Yuciana Wilandari
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PERBANDINGAN ARIMA DENGAN FUZZY AUTOREGRESSIVE (FAR) DALAM PERAMALAN INTERVAL HARGA PENUTUPAN SAHAM (Studi Kasus pada Jakarta Composite Index) Muhammad Fitri Lutfi Anshari; Dwi Ispriyanti; Yuciana Wilandari
Jurnal Gaussian Vol 2, No 3 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (394.047 KB) | DOI: 10.14710/j.gauss.v2i3.3665

Abstract

The capital market is one of the most popular investment option today. In capital market, stock price prediction is an important issue for investors, so needed a good forecasting method as a basic for decision-making for the transaction. One of the most popular forecasting method is ARIMA, but this method still uses the concept that measurement error which is obtained from the difference between the observed values with estimated values. To resolve the error in modeling, Fuzzy Autoregressive was developed, it is a model combination of Fuzzy Regression and Autoregressive (AR). This method gives results in interval forecasting, thus providing information to decision makers regarding the best and worst situation that may occur. This paper discusses the application of Fuzzy Autoregressive forecasting interval for the Jakarta Composite Index and compare it with the ARIMA prediction interval. The result of this study is Fuzzy Autoregressive interval is narrower than the ARIMA 95% significance rate
ANALISIS CONJOINT PAIRWISE-COMPARISON UNTUK MENGETAHUI TINGKAT KEPENTINGAN ATRIBUT JASA BIRO PERJALANAN WISATA (Studi Kasus Beberapa SMA Negeri di Kabupaten Klaten) Galih Maraseta W H Prasaja; Yuciana Wilandari; Sudarno Sudarno
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (311.541 KB) | DOI: 10.14710/j.gauss.v3i3.6450

Abstract

Competition in the business world travel agency today's increasingly stringent. Travel agency is a business that organizes tourist activities and other services related to the operation of the tour both domestically and abroad in a foreign country. To help business people of the travel agency in knowing and understanding consumer preferences on a combination of attributes of a travel agency conjoint analysis can be used. In this study conjoint analysis is used by using presentation method of pairwise-comparison. There are four attributes used in this analysis, they are bus facilities, agency facilities, hotel, and dining facilities. From the results of the analysis that obtained by the respondents, the most important attribute in selecting a travel agency is the dining attribute with a relative importance value of 38,02%. The next most important attribute according to the respondents is the attribute of the bus facility with a relative importance value of 28,46%, attributes agency facilities with a relative importance value of 19,58%, attributes the hotel facilities with a relative importance value of 13,94%. The combination of desired respondents in choosing or use the services of a travel agency is a travel agency with wifi bus facilities, hotel facilities with the large bed, an agency facility of video documentation and a buffet meal. 
PERBANDINGAN MODEL ARIMA DAN FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN KABUPATEN WONOSOBO Siti Lis Ina Atul Hidayah; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (217.392 KB) | DOI: 10.14710/j.gauss.v4i4.10239

Abstract

Rainfall is one of the things that affect agricultural production. The highest amount of rainfall will cause perturbation in the pollination of flowers and caused zalacca palm to produce fruits no season of the year. Zalacca palm is growing well in heavy rainfall area.. There are some factors which influence rainfall; those are: humidity, solar energy, wind direction and velocity as well as air temperature.  The application of ARIMA (Autoregressive Integrated Moving Average) and multi input transfer function was intended to model the rainfall which would be forecasted based on the best model chosen. There were two kinds of variables used in this study. Those were rainfall as the output series while humidity and air temperature as the input series during January 2009 to October 2014. The result showed that ARIMA ([3], 1, [12]) had a fewer Schwart’z Bayesian Criterion (SBC) value 293.199 than multi input transfer function model (0,0,0) (0,1,0) with the result 906.9632.Keywords: Rainfall, ARIMA, Transfer Function
ANALISIS SUMBER-SUMBER PENDAPATAN DAERAH KABUPATEN DAN KOTA DI JAWA TENGAH DENGAN METODE GEOGRAPHICALLY WEIGHTED PRINCIPAL COMPONENTS ANALYSIS (GWPCA) Alfiyatun Rohmaniyah; Hasbi Yasin; Yuciana Wilandari
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (595.422 KB) | DOI: 10.14710/j.gauss.v3i3.6438

Abstract

The districts/cities  sources of revenue  in Central Java consists of Natural Revenue District (PAD), the equalization fund (DAPER), and other local income. PAD consists of four variables namely local tax (X1) , retribution (X2) , the results of regional company and wealth management that is separated (X3) , and other legal PAD (X4). DAPER consists of four variables namely sharing of tax revenue (X5) , sharing of non-tax revenue (X6) , the general allocation fund (X7) , and the special allocation fund (X8). Other region revenues (X9) is a source of local income that is not included in the PAD or DAPER. Sources of local revenue variables are mutually correlated multivariate data and have spatial effect. Therefore Geographically Weighted Principal Components Analysis (GWPCA) is suitable for analyzing sources of local revenue variables. GWPCA is a multivariate analysis method that is used to eliminate multicolliniearity in the multivariate data that have spatial effect. The result of this study is that the variables of revenue sources on each location can be replaced by three new variables called PC1, PC2, and PC3 which is independent each other. Variance Cumulative Proportion that can be explained by those new variables is approximately 80%. Based on the first principal component (PC1) that have variance proportion approximately 50%, there are three groups which has different carracteristics. The first group is the region that the revenue have influenced by variables X9 followed by X1. The second group is the region that the revenue have influenced by variables X9 followed by X2. The third group is the region that the revenue have influenced by variables X9 followed by X5. It is also seen that Kudus District has the most distinct characteristics which the revenue are influenced by variables X5 followed by X9.
APLIKASI METODE PUNCAK AMBANG BATAS MENGGUNAKAN PENDEKATAN DISTRIBUSI PARETO TERAMPAT DAN ESTIMASI PARAMETER MOMEN-L PADA DATA CURAH HUJAN (Studi Kasus : Data Curah Hujan Kota Semarang Tahun 2004-2013) Tyas Estiningrum; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (456.846 KB) | DOI: 10.14710/j.gauss.v4i1.8154

Abstract

The rainfall with very high intensity cause a lot of problem like flood, landslide and be a factor restricting of flight aircraft at the airport. One of the methods that can be use to analyze such extreme events is Peak Over Threshold (POT) with distribution approach Generalized Pareto Distribution (GPD) include in the Extreme Value Theory (EVT). L-Moment method used for estimation of scale and shape parameter from GPD. In this research, data used is daily rainfall data of the Semarang city in 2004-2013 that recorded at the Meteorological Station of Class II Ahmad Yani Semarang. Daily rainfall data is analyzed each year during the rainy season. Result of analysis of the data shows rainfall there are heavy tail that indicates there is a possibility of occurrence extreme value. Return level obtained indicated occurrence of precipitation with very high intensity for the period of rainy season in 2006/2007, 2009/2010, 2010/2011, 2011/2012, 2012/2013 and 2013/2014 with intensity of rainfall 117,1905730 mm/day, 118,6389421 mm/day, 106,5032441 mm/day, 107,2133094 mm/day, 108,2262353 mm/day dan 111,2356887 mm/day.Keyword : Rainfall, Peak Over Threshold, Generalized Pareto Distribution, Extreme Value Theory, L-Moment, Return level.
PERBANDINGAN METODE REGRESI LOGISTIK BINER DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA PEMINATAN JURUSAN SMA (Studi Kasus SMA Negeri 2 Semarang) Ratih Binadari; Yuciana Wilandari; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (569.953 KB) | DOI: 10.14710/j.gauss.v4i4.10234

Abstract

Major specialization at High School is aimed to gives opened opportunity for students to choose subject that are interest and develop their potential in accordance with the abilities, interests, talents, and personality. Major specialization at High school is influenced by some factors. To detect those factors, used biner logistic regression method and Multivariate Adaptive Regression Spline (MARS). Biner Logistic Regression is method that describes relationship between dependent variable and some independent variable, with independent variable has been coded 1 as representing the presence of the characteristic, and 0 as representing the absence of the characteristic. MARS is multivariate nonparametric regression method that development of Recursive Partitioning Regression (RPR) method and Spline method for high dimensional data that produces accurate prediction and continuous models on knots. Both of the methods are compared to know the best method used in research. From the result of analysis using biner logistic regression method and MARS, concluded that major specialization has been influenced by mathematic score, science score and relationship between students and friends. From proportion test, concluded classification that formed by regression logistic is as good as by MARS. Keywords : Major specialization at High School, Biner Logistic Regression, Mutlivariate Adaptive Regression Spline (MARS), Clasification