVR in forecasting data on the number of aircraft passengers because the data has a nonlinear data pattern; Priliani et al. (2018) implements the Weight Attribute Particle Swarm Optimization (WAPSO) optimization technique to obtain optimal SVR parameters, etc. On the other hand, several researchers have used RF to predict time series data. ...
Classical statistics are usually based on parametric models, where the performance depends heavily on assumptions and is not robust in the presence of outliers in the data. Due to the COVID-19 pandemic, our daily lives have changed significantly, including slowing economic growth. These extreme changes can manifest as an outlier in time series studies and adversely affect the results of data analysis. Many classical methods of official statistics are prone to outliers. In this work, we evaluate machine learning methods: Support Vector Regression (SVR) and Random Forest (RF) and compare it with ARIMA to determine the robustness through simulation studies. Robustness is measured by the sensitivity of the SVR and Random Forest hyperparameter and the model’s error in the presence of outliers. Simulations show that more outliers lead to higher RMSE values, and conversely, more samples lead to lower RMSE values. The type of outliers significantly impacts the RMSE value of the ARIMA model, where additional outliers (AO) have a worse impact than temporary change (TC). Consecutive outliers produce a smaller RMSE mean than non-consecutive outliers. Based on the sensitivity of hyperparameters, SVR and Random Forest models are relatively robust to the presence of outliers in the data. Based on the simulation results of 100 iterations, we find that SVR is more robust than ARIMA and Random Forest in modeling time series data with outliers.KeywordsOutlierRandom forestRobustnessSupport vector regression
... Hasil penelitiannya adalah menghasilkan 3 variabel prediktor utama (kepuasan, merchandise, dan promosi) yang dapat digunakan untuk mengukur loyalitas pelanggan indomaret. Penelitian [11] mengunakan metode Support Vector Regression (SVR) untuk prediksi tingkat inflasi dengan akurasi sebesar 97.5%. ...
Toko Barokah merupakan toko eceran yang menjual berbagai sembako untuk kebutuhan sehari-hari. Persediaan barang terlalu banyak akan mengakibatkan kerugian seperti biaya simpan dan terjadinya kemungkinan penurunan kualitas barang. Sebaliknya, jumlah persediaan yangsedikit akan mengurangi keuntungan lebih besar. Penelitian inibertujuanmembangun sistem prediksi penjualan barang produk Unilever berbasis web menggunakan metode regresi linear sederhana.Pengujian akurasi terhadap hasil prediksi penjualan barang produk Unilever menggunakan MEA dan MAPE untuk melihat tingkatkesalahanhasil prediksi. Dataset menggunakandata penjualan produk Unilever sebanyak 15 bulan yaitu Januari 2021 sampai Maret 2022. Datasetdibagi menjadi 12 bulan sebagaidata training dan 3 bulan sebagaidata testing. Hasil prediksi pada 3 periode mendatangsetiap jenis produk menghasilkan nilai yang sama antara hasilsistem dengan hasil perhitungan manual regresi linear. Pengujian tingkat kesalahan terhadap hasil prediksi 3 periode yaitu bulan Januari sampai dengan Maret 2022 setiap produk Axe Deodorant, Bango Kecap, Buavita, Citra Lotion, Sabun Citra, ShampoClear, Sariwangi, SunsilkConditioner, Vixal dan Wall’s Ice Cream tergolong kategori hasil peramalan sangat akurat. Dengan nilai MAPE terkecil pada produk SunsilkConditioner sebesar 1%. Dengan demikian, metode regresi linear sangat akurat untuk prediksi penjualan barang jenis Unilever.
... Examples of commonly used kernel functions are polynomial kernel function, radial basis function, Sigmoid function, etc [21]- [23]. In this study, we use the radial basis function kernel that can be seen in Equation (7) because it provides the best performance to predict the load compared to other kernels [24]- [26]. ...
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.
... Normalisasi Min-Max dapat dicari dengan menggunakan formula sebagai berikut [12]: ...
... Pada akhir perhitungan, pada data yang sudah dinormalisasi dilakukan denormalisasi data untuk mengubah hasil prediksi yang nilainya masih dalam rentang 0 hingga 1 menjadi rentang data semula. Formulasi denormalisasi data diberikan pada persamaan berikut [12]: adanya faktor internal maupun faktor eksternal yang mempengaruhi naik turunnya harga penutupan saham harian kedua perusahaan tersebut. ...
... Normalisasi data bertujuan untuk memudahkan perhitungan numerik yang besar dengan mengubah nilai data asli ke kisaran nilai antara 0 hingga 1 [12]. Data input dan output dinormalisasi menggunakan metode Min-Max, karena metode ini dapat menjaga hubungan antara data asli dengan mencari nilai minimum dan maksimum pada setiap data [11]. ...
Pada artikel ini dikaji suatu metode yang dapat digunakan untuk meramalkan harga saham. Tujuan dari penelitian ini adalah memperkenalkan metode Support Vector Regression dengan Algoritma Grid Search untuk memprediksi harga saham INDF dan MYOR serta melakukan peramalan satu periode ke depan pada kedua perusahaan tersebut. Hasil kajian menghasilkan model prediksi terbaik untuk data saham INDF dengan nilai MAPE dan pada data testing berturut-turut sebesar 5.570% dan 79.9%, sedangkan untuk data saham MYOR diperoleh nilai MAPE dan pada data testing berturut-turut sebesar 2.954% dan 96%. Hasil penelitian juga menunjukkan prediksi harga saham INDF dan MYOR untuk satu periode selanjutnya (31 Desember 2021) berturut-turut sebesar Rp 6326.88/lembar dan Rp 2039.31/lembar.
... SVR is also proven to be better than Backpropagation for forecasting oil palm production [16]. However, similar to SVM, SVR cannot determine the appropriate parameters to produce optimal results, whereas the selection of appropriate parameters will be able to improve the accuracy of the results produced [17]. ...
... The transformation process is carried out using min-max normalization, as shown in (1). This normalization can accelerate the learning process involving data in the same scale value [17]. ...
The large number of Indonesians who consume rice as their primary food makes rice price a benchmark for determining the other staple food prices. The instability of rice prices due to climate change or other uncontrollable factors makes it difficult for Indonesians to estimate the rice prices, especially for the poor. This study proposes the usage of the Improved Crow Search Algorithm (ICSA) to optimize the Support Vector Regression (SVR) parameter in building a regression model to predict the price of staple foods. The forecasting process is carried out based on time series data of 11 staples for four years. The proposed ICSA optimizes the six parameters used in the SVR to form a regression model, consisting of lambda, epsilon, sigma, learning rate, soft margin constant, and the number of iterations. Algorithm performance is measured using MAPE and NRMSE by comparing the actual price of staple foods and forecasting results to get the error rate. With this parameter optimization mechanism, the forecasting results given are good enough with a small error value, in the form of MAPE of 17.081 and NRMSE of 1.594. A MAPE value between 10 and 20 indicates that the forecasting result is acceptable, while an NRMSE value of less than 10 indicates that the forecasting accuracy is excellent. The improvised technique on Crow Search Algorithm is proven to improve the performance of Support Vector Regression in forecasting the price of staple foods.
... Because MAPE is less than 10%, it is included in the very good category. Saputra, G. H., et al., in his research also revealed that the linear kernel has a fairly high prediction accuracy and produces relatively small errors in the prediction process [29]. ...
Purpose: Prigi Beach has the largest fishing port in East Java, but the topography of this beach is quite gentle, so it is prone to disasters such as tidal flooding. The tides of seawater strongly influence the occurrence of this natural event. Therefore, information on tidal level data is essential. This study aims to provide information about tidal predictions.Methods: In this case using the SVE method. Input data and time were examined using PACF autocorrelation plots to form input data patterns. The working principle of SVR is to find the best hyperplane in the form of a function that produces the slightest error.Result: The best SVR model built from the linear kernel, the MAPE value is 0.5510%, the epsilon is 0.0614, and the bias is 0.6015. The results of the tidal prediction on Prigi Beach in September 2020 showed that the highest tide occurred on September 19, 2020, at 10.00 PM, and the lowest tide occurred on September 3, 2020, at 04.00 AM. Value: After conducting experiments on three types of kernels on SVR, it is said that linear kernels can predict improvements better than polynomial and gaussian kernels.
... Priliani et al. [8] predict inflation rate based on consumer price index (CPI) using support vector regression (SVR)-based weight attribute particle swarm optimization (WAPSO). They use WAPSO to find the optimal SVR parameters and increase the accuracy for forecasting. ...
Variations of inflation rate possess a diverse influence on the economic growth of any country. Inflation rate control can be accommodated to stabilize the financial aspect’s condition, including the political area. The way to restrain the inflation rate is the prediction of the inflation rate. This paper proposes forecasting the inflation rate by applying machine learning algorithms: support vector regression (SVR), random forest regressor (RFR), decision tree, AdaBoosting, gradient boosting, and XGBoost. These algorithms are employed since the predicting value is nonlinear and complex. Moreover, the regression and boosting algorithms confer good accuracy, as inflation is a frequent dynamic variable that depends on several factors. The models show decent accuracy using the elements consumer price index (CPI), food, non-food, clothing-footwear, and transportation. Among the models, AdaBoost retrospectives the most desirable outcome with the lowest MSE value of 0.041.
... Data deret waktu merupakan jenis data yang sering dikembangkan untuk kasus peramalan. Peramalan yang menggunakan data deret waktu dalam perkembangannya menunjukkan bahwa keakuratan peramalan dapat ditingkatkan dengan menggabungkan beberapa model dengan kombinasi daripada hanya menggunakan salah satu model terbaik [5]. ...
... Maka dari itu metode yang dipilih untuk menormalisasi data adalah metode min-max dikarenakan metode normalisasi ini menskalakan ulang secara Linear data dari satu rentang nilai untuk rentang baru nilai-nilai, seperti [0,1] atau [-1,1]. Proses normalisasi dilakukan sebelum melakukan pelatihan menggunakan SVR dengan menggunakan rumus [5]: ...
PT. XYZ is engaged in the manufacture and sale of wood veneers. Starting from the constant occurrence of over stock, now the company must make improvements to the production forecasting process so that over stock can be avoided. It can be seen that accurate production forecasting can create conditions for an effective and efficient production system. This study aims to obtain a more accurate forecast of material requirements using the Support Vector Regression (SVR) method, which is the result of the development of a Support Vector Machine (SVM) which has good performance in predicting time series data. Application of the Support Vector Regression (SVR) method with the RBF kernel in predicting the need for veneer production using the MATLAB application, it produces the smallest error rate with a MAPE of 5%, RMSE of 4364.63 and of 0.748274147. on 67 training data and 20 testing data.
... The SVR method can also be added with the use of optimization and one of the optimizations is by using Particle Swarm Optimization (PSO). Where the addition of PSO optimization can improve the accuracy of the forecasting done [10]. ...
Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.
