Analisis Data Inflasi Di Indonesia Menggunakan Model Regresi Spline

The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. The developed model of parametric to cope with the volatility of the data is the ARCH and GARCH models. This alternative parametric models still requires the normality assumption in the data that often cannot be satisfied by financial data. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This research aims to conduct a study in Indonesia inflation data modeling using nonparametric methods is spline regression model with truncated spline bases. Goodness of a spline regression model is determined by an orde and knots location . However, the knots location are more dominant in spline regression model. One way to get the optimal knots location are by minimizing the value of Generalized Cross Validation (GCV). By modeling the annual inflation data of Indonesia in December 2006 - December 2011, the inflation target in 2012 is 4.5% + 1% can be achieved while the inflation target in 2013 is 4.5% + 1% cannot be achieved, because that prediction in 2013 is 8.55%. It was caused by government policy to raise the price of basic electricity and the fuel prices in 2013

Pemodelan Data Inflasi Indonesia pada Sektor Transportasi, Komunikasi, dan Jasa Keuangan Menggunakan Metode Kernel dan Spline

In this research, we study data modeling of Indonesian inflation in the transportation, communication and financial services sector using the kernel and spline models. Determination of the optimal models based on the smallest of GCV value and determination of the best model based on the smallest out sampels of Mean Square Error (MSE) value. By modeling the yoy (year on year) inflation data in Indonesia in the transportation, communication and financial services sector In January 2007 to January 2015, shows that the kernel model using Gaussian kernel function obtained optimal model with a bandwidth 0.24 and the optimal spline model with order 5 and 4 points knots. Based on out sampels data in February to August 2015, obtained out sampels MSE value of the spline model is smaller than the kernel model. So that the spline model is better than the kernel model to analyze the inflation data of transportation, communication and financial services sector

Pemodelan Regresi Nonparametrik Menggunakan Pendekatan Polinomial Lokal pada Beban Listrik di Kota Semarang

Semarang is the provincial capital of Central Java, with infrastructure and economic's growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)-th. Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672

Perhitungan Suku Bunga Efektif Untuk Penentuan Alternatif Pembiayaan Kendaraan Motor Pada Leasing Dan Bank Dengan Metode Interpolasi Linier (Studi Kasus Harga Sepeda Motor Honda Beat Injeksi Terdaftar Bulan September 2014)

Imposition of interest rates by the bank and leasing in providing credit is different. The interest rate usually not included in the brochure loan installments. The calculation of the interest rate can be calculated using the flat rate and the effective interest rate. In the calculation of the effective interest rate can be performed using linear interpolation. Determination of the motorcycle financing alternative most favorable to the customer, can be seen from the lowest interest rates charged. The results of the case study Honda Beat injection prices listed September 2014 on credit motorcycle through leasing Central Sentosa Finance (CSF), leasing Adira Multifinance (Adira) and credit through Bank Rakyat Indonesia showed the lowest interest rate on the lease Central Sentosa Finance (CSF). In addition to low interest rates charged are other benefits that the filing procedures quickly and without collateral (guarantee)

Analisis Faktor-faktor Yang Mempengaruhi Keputusan Penggunaan Transportasi Pribadi Pada Mahasiswa Menggunakan Pendekatan Partial Least Square (Studi Kasus Pada Universitas Diponegoro Semarang)

The process of structural development in developing countries is a must. Each sector that developed is related to one another. These sectors associated with the supporting factor named transport, means transport has a vital and strategic functions in the development of other sectors. Education is one of the construction sector that growing rapidly, especially in big cities, and transportation is one of the factors supporting it: since schools and universities is one of the important generator of domestic transportation network. Each university holds up to tens of thousands of new college students every year. In this point, the transport activity in big cities is becoming increasingly complex, due to the increase in the private transportation is not matched by the increase in roads, causing congestion. Factors that influence the decision of the use of private transport on the student comprehensively analyzed using structural equation based on the variance, Partial Least Square (PLS). PLS is a powerful analytical method, though it's not based on many assumptions (soft model), for example, the multivariate normal assumptions, it can use nominal scale up to ratios, as well as the sample size shouldn't be large. PLS estimates the model od relationship between latent variables and also latent variables with the indicator. Based on the analysis we concluded that the decision on the use in private transportations of Diponegoro University students affected by a combination of latent variables such time management, cost, physical, social interaction, and the intervening variable perception of 68.28%