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Journal : TELKOMNIKA Telecommunication, Computing, Electronics and Control

The step construction of penalized spline in electrical power load data Caraka, Rezzy Eko; Bakar, Sakhinah Abu; Anuraga, Gangga; Mauludin, M. A.; Anwardi, Anwardi; Pomalingo, Suwito; Rosalina, Vidila
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (697.286 KB) | DOI: 10.12928/telkomnika.v17i2.8460

Abstract

Electricity is one of the most pressing needs for human life. Electricity is required not only for lighting but also to carry out activities of daily life related to activities Social and economic community. The problems is currently a limited supply of electricity resulting in an energy crisis. Electrical power is not storable therefore it is a vital need to make a good electricity demand forecast. According to this, we conducted an analysis based on power load. Given a baseline to this research, we applied penalized splines (P-splines) which led to a powerful and applicable smoothing technique. In this paper, we revealed penalized spline degree 1 (linear) with 8 knots is the best model since it has the lowest GCV (Generelized Cross Validation). This model have become a compelling model to predict electric power load evidenced by of Mean Absolute Percentage Error (MAPE=0.013) less than 10%.
Neurocomputing fundamental climate analysis Caraka, Rezzy Eko; Bakar, Sakhinah Abu; Tahmid, Muhammad; Yasin, Hasbi; Kurniawan, Isma Dwi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (884.937 KB) | DOI: 10.12928/telkomnika.v17i4.11788

Abstract

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively.