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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.
Arjuna Subject : -
Articles 733 Documents
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PEMODELAN GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION (GWGPR) PADA KASUS KEMATIAN IBU NIFAS DI JAWA TENGAH Wahyu Sabtika; Alan Prahutama; Hasbi Yasin
Jurnal Gaussian Vol 10, No 2 (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.v10i2.30946

Abstract

Maternal mortality is one indicator to describing prosperity in a country and indicator of women's health. Most of the maternal mortality caused by postpartum maternal mortality. The number of postpastum maternal mortality is events that the probability of the incident is small, where the incident depending on a certain time or in a certain regions with the results of the observation are variable diskrit and between variable independent each other that follows the Poisson distribution, so that the proper statistical method is Poisson regression. However, in Poisson regression model analysis sometimes assumptions can occur violations, where the value of variance is greater than the mean value called overdispersion. Generalized Poisson Regression (GPR) is one model that can be used to handle overdispersion problems. This modeling produces global parameters for all locations (regions), so to overcome this we need a method of statistical modeling with due regard to spatial factors. The analytical method used to determine the factors that influence the number of postpartum maternal mortality in Central Java that have overdispersion and there are spatial factors, is Geographically Weighted Generalized Poisson Regression (GWGPR) using the Maximum Likelihood Estimation method and Adaptive Bisquare weighting. Poisson regression and GPR modeling produces a variable percentage of pregnant women doing K1 which has a significant effect on the number of postpartum maternal mortality, while for GWGPR modeling is divided into four cluster in all regency/city in Central Java based on the same significant variable. From the comparison of AIC values, it was found that the GWGPR model is better for analyzing postpartum maternal mortality in Central Java because it has the smallest AIC value.Keywords: The Number of Postpartum Maternal Mortality, Overdispersion, Generalized Poisson Regression, Spatial, Geograpically Weighted Generalized Poisson Regression, AIC
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
KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN ALGORITMA C5.0 DI KABUPATEN PEMALANG Fatiya Nur Umma; Budi Warsito; Di Asih I Maruddani
Jurnal Gaussian Vol 10, No 2 (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.v10i2.29934

Abstract

Pemalang regency is a district which has amount of poverty around 16.04%. One of the effort that must be improved in tackling poverty is increasing the accuracy of the government program’s target. The improvement of target accuracy is expected to give the better impact on the welfare of the population. This study classified the poverty status of households in Pemalang regency using C5.0 Algorithm. The poverty status of households is divided into two classes, namely poor and non-poor. There was an imbalance of data in both classes. Data imbalances were handled by using Synthetic Minority Oversampling Technique (SMOTE). From the research that has been done, SMOTE application in classification of household poverty status affected the evaluation value of the model. Previously the model could not classify the minority class and after using SMOTE the model produced an average value of sensitivity 25.80%. SMOTE application increased the average value of specificity from 91.16% to 94.91%. However, SMOTE application decreased the average value of accuracy which originally 91.16% down to 82.2%.Keywords : C5.0, Household poverty, Classification, SMOTE
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 
KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X) Chalimatus Sa'diah; Tatik Widiharih; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 2 (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.v10i2.29923

Abstract

One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that affect bad credit such as gender, marital status, education, employment, income, expenses, home ownership status and the dependent variable is credit status (bad and current). The analysis results show that the binary logistic regression has an accuracy value of 76.38% with an APER of 23.62%, while CHAID has an accuracy value of 93.19% with an APER of 6.81%. The accuracy value of the CHAID method is greater than the binary logistic regression method, while the APER value of the CHAID method is smaller than the binary logistic regression method. So it can be concluded that the CHAID method is better than the binary logistic regression method in classifying bad credit at company X. Keywords: Credit, Classification, Binary Logistic Regression, CHAID.
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.
EXPECTED SHORTFALL PADA PORTOFOLIO OPTIMAL DENGAN METODE SINGLE INDEX MODEL (Studi Kasus pada Saham IDX30) Eis Kartika Dewi; Dwi Ispriyanti; Agus Rusgiyono
Jurnal Gaussian Vol 10, No 2 (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.v10i2.30947

Abstract

Stock investment is a commitment to a number of funds in marketable securities which shows proof of ownership of a company with the aim of obtaining profits in the future. For obtaining optimal returns from stock investments, investors are expected to form optimal portfolios. The optimal portfolio formation using the Single Index Model is based on the observation that a stock fluctuates in the direction of the market price. It shows that most stocks tend to experience price increases if the market share price rises, and vice versa. Selection of optimal portfolio-forming stocks on IDX30 using the Single Index Model method produces 4 stocks, that are BRPT (Barito Pacific Tbk.) with weight 31.134%, ICBP (Indofood CBP Sukses Makmur Tbk.) 17.138%, BBCA (Bank Central Asia Tbk.) 51.331% and SMGR (Semen Indonesia (Persero) Tbk.) 0.397%. Every investment must have a risk, for that investors need to calculate the possible risks that occur before investing. To calculate risk, Expected Shortfall (ES) is used as a measure of risk that is better than Value at Risk (VaR) because ES fulfill the subadditivity. At the 95% confidence level, the ES value is 23.063% while the VaR value is 10.829%. This means that the biggest possible risk that an optimal portfolio investor will receive using the Single Index Model for the next five weeks is 23.063%.Keywords : Portfolio, Single Index Model, Expected Shortfall, Value at Risk.
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%.
PENGUKURAN KINERJA PORTOFOLIO OPTIMAL SAHAM LQ45 MENGGUNAKAN METODE CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM) Kristika Safitri; Tarno Tarno; Abdul Hoyyi
Jurnal Gaussian Vol 10, No 2 (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.v10i2.29414

Abstract

Investment is planting some funds to get profit and the stock is one of the type of investment in fincancial that the most interested for investors. To avoid the risk of investing, investors try to diversify their invesments by using portfolio. Stock portfolio is investment which comprised of various stocks from different companies, with the expect when the price of one stock decreases, while the other increases, then the investments do not suffer losses. Models that can be used to make a portfolio, one of them is Capital Asset Pricing Model (CAPM)  and Liquidity Adjusted Capital Asset Pricing Model (LCAPM). CAPM is a model that connects expected return with the risk of  an asset under market equilibrium condition. LCAPM is a method of new development of the CAPM model which is influenced by liquidity risk. To  analyze whether the formed portfolio have a good performance or not, so portfolio perfomance assessment will be done by using The Sharpe Index. This research uses data from closing prices, transaction volume and volume total of LQ45 Index stock on period March 2016-February 2020 and then data of JCI and interest rate of central bank of the Republic of Indonesia. Based on The Sharpe Index, optimal portfolio is LCAPM model portfolio with 3 stock composition and the proportion investment are 32,39% for LPPF, 49,86% for SRIL and  17,75% for TLKM. Keywords: LQ45 Index, Portfolio, Capital Asset Pricing Model (CAPM), Liquidity Adjusted Capital Asset Pricing Model (LCAPM), The Sharpe Index.
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

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