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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
Other
Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
Arjuna Subject : -
Articles 733 Documents
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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.
METODE ROBUST KRIGING UNTUK MENGESTIMASI DATA SPASIAL BERPENCILAN (Studi Kasus: Pencemaran Udara Gas NO2 di Kota Semarang) Anjan Setyo Wahyudi; Sugito Sugito; Dwi Ispriyanti
Jurnal Gaussian Vol 5, No 3 (2016): 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 (493.281 KB) | DOI: 10.14710/j.gauss.v5i3.14688

Abstract

Kriging is a geostatistical analysis used to estimate the value of the function at an unsampled point by computing a spatial correlation in the neighbourhood of the sample point. Interpolation can produce less precise predictive value if there are outliers among the data. Outliers defined as extreme observation value of the other observation values. Robust kriging is development method of ordinary kriging which transform weight of classic semivariogram thus become semivariogram that robust to outlier of the data. This research aims to estimate the concentrate of Nitrogen Dioxide (NO2) in Semarang using robust kriging method. The spatial data used in this research is coordinates point and concentrate of Nitrogen Dioxide (NO2). This method compare between robust semivariogram and theoretical semivariogram (such as spherical, exponential, and gaussian models) to determine the best estimator of the theoretical semivariogram model. The analysis showed that the best theoretical semivariogram model is exponential model. The estimation of Nitrogen Dioxide concentration conducted at 177 urban communities in Semarang.Keywords: kriging, outliers, robust kriging, robust semivariogram
PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF DENGAN MODEL GEOGRAPHICALLY WEIGHTED POISSON REGRESSION (GWPR) (Studi kasus : Angka Kematian Ibu di Provinsi Jawa Timur Tahun 2011) M. Ali Ma'sum; Suparti Suparti; Dwi Ispriyanti
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 (725.605 KB) | DOI: 10.14710/j.gauss.v2i3.3671

Abstract

Maternal mortality rate is one of the crucial problems of death in Indonesia. Maternal deaths in East Java province is likely to increase so that the role of data and information are very important. Negative Binomial Regression is a model that can be used to address the problem overdispersion. While the method of spatial attention factor for type discrete data is Geographically Weighted Poisson Regression Model (GWPR). This study was conducted on the comparison between the Negative Binomial Regression and GWPR to discuss the factors that influence maternal mortality rate in the province of East Java. Indicators that affect maternal mortality include maternal health services. Maternal health services such as antenatal care, obstetric complications treated, Aid deliveries by skilled health care child birth, and neonatal health care services handled neonatal complications. The results of testing the suitability of model shows that there is no influence of spatial factors on maternal mortality rate in the province of East Java. Based on Negative Binomial Regression derived variable number of puerperal women who received vitamin A significantly affect maternal mortality rate, while for GWPR is divided into six clusters districts/cities by same significant variables. From the comparison value of AIC was found that GWPR better to analyzing Maternal mortality in East Java because it has the smallest value of AIC
ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN METODE MUTLAK SIMPANGAN TERKECIL PADA PEMODELAN KEJADIAN DIARE DI KOTA SEMARANG Ika Chandra Nurhayati; Agus Rusgiyono; Hasbi Yasin
Jurnal Gaussian Vol 7, No 2 (2018): 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 (411.2 KB) | DOI: 10.14710/j.gauss.v7i2.26646

Abstract

Diarrhea is one of many health issues in developing country like Indonesia, because the sickness and the death number are still high. According to health profile of Semarang City, the people who suffer from diarrhea from 2010-2015 are decreasing. The lowest point happened at the year 2013 with the total case of 38.001, however there are an increasing number from 2014-2015. The distribution data of diarrhea is a spatial data. The differences between environment and sanitation could cause spatial heterogeneity. The spatial heterogeneity could cause the produced variant value no longer constant, but instead it is different on each region. Therefore, regression model that involves the effects of spatial heterogeneity is needed, which are Geographically Weighted Regression (GWR) that is built by Weighted Least Square (WLS) adjuster. Although, GWR parameter adjuster that used WLS is very sensitive with the existence of outliers. The existence of the outlier in the data will create a huge residual. Thus, more robust method is needed, which is Least Absolute Deviation (LAD) methods in order to estimate the parameter on model GWR. This model is called Robust GWR (RGWR). The result shows that the model events of diarrhea on each region in Semarang City are different. Furthermore, the model events of diarrhea with RGWR model generate MAPE 16,3396% which means the performance of RGWR is formed well. Keyword: Diarrhea, Robust, Geographically Weighted Regression, Least Absolute Deviation
PENGGUNAAN ANALISIS KETAHANAN HIDUP UNTUK PENENTUAN PERIODE GARANSI DAN HARGA PRODUK PADA DATA WAKTU HIDUP LAMPU NEON Dian Ika Pratiwi; Triastuti Wuryandari; Sudarno Sudarno
Jurnal Gaussian Vol 4, No 3 (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 (765.781 KB) | DOI: 10.14710/j.gauss.v4i3.9429

Abstract

Tubular lamp industries nowadays are highly competitive in order to create the most-demanded products. The main factor of consumer’s preferences in this product is quality, particularlythe durability as well as the price. Firstly, the longer a tubular/fluorescent lamp works - which indicates the quality of the fluorescent light - the better. The durability can be also a guideline for the company to determine the warranty cost by finding a value of Mean Time to Failure (MTTF). The next factor for consumers to buy or not to buy the lamp is the price of it. The price of a product can be obtained by calculating its production cost, invariably the warranty cost. In the case of tubular lamp, we use Free Replacement Warranty (FRW) policy and found that the warranty time given by the company for 365 days is precisely compared with the value of MTTF of 391 days. Meanwhile the warranty cost which is calculated by using FRW policy isRp 4.108,00.                  Keywords: tubular lamp, Mean Time to Failure (MTTF), warranty, cost, Free Replacement Warranty (FRW).
ANALISIS CLUSTER DENGAN ALGORITMA K-MEANS DAN FUZZY C-MEANS CLUSTERING UNTUK PENGELOMPOKAN DATA OBLIGASI KORPORASI Desy Rahmawati Ningrat; Di Asih I Maruddani; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 4 (2016): 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 (306.364 KB) | DOI: 10.14710/j.gauss.v5i4.14721

Abstract

Cluster analysis is a method of grouping data (object) that are based on information that found in the data which describes the object and relation within. Cluster analysis aims to make the joined objects in the cluster are identical (or related) with one another and different (not related) to objects in another cluster. In this study  used two method of grouping; Fuzzy C-Means and K-Means Clustering. The data used in this research had been using 357 corporate bonds data on December 1st, 2015. The variables used in this study consist of coupon rate, time to maturity, yield and rating of each corporate. The determination of the number of optimum clusters performed by Xie Beni index of validity calculation at FCM method. Having obtained the optimum number of clusters, evaluation step was conducted by comparing FCM method to K-Means method with noticing the average of standard deviation in the clusters and the average of standard deviation inter-clusters (Sw/Sb) from each method. Method with the smallest Sw/Sb ratio value would get chosen as the best method. Based on the validity index Xie Beni, the most optimum number of cluster is 10 because it has the smallest Sw/Sb ratio value compared to FCM, the value is 0,6651. Afterwards, the result of K-Means clustering is analyzed to determined the interpretation and characteristics of each formed clusters.Keyword: Cluster Analysis, coupon rate, time to maturity, yield, rating, Fuzzy C-Means, K-Means, Xie Beni Index, Sw/Sb ratio.
PENERAPAN DIAGRAM KONTROL IMPROVED GENERALIZED VARIANCE PADA PROSES PRODUKSI HIGH DENSITY POLYETHYLENE (HDPE) Rahma Kurnia Widyawati; Hasbi Yasin; Triastuti Wuryandari
Jurnal Gaussian Vol 3, No 1 (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 (720.967 KB) | DOI: 10.14710/j.gauss.v3i1.4782

Abstract

Dalam suatu industri manufaktur, pengendalian kualitas yang sesuai standar dari perusahaan terhadap produk yang dihasilkan sangat diperlukan.  Biasanya pengendalian kualitas tersebut hanya menggunakan metode sederhana, sehingga diperlukan adanya analisis lebih lanjut, yaitu dengan menggunakan salah satu metode statistika inferensia. Penelitian dilakukan pada CV. Garuda Plastik Karangawen untuk mengetahui keadaan proses produksi High Density PolyEthylene (HDPE). Pengendalian kualitas yang dilakukan melibatkan dua karakteristik kualitas yaitu Panjang dan Berat HDPE. Kualitas pada umumnya diukur menggunakan beberapa karakteristik, sehingga diperlukan metode pengendalian kualitas multivariat dalam melakukan monitoring. Pengendalian kualiatas mean proses menggunakan diagram kontrol T2Hotelling, sedangkan pengendalian kualitas variabilitas proses menggunakan diagram kontrol Improved Generalized Variance. Penelitian ini dilakukan dalam dua tahap. Pada Tahap I diketahui bahwa proses produksi HDPE belum stabil dalam variabilitas maupun meannya. Pada Tahap II diketahui bahwa proses produksi HDPE belum stabil dalam variabilitasnya tetapi sudah stabil dalam mean, artinya proses produksi Tahap II sudah dilakukan perbaikan. Berdasarkan hal tersebut yang menyebabkan proses produksi tidak stabil adalah sistem kejar target produksi sehingga berpengaruh pada bahan baku, pengaturan mesin dan suhu mesin yang sering berubah-ubah sehingga mengakibatkan ukuran roll HDPE menjadi beragam
RANCANGAN D-OPTIMAL LOKAL UNTUK REGRESI POLINOMIAL ORDE 3 DENGAN HETEROSKEDASTISITAS Arya Fendha Ibnu Shina; Tatik Widiharih; Triastuti Wuryandari
Jurnal Gaussian Vol 1, No 1 (2012): 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 (484.191 KB) | DOI: 10.14710/j.gauss.v1i1.571

Abstract

Kemajuan ilmu pengetahuan dan teknologi di berbagai bidang menuntut adanya rancangan percobaan yang efisien. Rancangan D-optimal merupakan rancangan yang efisien. Dalam suatu percobaan yang menggunakan model regresi polinomial orde  dengan heteroskedastisitas dengan fungsi bobot , rancangan D-optimal dan polinomial Jacobi menghasilkan titik-titik rancangan yang akan dicobakan. Suatu rancangan yang terdiri dari titik-titik rancangan dengan proporsi pengamatan yang menghasilkan determinan matriks rancangan maksimal merupakan rancangan D-Optimal. Rancangan D-optimal yang memiliki nilai variansi terstandardisasi sama dengan jumlah parameter di setiap titiknya, merupakan rancangan D-optimal lokal.
PENERAPAN TEORI ANTRIAN PADA PELAYANAN TELLER BANK X KANTOR CABANG PEMBANTU PURI SENTRA NIAGA Nia Puspita Sari; Sugito Sugito; Budi Warsito
Jurnal Gaussian Vol 6, No 1 (2017): 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 (676.136 KB) | DOI: 10.14710/j.gauss.v6i1.14771

Abstract

Bank X Puri Sentra Niaga branch office is one of bank that can not be separated from the queue issue. The customers want a fast and easy service. The length of queueing and the long waiting times may cause customers cancel the transaction and choose another bank. Therefore, it is necessary to define a suitable queueing model of teller service. Bank X Puri Sentra Niaga branch office have two types of teller service namely Antrian 1 and Antrian 2. Queueing model for Antrian 1 is (M/G/1):(GD//). The model describe that the customers arrival distribution is Poisson, the customer service distribution is General, the number of server is 1, the service disipline is FIFO (first in first out), the customers capacity and the resource of customers are infinite. Queueing model for Antrian 2 is (M/M/2):(GD//). The model describe that customers arival distribution and service distribution are Poisson, the number of server is 2, the service disipline is FIFO (first in first out), the customers capacity and the resource of customers are infinite. Software Arena is applied in simulation to compare the measures of performance if the number of teller added.Keywords: Queue, Queueing System Model, Bank, Teller.
KETEPATAN KLASIFIKASI KEIKUTSERTAAN KELUARGA BERENCANA (KB) MENGGUNAKAN ANALISIS REGRESI LOGISTIK BINER DAN FUZZY K-NEAREST NEIGHBOR IN EVERY CLASS DI KABUPATEN KLATEN Dhinda Amalia Timur; Yuciana Wilandari; Diah Safitri
Jurnal Gaussian Vol 3, No 4 (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 (430.729 KB) | DOI: 10.14710/j.gauss.v3i4.8072

Abstract

Fertility is one of the factors that affect population growth. High population growth resulted in the emergence of a variety of problems for a country including Indonesia. This requires a treatment that population growth can be controlled, one attempts to handle by using a Keluarga Berencana program. Therefore conducted a study to determine the factors that affect that participation of Keluarga Berencana (KB) by using Binary Logistic Regression analysis in which the participation of KB divided into two, namely join KB and KB did not participate. Based on the results obtained Binary logistic regression analysis predictor variables that significantly affect participation KB is the number of children, father's education, and mother's education. The resulting classification accuracy with training data comparison testing was 90:10 at 84.375%. Furthermore, the data were analyzed by using Fuzzy K-Nearest Neighbor in every Class (FK-NNC) to determine the accuracy of the classification results comparison with FK-NNC Binary Logistic Regression. From the analysis of the classification accuracy using the FK-NNC with a 90:10 ratio of training data and testing the value of K = 7 values obtained tersebesar ie 87.5%. The comparison of classification accuracy of this value indicates if the FK-NNC is better classify participation in Keluarga Berencana in Klaten district  2012. Keywords: Keluarga Berencana, Binary Logistic Regression, Fuzzy K-Nearest Neighbor in every Class (FK-NNC)

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