Jurnal Gaussian
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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<![CDATA[PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHENG PADA PERAMALAN HARGA EMAS DI INDONESIA DILENGKAPI GUI R ]]>
<![CDATA[Fauziyyah, Fida]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Santoso, Rukun]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.509-519
<![CDATA[Investment is placing a certain amount of money at this time to get some profit in the future. Investments are divided into three based on the period, namely short-term investments, medium-term investments, and long-term investments. Gold is an example of a good long-term investment. Gold price forecasting is an important thing to know when investing in gold. In this study, gold price data is divided into two parts, namely training data consisting of 674 data from 1 September 2020 to 6 July 2022 and testing data consisting of 75 data from 7 July 2022 to 19 September 2022. The data indicates that there is a trend element so it is suitable for analysis using the Double Exponential Smoothing Holt and Fuzzy Time Series Cheng. Data processing using the Double Exponential Smoothing Holt and Fuzzy Time Series Cheng methods is complemented by the creation of a Graphical User Interface (GUI) which can facilitate the process of selecting the best method. The analysis's findings indicate that Double Exponential Smoothing Holt (0.5427603%), which has a reduced MAPE value than Fuzzy Time Series Cheng (0.6053103%), is the best method.]]>
<![CDATA[PREDIKSI HARGA DAGING SAPI DI KABUPATEN BREBES MENGGUNAKAN PEMODELAN ARFIMA DENGAN EFEK GARCH ]]>
<![CDATA[Imani, Nanda Diva Lingkar]]>;
<![CDATA[Tarno, Tarno]]>;
<![CDATA[Saputra, Bagus Arya]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.570-580
<![CDATA[Beef is a source of animal protein which is rich in nutrients and much-loved by the people of Indonesia. Brebes Regency is an area in Indonesia that has local livestock assets, namely Java Brebes cattle or also known as Jabres cattle. The existence of this jabres cattle is one of the guardians of beef price stability in Brebes in particular and in Central Java in general. The price of beef often fluctuates, to minimize losses, it is necessary to predict the market price. The model for predicting research data is the ARFIMA-GARCH model which is a model that can explain long memory patterns in time series data and experience heteroscedasticity problems. This study aims to obtain the best model with time series analysis and predict the selling price of beef in Brebes Regency for the next 52 weeks using ARFIMA modeling which is enhanced using the addition of the GARCH model. The results of the analysis that has been carried out on beef price data in Brebes Regency can be concluded that the best model obtained is the ARFIMA model ([9], 0.5461747, 0) – GARCH (1, 1). Based on the predictions that have been made using the best model, the resulting MAPE value is 1.56375%, so the model is very good for predicting beef prices in Brebes Regency in the next several periods.]]>
<![CDATA[PENERAPAN REGRESI COX PROPORTIONAL HAZARD PADA KEJADIAN BERSAMA (TIES) DENGAN METODE BRESLOW, EFRON, DAN EXACT ]]>
<![CDATA[Zega, Nesty Novita Sari]]>;
<![CDATA[Mustafid, Mustafid]]>;
<![CDATA[Wuryandari, Triastuti]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.520-530
<![CDATA[Dengue Hemorrhagic Fever (DHF) is a contagious disease that continues to be public health concern. This disease can cause death in a short time and often causes an epidemic. Semarang city has a high number of deaths due to DHF. Reducing the mortality rate due to DHF can be done by knowing the factors that affect the patient's recovery rate. Cox proportional hazard regression is a method of survival analysis that represents the relationship between the independent variable and the dependent variable in the form of survival time. This study examined hospitalized DHF patients at RSI Sultan Agung Semarang. The data contains ties, so parameter estimation is carried out using the Breslow, Efron, and Exact methods. These three methods have different levels of computational intensity and size of data ties, so these three methods will be used in this study to determine the most appropriate method for handling DHF data ties at RSI Sultan Agung Semarang. the analysis reveals that the Cox proportional hazards regression model with the Exact method is the most suitable method for handling ties and the recovery rate of DHF patients is affected by age, platelets, and hemoglobin category.]]>
<![CDATA[KLASIFIKASI PENENTUAN LOKASI STRATEGIS OUTLET BANK SYARIAH INDONESIA DENGAN METODE NAÏVE BAYES CLASSIFIER ]]>
<![CDATA[Rizal, Navioer]]>;
<![CDATA[Fatekurohman, Mohamat]]>;
<![CDATA[Anggraeni, Dian]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.477-486
<![CDATA[Today, the development of the banking sector occurs in the conventional banking sector and the Islamic banking sector, one of which is developing Bank Syariah Indonesia. Bank Syariah Indonesia strives to develop a strategic new office network or branch outlet location that has not been optimal. This research aims to know and analyze the model and determination of variable importance and its effect on the strategic location classification of Bank Syariah Indonesia outlets using the Naïve Bayes Classifier method. The classification model of strategic location determination of offices or outlets obtained from the analysis results in calculating prior probability values and conditional probabilities. The results of the model evaluation test indicator for the Naïve Bayes Classifier method showed an accuracy value of 94,12% and an AUC score of 0,9808. The model was able to classify 16 of the 17 data. The model produces the results of variables importance 6 recommendations variables of the 7 variables used in the study it is location in office area, location in industrial area, populations density of the area, moslem populations of the area, distance from the security office, and distance from the market. The variable importance can be a consideration of Bank Syariah Indonesia optimizing indicators of the office location selection.]]>
<![CDATA[PERBANDINGAN SAR DAN SARQR PADA PEMODELAN INDEKS PEMBANGUNGAN MANUSIA DI JAWA TENGAH TAHUN 2022 ]]>
<![CDATA[Hapsery, Alfisyahrina]]>;
<![CDATA[Hermanto, Elvira Mustikawati Putri]]>;
<![CDATA[Aprilia, Yohanita Uniyantri]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.581-592
<![CDATA[The Human Development Index (HDI) is one of the indicators created to measure the success of human quality of life. Central Java is one of the provinces that has experienced a significant increase in HDI in recent years. However, the rankings of its regencies/cities have not shown significant changes. This study aims to model the HDI in Central Java based on the factors that influence it. The data used for modeling the HDI are secondary data obtained from the Central Statistics Agency (BPS) of Central Java, encompassing 35 regencies/cities in Central Java. The analysis in this study employs spatial analysis, specifically Spatial Autoregressive (SAR). Given the potential spatial effects at certain quantiles of the independent variables, the appropriate analysis is Spatial Autoregressive Quantile Regression (SARQR), which combines the SAR method with quantile regression. The best model from the study results is the SAR model, with factors influencing the HDI in Central Java including Population Percentage, Labor Force Participation Rate, Crime Rate, and Average Non-Food Expenditure. The cities of Semarang, Salatiga, and Surakarta have the highest HDI values at each quantile, ranging from the 0.10 quantile to the 0.90 quantile.]]>
<![CDATA[ANALISIS SISTEM PELAYANAN GARDU TOL OTOMATIS GERBANG TOL GAYAMSARI MENGGUNAKAN METODE BAYESIAN ]]>
<![CDATA[Akbari, Windusiwi Asih]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Suparti, Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.531-538
<![CDATA[Transportation is important things to support mobility. The high level of mobility is in line with the growth of vehicles which has increased causing congestion on roads. Highways are one of the government’s efforts to reduce congestion. Gayamsari Toll Gate is one of the toll gates in Semarang City that experiencing the phenomenon of queuing when paying tolls. This study aims to determine the operation of the service system by obtaining a queuing model and system performance measures from the distribution of arrivals and services. Bayesian method is used to determine the distribution of arrivals and services by finding the posterior distribution. The combination of the sample likelihood function and the prior distribution is known as Bayesian method. The prior distribution used is the previous research data which produces a negative binomial distribution. The likelihood function of the arrival sample in this study is discrete uniform and the likelihood function of the service sample produces a negative binomial distribution. The results are the queuing system model is (Beta/Beta/5): (GD/∞/∞). Based on the results of the queue simulation, we can assume that the service system is optimal.]]>
<![CDATA[PERAMALAN PEREDARAN UANG KARTAL DI INDONESIA MENGGUNAKAN MODEL HYBRID SARIMAX-NEURAL NETWORK ]]>
<![CDATA[Juliarto, Handy Kurniawan]]>;
<![CDATA[Purnamasari, Ika]]>;
<![CDATA[Prangga, Surya]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.465-476
<![CDATA[Stability in the economy is influenced by technological advancements, which impact the digitization of the economy and lead to an increasing demand for electronic and digital payment systems compared to physical currency. There are certain months, such as during year-end holidays, when the circulation of physical currency increases. This study purpose to forecasting the total currency circulation in Indonesia, considering the influence of calendar variations, using a hybrid method that combines SARIMAX and NN. The SARIMAX method was utilized to capture linear effects related to calendar variations, while the NN method was employed to capture nonlinear patterns. The analysis results indicated that the hybrid SARIMAX-NN model with 1 to 3 neurons yielded accurate forecasts, with Mean Absolute Percentage Error (MAPE) values below 2%. However, the highest accuracy was achieved by the SARIMAX-NN hybrid model with 1 neuron, which had a MAPE of 1.38%. Additionally, the forecasting results showed a consistent monthly increase, particularly during the holiday season in December]]>
<![CDATA[OPTIMASI PORTOFOLIO MEAN-VARIANCE DENGAN ANALISIS KLASTER FUZZY C-MEANS ]]>
<![CDATA[Gubu, La]]>;
<![CDATA[Cahyono, Edi]]>;
<![CDATA[Arman, Arman]]>;
<![CDATA[Budiman, Herdi]]>;
<![CDATA[Djafar, Muh. Kabil]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.593-604
<![CDATA[Many studies have been carried out to solve and develop the Markowitz portfolio model. This was done to correct existing models in response to the changes in financial market dynamics and the needs of capital market practitioners. In this study, we provide Mean-Variance (MV) portfolio selection via cluster analysis. Fuzzy C-Means clustering is used to separate stocks into different categories. As a comparison, stocks categories were also carried out using K-Mean clustering. Based on the Sharpe ratio, a stock from each cluster is chosen as a cluster representative. The stocks chosen for each cluster have the greatest Sharpe ratio. The MV portfolio model is used to determine the best portfolio. For the empirical analysis, we examined the fundamental data and the daily return data of stocks that were included in the LQ-45 index from August 2022 to January 2023. The fundamental data of stocks are used to form clusters and the daily return of stocks are used to construct the best portfolio. The results of this study reveal that, for all given risk aversion values, portfolio performance created by Fuzzy C-Means clustering outperformed portfolio performance produced by K-Means clustering.]]>
<![CDATA[PEMODELAN JUMLAH KASUS PNEUMONIA PADA BALITA DI JAWA TIMUR MENGGUNAKAN METODE REGRESI POISSON INVERSE GAUSSIAN DILENGKAPI GUI-R ]]>
<![CDATA[Utami, Krisdiana Nur]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Santoso, Rukun]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.539-548
<![CDATA[Reducing toddler mortality is one of the desire of sustainable development programs.Modeling count data may be analyzed the usage of Poisson regression.The assumption that must be met in Poisson regression is that the mean and variance values must be equal, often in count data there is a violation of this assumption. This is indicated by the variance value which is greater than the mean value (overdispersion). Poisson Inverse Gaussian (PIG) regression is one form of mixed Poisson regression to model data that experience overdispersion cases. The MLE method is used to estimate the PIG regression parameters and hypothesis testing using the MLTR method. The best model of the PIG regression form is based on the smallest AIC value. The results of hypothesis testing concluded that the percentage of under-fives who received exclusive breast feeding had a significant effect on the number of pneumonia cases among toddler. Data modeling using the PIG regression method in this study is complemented by the creation of a Graphical User Interface (GUI) that can facilitate the process of selecting the best model.]]>
<![CDATA[PEMBENTUKAN PORTOFOLIO OPTIMAL DENGAN METODE MEDIAN VARIANCE PADA SAHAM JAKARTA ISLAMIC INDEX (JII) SEKTOR CONSUMER GOODS ]]>
<![CDATA[Faadillah, Muhamad Nabil]]>;
<![CDATA[Maruddani, Di Asih I]]>;
<![CDATA[Hakim, Arief Rachman]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.4.487-498
<![CDATA[Investment is an activity to place owned assets or funds in a product hoping that there will be profits in the future. This case study was conducted by calculating the optimal portfolio using the median variance and calculating Value at Risk (VaR) using the historical simulation method. Median Variance in portfolio optimization is more suitable to be used as an investment guide because the method is not fixated on the normality distribution of the data. The data used is the Jakarta Islamic Index (JII) daily stock price data for 1 year period, which start from April 23th 2021 until April 23th 2022. The stock price used in this research is the closing price data each day during the period. The return data is used to find the weight using Median Variance method so that an optimal portfolio is formed. it is known that the Value at Risk with a confidence level of 95% and the next 1-day time period is -0,024088232 or -2,41% by investing 1% of the funds into UNVR.JK shares., by 58 % to shares of ICBP.JK, by 57% to shares of INDF.JK, by 1% to shares of JPFA.JK, and the last -17% to KLBF.JK shares is 2.41%. ]]>
