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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,
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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.
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Articles 15 Documents
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Search results for , issue "Vol 10, No 1 (2021): Jurnal Gaussian" : 15 Documents clear
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMA BERAS RASKIN MENGGUNAKAN REGRESI LOGISTIK BINER DENGAN GUI R Agustinus Salomo Parsaulian; Tarno Tarno; Dwi Ispriyanti
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30934

Abstract

The Rice Subsidy Program for Low-Income Communities or the Raskin Program is one of the government's programs to eradicate poverty. However, in practice, determining the criteria for Raskin recipients is a complicated problem. The Raskin program is a cross-sectoral national program both horizontally and vertically, to help meet the rice needs of low-income citizens. Determining the criteria for Raskin recipients is often a complicated issue. This study aims to analyze the classification of the Target Households (RTS) for the Raskin Program. The method used is binary logistic regression by utilizing R GUI. Binary logistic regression method is a method to find the relationship between independent and dependent variables, with a binary or dichotomous dependent variable. The data used is the March 2018 National Socio-Economic Survey (Susenas) data for Brebes Regency. The independent variables used in this study are the criteria for determining poor households, namely the area of the house, floor type of the house, wall type of the house, defecation facilities, lighting used, fuel used, ability to buy meat/milk, education level of the head of the household, and the capacity of installed electricity in the main residence. The results of the analysis show that in the final model, the variables that significantly affect the classification of RTS are the ability to eat healthy food, the capacity of installed electricity in the main residence, the education level of the head of the household, and defecation facilities with an accuracy value of 85.4%.Keywords: Raskin Program, Binary Logistic Regression, R GUI
METODE MODIFIED JACKKNIFE RIDGE REGRESSION DALAM PENANGANAN MULTIKOLINIERITAS (STUDI KASUS INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH) Arya Huda Arrasyid; Dwi Ispriyanti; Abdul Hoyyi
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.29922

Abstract

The human development index is a value where the value showed the measure of living standards comparison in a region. The Human Development Index is influenced by several factors, one of them is the education factor that is the average years of schooling and expected years of schooling. A statistical method to find the correlation between the independent variable and the dependent variable can be conducted using the linear regression method. Linear regression requires several assumptions, one of which is the multicollinearity assumption. If the multicollinearity assumption is not fulfilled, another alternative is needed to estimate the regression parameters. One method that can be used to estimate regression parameters is the ridge regression method with an ordinary ridge regression estimator. Ordinary ridge regression then developed more into several methods, such as generalized ridge regression, jackknife ridge regression, and modified jackknife ridge regression method. The generalized Ridge Regression method causes a reduction to variance in linear regression, while the jackknife ridge regression method is obtained by resampling jackknife process on the generalized ridge regression method. Modified jackknife ridge regression is a combination of generalized ridge regression and jackknife ridge regression method. In this journal, the three ridge regression methods will be compared based on the Mean Squared Error obtained in each method. The results of this study indicate that the jackknife ridge regression method has the smallest MSE value. Keywords: Generalized Ridge Regression, Jackknife Ridge Regression, Modified Jackknife Ridge Regression, Multicolinearity  
PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL Maghfiroh Hadadiah Mukrom; Hasbi Yasin; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30935

Abstract

Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.
PEMODELAN JUMLAH KASUS DEMAM BERDARAH DENGUE (DBD) DI JAWA TENGAH DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) Indah Suryani; Hasbi Yasin; Puspita Kartikasari
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.29400

Abstract

Dengue Hemorrhagic Fever (DHF) is one of the diseases with unsual occurrence in Central Java and spread throughout the regency/city. The number sufferers of this disease is still high because the mortality rate is still above the national target. Regarding the less handling of DHF spread, it is necessary to make a plan by identify the factors that allegedly affect that case. Characteristics of data the DHF cases is count data, so this research is carried out using poisson regression. If in poisson regression there is overdispersion, it can be overcome using negative binomial regression. Meanwhile to see the spatial effect, we can use the Geographically Weighted Negative Binomial Regression (GWNBR) method. GWNBR modeling uses a fixed exponential kernel for weighting function. GWNBR is better at modeling the number of DHF cases because it has the smallest AIC value than poisson regression and negative binomial regression. The results of research with poisson regression obtained three variables that have a significant effect on dengue cases. For negative binomial regression, two variables have a significant effect on DHF cases. While the GWNBR method obtained two groups of districts/cities based on significant variables. The variables affecting the number of DHF cases in all districts/cities in Central Java are the percentage of healthy houses, the percentage of clean water quality, and the ratio of medical personnel.Keywords: DHF, GWNBR, Poisson Regression, Binomial Negative Regression, Fixed Exponential Kernel
PENANGANAN KLASIFIKASI KELAS DATA TIDAK SEIMBANG DENGAN RANDOM OVERSAMPLING PADA NAIVE BAYES (Studi Kasus: Status Peserta KB IUD di Kabupaten Kendal) Reza Dwi Fitriani; Hasbi Yasin; Tarno Tarno
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30243

Abstract

The Family Planning Program (KB) launched by the Government of Indonesia to address the problem of population control does not always produce the desired program results. In 2017, there were 7 users of the IUD contraceptive type of contraceptive who failed from 1,102 new IUD users in Kendal Regency so that the ratio of success and failure to the IUD KB program when compared to users of the new IUD KB is 0.64%: 99.36% . The ratio of success and failure of family planning programs which tend to be unbalanced makes it difficult to predict. One of the handling imbalanced data is oversampling, for example using Random Oversampling (ROS). Naive Bayes is used for classification because it’s easy and efficient learning model. The data in this study used 14 independent variables and 1 dependent variable. The results of this study indicate that the G-mean of Naive Bayes is less than 60%. The G-mean of ROS-Naive Bayes is 96.6%. It can be concluded that in this research, the ROS-Naive Bayes method is better than the Naive Bayes method for detecting the success status of IUD family planning in Kendal Regency. Keywords: Naive Bayes, Random Oversampling, G-mean 
PERBANDINGAN METODE OPTIMASI UNTUK PENGELOMPOKAN PROVINSI BERDASARKAN SEKTOR PERIKANAN DI INDONESIA (Studi Kasus Dinas Kelautan dan Perikanan Indonesia) Edy Sulistiyawan; Alfisyahrina Hapsery; Lucky Junita Ayu Arifahanum
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30936

Abstract

The fisheries sector has an important role in supporting the food security chain, where the world's protein needs can be met by fisheries resources, both from capture fisheries and aquaculture. There are several fisheries sectors including fishing companies, capture fisheries production, number of ships, types and size of cultivated land. Therefore a statistical analysis is needed to increase the potential of fisheries in Indonesia. Data on the fisheries sector used in this study from the Indonesian Central Statistics Agency in 2018, which included the 2016 fisheries sector with 34 observation units in Indonesia. By using cluster analysis K-Means aims to group provinces in Indonesia based on the fisheries sector so that several groups are formed which will show the characteristics of each group. There are three determinations of the optimum number of clusters, namely the Elbow method, Silhouette method, and GAP Statistics. The results showed that optimum clusters were formed in 2 clusters, with the best Elbow and Silhouette methods. Where the first cluster is a region that shows a low value of the fisheries sector consisting of 30 provinces this is due to inadequate infrastructure and use that is not optimal while cluster 2 regions that have great potential in the Indonesian fisheries sector in 2016 as many as 4 provinces namely West Java, Java Central, East Java, and South Sulawesi as dominating capture fisheries production and aquaculture. Keywords: Fisheries Sector, K-Means Cluster Analysis, Elbow Method, Silhoutte Method and GAP Statistics.
PEMODELAN INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA BARAT, JAWA TIMUR DAN JAWA TENGAH TAHUN 2019 DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL Meylita Sari; Purhadi Purhadi
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30022

Abstract

Ordinal logistic regression is one of the statistical methods to analyze response variables (dependents) that have an ordinal scale consisting of three or more categories. Predictor variables (independent) that can be included in the model are category or continuous data consisting of two or more  variables. Human Development Index (HDI) is an indicator of the success of human development in a region and can be categorized into medium, high and very high. Based on the further categorization, in this study would like to know more about the HDI model using the Ordinal Logistic Regression method, with predictor variables that are suspected to affect, so that it is obtained in West Java Province is influenced by variable poverty rates and clean water sources with a classification accuracy value of 77.78%, Central Java Province is influenced by variable economic growth rate based on constant price GDP, poverty rate and open unemployment rate with a classification accuracy value of 82.85%. East Java province is influenced by variable poverty rate and open unemployment rate with a classification accuracy value of 76.31%. As well as in the three provinces in Java Island is influenced by variable economic growth rate, variable poverty rate, variable clean water source with a classification accuracy value of 73%.
PEMILIHAN SMARTPHONE TERBAIK PENUNJANG KEGIATAN AKADEMIS MENGGUNAKAN METODE BWM DAN PENGEMBANGAN AHP Mochammad Iffan Zulfiandri; Hasbi Yasin; Sudarno Sudarno
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30542

Abstract

Multi-Criteria Decision Making (MCDM) is a decision-making method to determine the best alternative from several alternatives based on several certain criteria. One of the alternative decision-making methods that can be used is the Best Worst Method (BWM) and the Analytical Hierarchy Process (AHP). BWM makes structured pairwise comparisons and AHP breaks down complex problems into hierarchical structures. One of the decision-making problems that can be solved by the BWM and AHP methods is the problem of choosing a smartphone. Smartphones are one of the most widely used Information and Communication Technology (ICT) devices by Indonesians. The use of smartphones as ICT devices has benefits for the academic community, especially as a means of supporting academic activities. However, various types and mereks of smartphones are circulating, making users confused about choosing the best smartphone according to their needs. Therefore, a reliable method is needed to make it easier for users to choose the best smartphone, especially in supporting academic activities, namely by using a combination of the BWM method and AHP development. The BWM method is used to calculate the optimal weight of the criteria and the AHP method that has been developed is used to calculate the alternative optimal weight based on the criteria. The combination of the two is used to calculate the final optimal weight for each alternative. The results of the calculation of the optimal weight of the criteria show that the RAM criterion has the highest weight, which is 0.290 and the Screen Size criterion has the lowest weight, which is 0.047. The final result obtained is a smartphone type OPPO Find X2 with a final optimal weight of 0.153 to be the best alternative among other alternatives.  Keywords: Multi-Criteria Decision Making (MCDM), Best Worst Method (BWM), Analytical Hierarchy Process (AHP), Information and Communication Technology (ICT), Smartphones, Academic Activities
PEMODELAN TRANSFORMASI FAST-FOURIER PADA VALUASI OBLIGASI KORPORASI (Studi Kasus: PT. Bank Danamon Tbk, PT. Bank CIMB Niaga Tbk, dan PT. Bank UOB Indonesia Tbk) Ubudia Hiliaily Chairunnnisa; Abdul Hoyyi; Hasbi Yasin
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30937

Abstract

The basic assumption that is often used in bond valuations is the assumption on the Black-Scholes model. The practical assumption of the Black-Scholes model is the return of assets with normal distribution, but in reality there are many conditions where the return of assets of a company is not normally distributed and causing improperly developed bond valuation modeling. The Fast-Fourier Transform model (FFT) was developed as a solution to this problem. The Fast-Fourier Transformation Model is a Fourier transformation technique with high accuracy and is more effective because it uses characteristic functions. In this research, a modeling will be carried out to calculate bond valuations designed to take advantage of the computational power of the FFT. The characteristic function used is the Variance Gamma, which has the advantage of being able to capture data return behavior that is not normally distributed. The data used in this study are Sustainable Bonds I of Bank Danamon Phase I Year  2019 Series B, Sustainable Bonds II of Bank CIMB Niaga II Phase IV Year 2018 Series C, Sustainable Subordinated Bonds II of Bank UOB Indonesia Phase II 2019. The results obtained are FFT model using the Variance Gamma characteristic function gives more precise results for the return of assets with not normal distribution.  Keywords: Bonds, Bond Valuation, Black-Scholes, Fast-Fourier Transform, Variance Gamma
PENENTUAN MODEL ANTREAN NON-POISSON DAN PENGUKURAN KINERJA PELAYANAN BUS RAPID TRANSIT TRANS SEMARANG (STUDI KASUS: SHELTER PEMBERANGKATAN BRT KORIDOR V) Purwati Ayuningtyas; Sugito Sugito; Di Asih I Maruddani
Jurnal Gaussian Vol 10, No 1 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i1.30932

Abstract

One of the queue systems that is often found  in daily life is the transportation service system, for example a queue system at the shelters departure of corridor V Bus Rapid Transit (BRT) Trans Semarang. Corridor V has three departure shelters, they are Shelter Victoria Residence, Shelter Marina, and Shelter Bandara Ahmad Yani. Corridor V was choosen, because of its high load factor on January to June 2019. Based on the observation, the service time at the departure shelter is usually longer than the normal shelter. This causes the rise of queue at the departure shelters. The queue at the departure shelters can hamper the arrival of BRT at the other shelters, so the application of the queue theory is needed to find out the extent of operational effectiveness at the departure shelters. The resulting queue model is the Non-Poisson queue model, the queue model for Victoria Residence Shelter: (DAGUM/GEV/1):(GD/∞/∞), Marina Shelter: (DAGUM/G/1):(GD/∞/∞), and Bandara Ahmad Yani Shelter: (GEV/GEV/1):(GD/∞/∞). Based on the value from measurement of the queue system performance, it can be conclude that the three departure shelters of corridor V BRT Trans Semarang have some optimal condition. Keywords: Shelter Departure of Corridor V, Non-Poisson Queueing Model, Dagum, Generalized Extreme Value, System Perfomance Measure  

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