Judul: Peramalan Data Deret Waktu Menggunakan Support Vector Regression berdasarkan Berbagai Tipe Kernel
Abstrak: Support vector regression (SVR) is a nonlinear method that uses the principle of risk minimization. SVR model uses the kernel function with regularization constant (C and ε). The kernel function which is commonly used in the SVR are linear, polynomial and radial basis function (RBF). Determination of kernel with its constants is required in SVR model. Grid search algorithm determines the best regularization constant combination based on the smallest error rate. SVR is able to replace the case of linear regression and forecasting. The accuracy of forecasting is very important. The type of time series data is divided into several models, i.e. constant, trend, polynomial, and seasonal model. The study simulated various model of time series data using SVR. Model that had been tested was evaluated by Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The simulation found that SVR model which used RBF kernel on the four types of time series data performed the best forecasting accuracy. In the application data, the RBF kernel with C of 10 and ε of 0.04 was implemented to build the SVR model. In the training data, the best SVR model produced MAE of 7.15436, RMSE of 10.03607 and MAPE of 0.86904%, while in the testing data the model produced MAE of 6.67387, RMSE of 9.41013 and MAPE of 0.865755%. Even if the testing data produced smaller error compared to training data, the difference of errors in the training and testing data was not statistically significant.
Keyword: Statistik, Regression, Kernel, stocks, SVR, time series

Judul: Performance Comparison of SVR, Random Forest dan ARIMA on Data Time Series Analysis (Study Case of Poverty at the District/City Level)
Abstrak: Perkembangan analisis deret waktu semakin cepat, pesat dan telah banyak mengalami perkembangan dan pembaruan dengan temuan terbaru. Metode klasik yang umum digunakan dalam analisis deret waktu adalah Autoregressive Integrated Moving Average (ARIMA). Selain itu, machine learning dapat digunakan pada data analisis deret waktu untuk menangani data yang bersifat tidak stasioner, tidak linier dan overfitting serta menghasilkan akurasi yang tinggi. Contohnya adalah Support Vector Regression (SVR) dan random forest. Salah satu manfaat dari analisis deret waktu adalah memprediksi angka kemiskinan di Indonesia yang berguna bagi pemerintah untuk membuat kebijakan yang tepat agar tidak menghambat pertumbuhan ekonomi. Penelitian ini bertujuan untuk menentukan model yang terbaik berdasarkan metode SVR, random forest dan ARIMA. Data yang digunakan adalah data tingkat kemiskinan di tingkat kabupaten/kota di Pulau Jawa dari tahun 2010 hingga 2022 dengan fokus penelitian Kabupaten Karawang, Kota Bogor dan Kota Sukabumi. Hasil penelitian menunjukkan bahwa metode yang memiliki performa yang terbaik adalah SVR dengan kernel Radial Basic Function (RBF).
Keyword: ARIMA, poverty, random forest, SVR, time series

Judul: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
Keyword: