Multi-input Transfer Function Research Articles

THE BIBLIOMETRIC NETWORK TO IDENTIFY RESEARCH TRENDS IN MULTI-INPUT TRANSFER FUNCTION

Bibliometrics has become one of the most widely used approaches to examining academic publications. Bibliometric analysis helps researchers obtain references from other researchers, strengthening recent research. In a bibliometric analysis, VOSviewer made the analysis process easier. VOSviewer is used to build and visualize bibliometric networks. This study aims to present and visualize an overview of the development of multi-input transfer function research trends and collaboration networks. The research method used is a literature review from Scopus using VOSviewer in 2007-2023. This study shows a growing trend in multi-input transfer function research, with China leading in publications. The development map of the multi-input transfer function is based on nine co-words and ten co-author clusters. The study's results are dominated by applying the model to real data. However, the estimation of multi-input transfer function parameters has yet to be carried out using a numerical approach.

Analysis of Residential Property Price Index (RPPI) Using Multi Input Transfer Function

Residential property or home is one of the basic human needs as a shelter, to be able to continue to live. As the population in Indonesia increases, express the need for residential property will also increase. The movement of residential property prices in Indonesia can be observed from the Residential Property Price Index (RPPI) issued by Bank Indonesia. Residential property prices can be influenced by several factors. For example in economic, the movement of RPPI can be influenced by several factors such as inflation, Composite Stock Price Index (RPPI) and loan interest rates. Forecasting the RPPI can be done with times series modeling. Modeling RPPI using three influencing variables requires a multivariate time series model. The multi-input transfer function model is a multivariate model that can be used in modeling RPPI. In the multi-input transfer function model there is an output series (y t ) which is the RPPI which is estimated to be influenced by several input series (x jt ), which are inflation, IHSG and loan interest rates. Based on the model obtained in this study, it can be seen that the prediction of RPPI at the t-time is influenced by the amount of inflation in the previous two months until previous five months, influence by loan interest rates without delay so that it is influenced by the t-time until previous three months, influenced by the IHSG at t-time until previous four months, and also influenced by itself at one month before to four months before.

PERAMALAN DATA INDEKS HARGA KONSUMEN KOTA PURWOKERTO MENGGUNAKAN MODEL FUNGSI TRANSFER MULTI INPUT

The Consumer Price Index (CPI) provides information on changes in the average price of a group of fixed goods or services that are generally consumed by households within a certain period of time. The General CPI is formed from 7 sectors of public consumption expenditure groups. Because the formation of the consumer price index value is influenced by several sectors, the method that can be used is the transfer function method. The purpose of this study is to analyze the transfer function model so that the best model is produced to predict CPI in Purwokerto for the next several periods. In this study, general CPI modeling will be carried out based on the CPI value for the transportation services sector and the CPI for the Health sector in Purwokerto from January 2014 to July 2019 using the multi-input transfer function method. Based on the analysis, the best models are obtained, namely the multi-input transfer function model (2,0,0) (0,1,0) and the ARIMA noise series ([3], 0,0). The model has an Akaike's Information Criterion (AIC) value of 72.42021 and an sMAPE value of 2,351591 % which indicates that the model can be used for forecasting..Keywords: Consumer Price Index (CPI), Inflation,transfer function, AIC

MODEL FUNGSI TRANSFER MULTI INPUT DALAM PERAMALAN PENERIMAAN PAJAK HOTEL KOTA MALANG TAHUN 2019

A transfer function is one of the models contained in the time series. The multi-input transfer function is used to establish a simple model derived from the relationship between input and output series. The transfer function model will be applied to the forecasting of hotel tax revenue (Y) into an output series with the input series variables affected by the number of sold rooms (X^a), the number of staying guests (X^b), and the average of the guest’s staying time (X^c). The purpose of this study is to anticipate the failure of the realization of the hotel tax revenue target in Malang City and to determine the results of the next forecasting. The result of this study is the multi-input transfer function, Y_t = Y_(t-1) + 0.0052294*X^b_(t-8) + 573.32180*X^c_t - 0.0052294*X^b_(t-9) - 573.32180*X^c_(t-1) + 1.18967*a_t. Based on this function, the forecasting nearly coincides with the actual data indicated by the graph of forecasting result nearly coincides with the graph of the actual data. Thus, this multi-input transfer function could be suggested to be used in forecasting Malang City hotel tax revenues from January 2019 to May 2019.

PERBANDINGAN ANTARA MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN MODEL FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN DI KABUPATEN MANOKWARI

This study discusses the comparison of forecasting time series data between the Autoregressive Integrated Moving Average (ARIMA) method and the multi input transfer function model. ARIMA method is one of the most frequently used methods for forecasting time series data. Meanwhile, the transfer function model is a combination of the characteristics of multiple regression analysis with the characteristics of the ARIMA time series. Meanwhile, the multi input transfer function model is a transfer function model that has input variables of more than two time series. The application of these two methods is carried out on rainfall data from January 2012 to December 2017 in Manokwari Regency, West Papua Province. The input variables used are temperature, humidity, solar radiation, air pressure, and wind speed variables. The results showed the best ARIMA model was ARIMA (1,0,0) (2,0,0) 12 with an AIC value of 910.07, while for the best multi input transfer function model was ARIMA (1,1,0) AIC value of 898.24. Between the two methods, the best model used to forecast rainfall in Manokwari Regency, West Papua Province is the multi-input transfer function model (1,1,0).

Modelling of Multi Input Transfer Function for Rainfall Forecasting in Batu City

The aim of this research is to model and forecast the rainfall in Batu City using multi input transfer function model based on air temperature, humidity, wind speed and cloud. Transfer function model is a multivariate time series model which consists of an output series (Yt) sequence expected to be effected by an input series (Xt) and other inputs in a group called a noise series (Nt). Multi input transfer function model obtained is (b1,s1,r1) (b2,s2,r2) (b3,s3,r3) (b4,s4,r4)(pn,qn) = (0,0,0) (23,0,0) (1,2,0) (0,0,0) ([5,8],2) and shows that air temperature on t-day affects rainfall on t-day, rainfall on t-day is influenced by air humidity in the previous 23 days, rainfall on t-day is affected by wind speed in the previous day , and rainfall on day t is affected by clouds on day t. The results of rainfall forecasting in Batu City with multi input transfer function model can be said to be accurate, because it produces relatively small RMSE value. The value of RMSE data forecasting training is 7.7921 while forecasting data testing is 4.2184. Multi-input transfer function model is suitable for rainfall in Batu City.