PEMODELAN GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA DATA RETURN INDEKS HARGA SAHAM EURO 50

General Regression Neural Network (GRNN) is one of the network models that is used for the radial basis function approach. GRNN models including neural network models with a quick solution, because it does not need a large iteration in estimation weights. This model has a standard network architecture, where the number of units in the pattern layer in accordance with the amount of input data. One application GRNN is to predict stock returns of the index value of Euro 50 CFD (Contract for Difference). Euro 50 Index CFD (Contract for Difference) is used as the benchmark stock price of the 50 largest companies in the euro zone. The investors to invest in the stock index Euro 50 CFD (Contract for Difference) with expectation of obtaining appropriate rewards back to what has been invested in. GRNN models showed that the value of RMSE and R2 for training data and 0.00095 and 99.19%. For testing the data obtained RMSE and R2 value of 0.00725 and 98.46%. Based on the forecast value of the stock return next twelve days obtained the highest loss or capital loss on December 10, 2014 at 5.583188% and the highest profits or capital gains on December 10, 2014 by 2.267641% Keywords: GRNN, Neural Network , Stock Return, Euro 50, Forecasting, Capital Loss, Capital Gain, Forecastin

PREDIKSI PRODUKSI GAS BUMI DENGAN GENERAL REGRESSION NEURAL NETWORK (GRNN)

Gas bumi sebagai salah satu sumber energi memiliki peranan yang sangat penting bagi pertumbuhan pembangunan nasional. Selama dekade terakhir, peranan gas bumi mulai menggeser peranan BBM sebagai sumber energi karena selain lebih murah juga ramah lingkungan. Pemanfaatan gas bumi di Indonesia meliputi sektor pembangkit listrik 52%, sektor industri pupuk 12% serta sektor industri dan sektro lainnya 36%. General Regression Neural Network (GRNN) merupakan salah satu model jaringan radial basis yang digunakan untuk pendekatan suatu fungsi. Model GRNN termasuk model jaringan syaraf tiruan dengan solusi yang cepat, karena tidak diperlukan iterasi yang besar pada estimasi bobot-bobotnya. Model ini memiliki arsitektur jaringan yang baku, dimana jumlah unit pada pattern layer sesuai dengan jumlah data input. Analisis dilakukan simulasi jaringan dengan menguji 17 data tersisa didapat nilai mse training sebesar 553,9764 dan nilai mse testing gas bumi sebesar 645,870. Kalau saat ini industri meminta pasokan gas hingga lebih dari 2000 mmscfd maka konsekwensinya belum dapat memenuhi kebutuhan industri karena berdasarkan peramalan produksi gas bumi baru dapat menyumbang sebesar 1500 mmscfd (Million Metric Standard Cubic Feet Per Day) atau sebesar 75% dari kebutuhan industri. Sehingga untuk memenuhi kebutuhan industri pemerintah harus mengoptimalkan produksi atau mencari sumber energi gas alternatif. Kata Kunci : Gas Bumi, General Regression Neural Network (GRNN), (Forecasting), Simulasi, Peramala

PEMODELAN TINGGI PASANG AIR LAUT DI KOTA SEMARANG DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM (MODWT)

Penggunaan dekomposisi wavelet untuk pemodelan statistika khususnya pada data time telah mengalami perkembangan yang pesat. Transformasi wavelet yang dipandang lebih sesuai untuk data time series adalah Maximal Overlap Discrete Wavelet Transform (MODWT) karena dalam setiap level dekomposisi terdapat koefisien wavelet dan skala sebanyak panjang data. Kelebihan ini mereduksi kelemahan pemfilteran dengan Discrete Wavelet Transform (DWT) yang tidak dapat dilakukan pada sebarang ukuran sampel. Penentuan level dekomposisi dan koefisien yang digunakan sebagai input model menggunakan dekomposisi multi skala. Dari analisis dapat disimpulkan data pasang surut Kota Semarang dengan menggunakan MODWT didapat nilai MSE minimal diperoleh pada dekomposisi level 4 dan banyaknya koefisien pada level tersebut adalah 5 didapatkan nilai koefisien determinasi R2 sebesar 99.26% Kata Kunci: MODWT, pasang surut, time serie

Biresponses Kernel Nonparametric Regression: Inflation and Economic Growth

The relation between inflation and economic growth is interesting to observe. To maintain the inflation rate, two factors should be taken into account, namely keeping the economic pulse at its optimal rate and keeping people's purchasing power from decreasing. Many factors influence the inflation and economic growth of a nation; one of which is the national bank interest rate. Since the data of inflation are closely related to economic growth, this study aims at modelling the data of inflation rate and economic growth of Central Java Province in Indonesia using bi-response kernel regression. Employing the data from the first trimester of 2007 up to those from the second trimester of 2019 which were processed using kernel Gauss, the best model to minimise the value of GCV was obtained with optimum h for inflation model amounting to 0.12 and 81.75 for economic growth model. The model performance was excellent because the MAPE out sample was less than 10%. The biresponses kernel model is better than the linear biresponses model in terms of GCV, MSE, R2, and MAPE values

Persepsi tubuh dan gangguan makan pada remaja

Background: There can no longer be any doubt that adolescents do indeed have body esteem problems. Several types of research seem to suggest that this wrong behavior caused by some negative perceptions as a result of dissatisfaction with the body and a person’s level of self-confidence. Therefore they often do misperceptions of his/her body that can lead to wrong dietary behavior. It’s related to the development of more severe body image and eating-related problems.Objective: This study determined the relationship of body image perception and eating disorders in adolescents.Method: A cross-sectional study was applied in this study. The subjects were 120 new students majoring in a nutritional program in Bogor Agricultural University (IPB). They completed a questionnaire measuring appearance evaluation, appearance orientation, body areas satisfaction, overweight preoccupation, self-classified weight and eating disorders. Multidimensional Body Self-Relations Questionnaire-Appearance Scale (MBSRQ-AS) method is used to assess body image perception and Eating Attitude Test 40 (EAT-40) to predict eating disorders.Results: Descriptive analysis showed nutritional status were categorized as normal (83.3%), overweight (10.0%), obesity (4.2%) and thin (2.5%). With MBSRQ-AS method, most of the adolescents have appearance evaluation (80.0%), appearance orientation (99.2%), body areas satisfaction (80.8%), self-classified weight (71.7%) were categorized as negative, whereas overweight preoccupation (57.5%) were categorized as positive. There was (7.8%) female subject with eating disorders with more risk or have attitude the desire to eat continuously and can’t stop eating (2-3 times a month).Conclusion: Statistically using a significance level (α=5%) obtained a significant correlation between body image perception in overweight preoccupation subscale with eating disorders. However, there were no significant correlations for other subscales

Neurocomputing fundamental climate analysis

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

SHORT COMMUNICATION: COVID-19 Pandemic and Attitude of Citizens in Bandung City Indonesia (Case Study in Cibiru Subdistrict)

In the beginning, the pandemic panicked the people of Cibiru. Over time, the case fell in line with the increasing number of patients recovering. In addition, different views between elements of government make people surrender and believe in the power of nature's creator. Under these conditions, the researchers were interested in learning more. The study was conducted using a descriptive analysis of a number of parties regarding economic and social activities. The results show that there are three important components: First, trust builds the creator and reduces to the government component, communication that a number of parties do not work consistently when responding to COVID-19, and enforcement of unclear rules. In a nutshell. The citizens, grouped into two groups, agree that a pandemic is dangerous and urge them to follow values in the form of existing rules. Also,The pandemic communication competes in a short time and therefore cannot be carried out interactively.The government’s assertiveness of forcing residents to be at home becomes difficult as compensation can be granted for lost opportunities to seek family income Lastly, due to the preparation of the strategy that precedes the arrival of a pandemic, it cannot be face wisely

Social Vulnerability and How It Matters: A Bibliometric Analysis

Interdisciplinary and cross-cultural studies of the impacts of environment and social vulnerability must be undertaken to address the problem of social vulnerability in the foreseeable future. Scientist or social scientists should first continuously strive towards approaches can integrate municipal technological expertise, experiences, knowledge, perceptions, and expectations into emergency circumstances, so that people can be sharper on issues and offer responses with their matters. In this paper. We performing the Bibliometric Analysis to review published papers on the keyword 'Social Vulnerability'. There are 29,468 papers published in the last 52 years from 1969 to November 2020. Disaster research by implementing the Internet of Things (IoT), data mining, machine learning is still needed

The step construction of penalized spline in electrical power load data

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%