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All Journal Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika

Hasbi Yasin

Unknown Affiliation

Author-ID : 452055

Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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APLIKASI MODEL REGRESI SPASIAL UNTUK PEMODELAN ANGKA PARTISIPASI MURNI JENJANG PENDIDIKAN SMA SEDERAJAT DI PROVINSI JAWA TENGAH Restu Dewi Kusumo Astuti; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Net Enrollment Ratio (NER) is an instrument to measure education rate. But NER rate of Senior High School in Central Java Province is only 47,34 %. This study discuss about regression model of factors which influence NER of Senior High School for Central Java province considering spatial effects for each regency in Central Java province. The examination of spatial effects shows that there is spatial dependence in response variable so this study is developed by using Spatial Autoregressive Model (SAR). The methods for estimating the parameter are Ordinary Least Square and Maximum Likelihood Estimation. The result of this study shows that the average number of household members has significant spatial effect for NER rate of Senior High School in Central Java Province. From the comparison AIC value, it was found that SAR model is better to analyze NER rate of Senior High School in Central Java province than classic one.

PENERAPAN DIAGRAM KONTROL T2 HOTELLING PADA PROSES PRODUKSI KACA Muhammad Hilman Rizki Abdullah; Rita Rahmawati; Hasbi Yasin
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

SPC (Statistical Process Control) is a method used to monitoring the process of identifying the causes of variance and improve processes. In term of its variable characteristic, quality control in SPC can be divided into two kinds of univariate control charts and multivariate control charts. T2 Hotelling control chart is a multivariate control charts used in quality control process mean. In the process of glass production, This research was conducted in two stages by making use three major characteristics of quality, those are thickness, length and width. Application of T2 Hotelling control chart on the first phase of the signal are out of control, so it is necessary to identify the variable signal causes the uncontrolled use Decomposition MYT (Mason, Young and Tracy). Based on the identification of variables obtained that the variable width is the cause of the signal out of control. In the second phase is stable glass production process it shows the company has made improvements to the production process of phase II. Keywords: Statistical Process Control, Quality Control, Hotelling T2 control chart, signal out of control

ANALISIS SISTEM PELAYANAN DI STASIUN TAWANG SEMARANG DENGAN METODE ANTRIAN Nursihan Nursihan; Sugito Sugito; Hasbi Yasin
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Semarang Tawang Station is one of the stations visited by customers. As it is known that the train journey into one of the fastest alternative but to use other means of transportation. Therefore, it is necessary to analyze queuing model that describes the conditions to determine the size of the performance of the system to see how the service provided. When the distribution is a Poisson arrival or services or the exponential model (M) but if the distribution is not Poisson or exponential, the model General (G). Model queue at the station with the number of arrivals and the number of services is (M/M/5):(GD/∞/∞). Keywords: Processqueue, Semarang Tawang station, queuing models.

PEMODELAN LAJU INFLASI DI PROVINSI JAWA TENGAH MENGGUNAKAN REGRESI DATA PANEL Dody Apriliawan; Tarno Tarno; Hasbi Yasin
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Panel regression is a regression which is a combination of cross section and time series. To estimate the panel regression there are 3 approaches, the common effect model (CEM), the fixed effect model (FEM) and the random effect model (REM). In the CEM, the parameters were estimated using the Ordinary Least Square (OLS). In the FEM, the parameters estimated by OLS through the addition of dummy variables. At REM, error is assumed random and estimated by the method of Generalized Least Square (GLS). This study aims to analyze the factors that influence inflation in the Central Java province using panel regression. Based on test result of panel regression, the appropriate model is the CEM. The parameters of model are estimated by using OLS the cross section weights. The model show that the Consumer Price Index (CPI), Minimum Salary of City/Regency (MSCR) and the economic growth significantly effect on percentage of inflation in Central Java Province.

ANALISIS INFLASI KOTA SEMARANG MENGGUNAKAN METODE REGRESI NON PARAMETRIK B-SPLINE Alvita Rachma Devi; Moch. Abdul Mukid; Hasbi Yasin
Jurnal Gaussian Vol 3, No 2 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation is an important consideration for investors to invest in an area. An accurate prediction of inflation is required for investors in conducting a careful planning. One of the method to find the predicted value of inflation is by using B-Spline regression, a nonparametric regression which is not depend on certain assumptions, thus providing greater flexibility. The optimal B-Spline models rely on the optimal knots that has a minimum Generalized Cross Validation (GCV). By using Semarang year-on-year inflation data from January 2008 - August 2013, the optimal B-spline models in this study are on the order of 2 ( linear ) with 2 knots, that is 5,99 and 6,09. Prediction of Semarang inflation in 2014 fluctuated around the number five and six and inflation in the end of 2014 is 6,286394%.

PEMETAAN PREFERENSI MAHASISWA BARU DALAM MEMILIH JURUSAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK (ANN) DENGAN ALGORITMA SELF ORGANIZING MAPS (SOM) Muh Najib Hilmi; Yuciana Wilandari; Hasbi Yasin
Jurnal Gaussian Vol 4, No 1 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

College is the highest educational institution and the role the intellectual life of the Indonesian people that the main purpose of academics. Not all colleges into their destination but only college that has a role, credibility and rank the best course of which it is their goal. This makes higher education marketing research approach to get attention and become the main goal of the academics in choosing a college. This research was conducted in order to determine with certainty attribute / emotional reasons academics in choosing college as their academic goals. The method used in this study were self-organizing maps with the Kohonen algorithm is a classification method. Kohonen SOM algorithm with learning rate used 0:05, 0.25, 0:50, 0.75, 0.95 and initialization of initial weight value and the value of the midpoint and 500 iterations with output 3 clusters are formed. Results clustering of SOM validated using Davies-Bouldin index with the best clustering results that DBI minimum (1.7802) with the learning rate is 0.95 and the cluster formed three clusters for the first cluster as many as six members, cluster-2 by 9 members and 3rd cluster as 5 members the results of clustering with top priority contained in the cluster to-2 with a mean (7.434) with the characteristics of each member is an emotional reason in choosing a major. Keywords: Self Organizing Maps, Kohonen algorithm, Learning Rate, Index Davies Bouldin, Cluster.

PROYEKSI DATA PRODUK DOMESTIK BRUTO (PDB) DAN FOREIGN DIRECT INVESTMENT (FDI) MENGGUNAKAN VECTOR AUTOREGRESSIVE (VAR) Indra Satria; Hasbi Yasin; Suparti Suparti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) is an economic instrument that has an attachment and often used for economic development of a country. To predict these two variables there are several methods that can be used, one of which is a method of Vector Autoregressive (VAR). VAR method has some assumptions that the data to be foreseen must have an attachment, stationary in the mean and variance and the resulting error must meet the test of independence and normal distribution. In the early stages of identification done by considering the value of AIC as a determinant of the optimal lag value, which in this case lag 4 who came out as the optimal lag. Granger causality test as an attachment test between variable and Augmented Dickey Fuller test (ADF) as a stationary test. In the parameter estimation phase used Ordinary Least Square method (OLS) to determine the values of the parameters to be used as a model. After getting the model it is necessary to do verification on condition that the residuals must comply with the independence test and multivariate normal test. With a second fulfillment verification test is carried out projections for the next 5 years with a value of R-Square 64% to GDP and 48% for the variable FDI Keywords: FDI, GDP, VAR, causality, independency, multivariate normal, R-Square

PEMILIHAN CLUSTER OPTIMUM PADA FUZZY C-MEANS (STUDI KASUS: PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA) Sarita Budiyani Purnamasari; Hasbi Yasin; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Cluster analysis is a process of separating the objects into groups, so that the objects that belong to the same group are similar to each other and different from the other objects in another group. One method of clustering is Fuzzy C-Means (FCM). FCM is used because each data in a cluster determined by a degree of membership that have value between 0 and 1. This research use two kinds of distance, Manhattan and Euclidean. To determine the proper distance in clustering district / city in Central Java based on indicators of Human Development Index (HDI), we have to calculate the ratio of the standard deviation, where the smaller value indicates a better clustering. While the optimum number of groups obtained from the minimum value of Xie Beni. Variables that used in this research are the indicators of HDI in 2012 for district / city in Central Java, consists of: Life Expectancy Value (years), Literacy Rate (percent), Average Length of School (years), and Purchasing Power Parity (thousands rupiah). The results from this research are the distance that gives a better quality is Euclidean and the optimum cluster given when the number of cluster is five with the smallest value of Xie Beni is 0,50778.

PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA KABUPATEN/ KOTA DI JAWA TIMUR MENGGUNAKAN GEOGRAPHICALLY WEIGHTED ORDINAL LOGISTIC REGRESSION Rahma Nurfiani Pradita; Hasbi Yasin; Diah Safitri
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Human Development Index (HDI) is a measurement used for measuring human developmental achievement in certain area. Although, it does not measure all dimensions of human development, HDI seems able to measure principal dimension of human development that include longevity and health, knowledge and a good life. Geographically Weighted Ordinal Logistic Regression (GWOLR) Model is used to model a relationship between categorical response variable that have ordinal scale toward predictor variable that depend on geographical location where the data are observed. This research aims to know the factors that influence HDI of Regency/ City in East Java Province 2013 using ordinal logistic regression model and GWOLR with exponential kernel function weighting. Factors that are influencing HDI of Regency/ City in East Java are percentage of population that finish Junior High School (X2), the number of health facility (X4), and population density (X5). Based on HDI of Regency/ City in East Java’s accuracy classification result, between observations and prediction counted based on Apparent Error Rate (APER) value, it is known that GWOLR model with exponential kernel function weighting has better classification’s accuracy (86,84%) than ordinal logistic regression model (81,58%). Keywords: HDI, Ordinal Logistic Regression Model, GWOLR, Exponential Kernel Function, Classification’s Accuracy, APER

PEMODELAN PENDAPATAN ASLI DAERAH (PAD) DI KABUPATEN DAN KOTA DI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY WEIGHTED RIDGE REGRESSION Depy Veronica; Hasbi Yasin; Tatik Widiharih
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Linear Regression Analysis is a statistical method for modeling the relation between response variable and predictor variable. Geographically Weighted Regression (GWR) is an expansion of linear regression model if spatial heterogeneity occurred. Local multicollinearity test is required to know the presence of linear correlation between independent variables for each observation location. Geographically Weighted Ridge Regression (GWRR) is a extension of GWR model to solve local multicollinearity problem. Parameter estimation for GWR and GWRR model is done using Weighted Least Square (WLS) method by applying optimum bandwith with Cross Validation (CV) criteria. GWRR model is applied on locally generated recurring revenues (PAD) at district and city of Central Java and its result shows the ability of GWRR model to erase multicollinearity problem. Based on Mean Squared Error (MSE) and Akaike Information Criterion (AIC) value for GWR and GWRR model, it is know that the best model to analyze locally generated recurring revenues (PAD) at district and city of Central Java is GWRR model with the smallest MSE and AIC value. Keywords : Akaike Information Crietion, Spasial Heterogeneity, Geographically Weighted Ridge Regression, Mean Square Error, Local Multicoliniearity

Co-Authors Abdul Hoyyi Agus Rusgiyono Ahmat Dhani Riau Bahtiyar Ajeng Arum Sari Alan Prahutama Aldila Abid Awali Alfiyatun Rohmaniyah Alvita Rachma Devi Andreas Pedo Baluk Arief Rachman Hakim Budi Warsito Cakra Kurniawan Catra Aditya Wisnu Aji Christa Monica David Yuliandar Depy Veronica Diah Safitri Dody Apriliawan Dwi Ispriyanti Ely Fitria Rifkhatussa'diyah Erfan Sofha Firda Shintia Dewi Ilham Indra Bakti Al-Irsyad Indra Satria Indra Satria Ira Puspita Sari Ishlahul Kamal Johan Adi Wicaksana Khusnul Yeni Widiyanti Laily Nadhifah Lenti Agustina Lianasari Tambunan Lina Irawati Mamuroh Mamuroh Mega Fitria Andriyani Moch. Abdul Mukid Muh Najib Hilmi Muhammad Hilman Rizki Abdullah Muryanto Muryanto Muryanto Muryanto Novian Trianggara Novika Pratnyaningrum Nurmalita Sari Nursihan Nursihan Putri Aulia Netra Putri Aulia Wahyuningsih Rahafattri Ariya Fauzannissa Rahma Kurnia Widyawati Rahma Nurfiani Pradita Restu Dewi Kusumo Astuti Rezzy Eko Caraka Rima Nurlita Sari Rita Rahmawati Rizky Amanda Rukun Santoso Safitri Daruyani Sarita Budiyani Purnamasari Sudarno Sudarno SUGITO Sugito Sugito Suparti Suparti Syaifudin Karyadi Tarno Tarno Tatik Widiharih Tika Dhiyani Mirawati Triastuti Wuryandari Vita Dwi Rachmawati Wayaning Apsari Yuciana Wilandari

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