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Journal : PROSIDING SEMINAR NASIONAL

ANALISIS PERSEPSI MASYARAKAT TERHADAP KUALITAS PELAYANAN KEPENDUDUKAN DAN CATATAN SIPIL KOTA TEGAL Indah Manfaati Nur; M.Saifuddin Nur
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

The study is focused on analysis of community perception on quality of population services and civil registration of Tegal city. Based on several analysis results above can be concluded that Gap analysis shows the overall gap mark is negative, meaning the quality of service at the Department of Population and Civil Registration has not reached the quality expected by the community. More specifically in the service indicator the service features that have the greatest coefficient on all service units. The average comparison test between the perception of service quality and the quality expectation of the community indicates that there is no significant difference, meaning that the quality of service provided with the expected quality of expectations by the community does not differ from the good of the service. The weighted average index value for the overall service unit is very good category, which means that in general all the services in the Department of Population and Civil Registry are assessed to have excellent perception quality by the people of Tegal City. Keywords:   Service Quality, Department of Population and Civil Registration, Tegal City
PEMODELAN MEAN SEA LEVEL (MSL) DI KOTA SEMARANG DENGAN PENDEKATAN REGRESI NONPARAMETRIK DERET FOURIER Tiani Wahyu Utami; Indah Manfaati Nur
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Publikasi Hasil-Hasil Penelitian dan Pengabdian Masyarakat
Publisher : Universitas Muhammadiyah Semarang

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Abstract

The statistical method used to estimate or estimate sea level is by nonparametric regression approach of Fourier series. The problem of flooding due to rising sea levels in Semarang includes problems that have not been solved yet. This resulted in the need for modeling to predict and find out how high the average rising sea level. Fourier series have a fluctuative data pattern due to its periodic nature. This makes the Fourier series as an appropriate approach for modeling the mean sea level or called the Mean Sea Level (MSL). Before modeling the MSL data with fourier approximation approach, first determine the optimal K value, based on optimal K determination with GCV method obtained K = 277. The result of MSL modeling on tide data of Semarang City with Nonparametric Regression approach Fourier R2 obtained R2 of 95% and MSE = 4,42. Maximum MSL modeling results or average sea level experienced maximum tides occurred on 31 August 2016 and minimum MSL or so-called mean sea level experienced minimum receding occurred on March 2, 2016.Keywords: MSL, Nonparametric Regression, Fourier Series
FOURIER SERIES NONPARAMETRIC REGRESSION FOR THE MODELIZING OF THE TIDAL Tiani Wahyu Utami; Indah Manfaati Nur; Ismawati -
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Proceeding 3rd ISET 2017 | International Seminar on Educational Technology 3rd 2017
Publisher : Universitas Muhammadiyah Semarang

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Abstract

The method of statistic used to estimate the estimation of sea water level is by nonparametric regression approaching of Fourier series. The rob flood caused by sea level rise in Semarang becomes a dissolved problem until today This results the need of modeling to predict and know how high sea level is.The fourier series have fluctuative data pattern because of its periodic character. This makes Fourier series as the appropriate approaching to modelize the sea tidal. Before modelizing the sea tidal with Fourier series approaching, It is previously necessary to find the optimal K value . Based on the determination of optimal K value, with GCV method, It is obtanied K equals 277. The result of average data of the Semarang sea tidal with reggression nonparametic method showed that R 2 is 95% and MSE = 4,42. The lowest tidalestimation resulted in Semarang is on March 2, 2016. Then the highest tidal estimation in Semarang Cityoccurred on August 31, 2016. Keywords : Nonparametric Regression, Fourier Series, Tidal Sea
PEMODELAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) PROVINSI JAWA TENGAH DENGAN PENDEKATAN SPASIAL AUTOREGRESSIVE MODEL PANEL DATA Ulfatun Khasanah; Abdul Karim; Indah Manfaati Nur
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Modeling spatial is modeling that deals with approach point and area .While regression analysis data panel is regression analysis based on data panel toobserve the relation between one variable bound ( dependent variables ) withone or more variables free ( independent variables). Approach used is themodel spatial autoregressive (SAR). Model spatial autoregresive is a modelcombined model regression simple with lag spatial on the variables ofdependent using data cross section . The data used was data gdp and factorswhich influence it namely the local revenue, population, and investment 20112015year Based on Hausman test for the above model SAR is hisq = 0.50389, df = 3, p-value = 0.918. This means that p-value> 0.05 thus, the selected model is SARrandom effects.Keywords :PDRB, Spatial Panel, Spatial Autoregressive (SAR)
PEMODELAN REGRESI RIDGE PADA KASUS CURAH HUJAN DI KOTA SEMARANG Maulana Afham; Indah Manfaati Nur; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Rainfall is the amount of water that falls on the surface of the flat ground for acertain period measured in units of height (mm) above the horizontal surface. Theclassification of rainfall is divided into thick, medium, and light. Based on data of2016 Semarang city rainfall for 6 years experienced a significant decrease andincrease. With the data of rainfall Semarang city is very high potential for flooding.Semarang rainfall data tend to be unstable then it will cause problems in rainfalldata. Therefore it is necessary to solve the problem in rainfall data. The purpose ofthis study is to model and know the factors that affect rainfall in the city ofSemarang. The results of multiple regression found problems in Multicollinearity. An appropriate method for overcoming multiko in multiple regression is the ridgeregression. Regression of ridge to stabilize regression coefficient value of deviationof assumption in Multicolinearity. The result of the research to select the best model using the smallest MSE value which in the regex ridge model has MSE value 1.517smaller than the value of MSE in multiple regression of 1,519. While for variables that have significant effect on rainfall is wind speed, while variable temperature, humidity, solar irradiance have a significant influence but have weak effect on rainfall in Semarang city.Keywords: Rainfall, Ridge Regression and Multiple Regression
FORECASTING FREEPORT-MCMORAN STOCK PRICE USING LOCALLY STATIONARY WAVELET Vega Zayu Farima; Indah Manfaati Nur
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: PROCEEDING 1ST INSELIDEA INTERNATIONAL SEMINAR ON EDUCATION AND DEVELOPMENT OF ASIA (INseIDEA)
Publisher : Universitas Muhammadiyah Semarang

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Abstract

PT Freeport Indonesia is the manager of the Grasberg Mine in Papua, Indonesia, which is one of the largest gold mines in the world. This mine also contains copper and silver for the world market. Freeport McMoRan'sshareholder has recently trending topic because its shares have been purchased by the Indonesian government.This stock price value has a very high volatility and tend not stationary. Wavelet transformation is capable ofrepresenting functions that are not smooth or have high volatility. Locally Stationary Wavelet (LSW) is aforecasting model by minimizing error values and capturing most of the time series data information. In thisresearch we can get stock price of Freeport-McMoran stationary after differencing. Stock price forecasting usingLSW yields a small MAPE value of 1.94%. This indicates that the LSW model is good for forecasting usingstock price data. Keywords: Freeport McMoRan, Stasionary, LSW, MAPE