Nuning Kurniasih

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.

Hubungan perilaku pencarian informasi guru militer dengan hasil pembelajaran pembekalan angkutan TNI AD Cimahi

Information needs are necessary for humans to answer questions that are in the mind, in order to enhance their professions or main activities. The needs of each person are influenced by different situations, cognitions and physiological backgrounds. All aspects of human life require information which is expected to continuously support the improvement of life patterns toward increasing complexities. The purpose of this study was to identify and analyze the relationship of information seeking behavior by military instructors with the learning outcomes of transportation briefing of the Indonesian Army in Cimahi. This study used the correlational research method with the Spearman rank statistical test; study results showed: 1) a positive relationship between the information seeking behavior of the initiation phase and learning outcomes of transportation briefing; 2) a positive relationship between the information seeking behavior of the selection phase and the learning outcomes of transportation briefing; 3) a positive relationship between the information retrieval behavior of the exploration stage and the learning outcomes of transportation briefing; 4) a positive relationship between the information seeking behavior of the formulation stage and the learning outcomes of transportation briefing; 5) a positive relationship between the information collection stage collection behavior and the learning outcomes of transportation briefing; and 6) a positive relationship between the information seeking behavior of the presentation stage and the learning outcomes of transportation briefing. In conclusion, there is a positive relationship between information seeking behavior by military instructors and the learning outcomes of transportation briefing.

Sensitivity Analysis with AHP Method: Selection of Foundation Design on Bridge Sei Muara Bulan Anak 2

The selection of the bridge is fundamental thing to Sei Anak Muara Bulan 2 Bridge Planning. After knowing the results of capacity analysis of land and pile support capacity, ease of implementation, determine the selection of foundation planning decisions. The results of AHP implementation indicate that the aspects of land and pile carrying capacity are the main priority criteria with the amount 61.7% as the reason to determine the choice of bridge foundation and the result of alternative is the foundation of equal to 42,2%

Forecasting Infant Mortality Rate for China: A Comparison Between α-Sutte Indicator, ARIMA, and Holt-Winters

The purpose of this study is to apply the α-Sutte Indicator in forecasting data. α-Sutte Indicator is a new forecasting method that was developed in 2017 by Ansari Saleh Ahmar. To see the accuracy of these methods, the forecasting results of the α-Sutte Indicator will be forecasting methods compared to other items, namely: ARIMA and HoltWinters. Based on the results of forecasting, it is found that α-Sutte Indicator has MSE and MAPE values that are lower than other methods (ARIMA and HoltWinters). This is supported by MSE data from α-Sutte Indicator smaller than ARIMA(2,2,2) and HoltWinters i.e. 0.03; 3.06; and 3.15.

Forecasting Historical Data of Bitcoin using ARIMA and α-Sutte Indicator

The purpose of this study is to apply the α-Sutte Indicator and ARIMA in forecasting data. α-Sutte Indicator is a new forecasting method that was developed in 2017 by Ansari Saleh Ahmar. To see the accuracy of these methods, the forecasting results of the α-Sutte Indicator will be forecasting methods compared to other items, namely: ARIMA. Based on the results of forecasting, it is found that α-Sutte Indicator has MSE and MAE values that are lower than other methods (ARIMA). This is supported by MSE data from α-Sutte Indicator smaller than ARIMA(1,1,1).

E-Business, Airport Development and Its Impact on the Increasing of Information of Communication Development in Indonesia

The increasing number of the internet usage by households have an effect on the tourism sector. On the other hand, the aviation industry is growing as one of the development centers. This study aims to analyze the impact of information and communication development to airport performance in Indonesia. This is a correlation research with 151 of airports in Indonesia as a population. The sampling technique was done by using total sampling. The results of correlation (R) indicates the Gross Regional Domestic Product (GRDP) of Information and Communication has a relatively strong relationship with the Airport Performance. Meanwhile the results of Adjusted R test shows that they are other factors in increasing GRDP of Information and Communication besides Airport Performance. It shows the low impact of Information and Communication GRDP to the Airport Performance.

E-Business, The impact of the Regional Government Development (APBD) on Information and Communication Development in Indonesia

The decline of development delived from natural resources in several regions in Indonesia requires local governments to make a breakthrough in increasing revenues beyond natural resources, including through increased revenue in the field of information technology. The purpose of this study is to analyze the impact of the regional government development (APBD) on information and communication development in Indonesia. By using correlational research, it is known that all of APBD sub variables have a high correlation in increasing Gross Regional Domestic Product (GRDP) of Information and Communication in Indonesia, only sub-variable of surplus/deficit development, unexpected development sub-variable, subvariable of development aid to province/regency/city and village government, and sub-variable of development special allocations that have not a significant correlation to increase of GRDP of Information and Communication in Indonesia. There is 86,7% of APBD variable influence the increase of GRDP of Information and Communication, while the increase of 15,3% of Information and Communication GRDP influenced by other factors aside from APBD. It shows the strong impact of APBD on increasing GRDP of Information and Communication

Implementation of the ARIMA(p,d,q) method to forecasting CPI Data using forecast package in R Software

The Consumer Price Index is an index which calculates the average change in prices over a period, of a set of goods and services consumed by the population/households within a specific time. In this paper will be discussed about the forecasting of consumer price index of Indonesia using forecast package with R Software. The forecasting process this data using algorithms popularized by Rob J. Hyndman and Yeasmin Khandakar in 2008. By using this method, it is obtained a suitable ARIMA model to forecast CPI data Indonesia. The model most suitable time series is ARIMA (1,0,0).

Accuracy of Periodogram Analysis for Identification of Multiplicative SARIMA Models

In everyday life, seasonal events are common, seasonal behavior is common in various periods, such as daily, weekly or monthly. business, economics and financial cases for example, we often encounter a seasonal phenomenon, namely data that repeated over the same period. However, this seasonal test is only accurate For stationary Seasonal time series data. So, in this research we apply exact identification for multiplicative time series data from generated data. Performance of this test is determined by the percentage of fit identification. We apply this Periodogram Analysis to real data, AirPassengers and UKgas data. All simulation and real data analysis were done by open source Software R (OSSR). It is observed that in all model for Period 12, accuracy of detection seasonal models under 50%. However, for P=6, The algorithm was accurate enough to detect multiplicative-seasonal models, 70% of seasonal model can be detected by the algorithm.

Business Centre Development Model of Airport Area in Supporting Airport Sustainability in Indonesia

Airport is expected to play the role in enhancing the economic level of the region, especially the local people around the airport. The Aero City concept in developing an airport might also develop a city centreed in the airport that combining airport oriented business development, business actors and local people around the airport area. This study aims to generate development model of business centre at the airports in Indonesia. This is a mixed method based study. The population includes 296 airports under government management, government subsidiary and military. By using stratified random sampling, there were 151 sample airports. The results show that business centre development in the airport area will be related with the airport management and the commercial property (business centre) growth at the airport. Aero City in Indonesia can be developed by partnership system between government and private sector that consists of construction, development, and implementation of commercial property such as hotel, apartment, retail, office, etc. Based on the result of T-Value test, Airport Performance variable predicted to have significant influence on Gross Regional Domestic Product Central Business District performance.