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[PEMODELAN DATA LONGITUDINAL MENGGUNAKAN REGRESI POLINOMIAL LOKAL PADA KELOMPOK SAHAM PERUSAHAAN PENYEDIA JASA TELEKOMUNIKASI DENGAN GUI R ]]>
<![CDATA[Noer Rachma, Gustyas Zella]]>;
<![CDATA[Suparti, Suparti]]>;
<![CDATA[Santoso, Rukun]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.352-361
<![CDATA[The economic development of a country can be seen based on the capital market that was growing and developing. One of the most popular capital market instruments is stocks. Stocks based on market capitalization groups include longitudinal data. One of the statistical methods for longitudinal data modelling is nonparametric regression which has no modelling assumptions requirement. This research models monthly stock prices using a nonparametric local polynomial method with the selection of the best model which has minimum value of Mean Square Error (MSE). The data was divided into 2 parts, namely in sample data from November, 2018 to June, 2021 to form a model and out sample data from July, 2021 to February, 2022 used for evaluation of model performance by Mean Absolute Percentage Error (MAPE) values. The best model is the local polynomial model with Biweight kernel function of degree 5, local point of 4, bandwidth of 37, and MSE value of 0.03481085. MAPE out sample of data value is 31.13%, which indicating that the model has sufficient forecasting. In this research arrange Graphical User Interface (GUI) by using R software with shiny package is built to make display output data analyzing more easy and more interactive.]]>
<![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[ANALISIS TINGKAT KEPENTINGAN DAN KINERJA (IMPORTANCE-PERFORMANCE ANALYSIS) NILAI KEGUNAAN APLIKASI M-COMMERCE ]]>
<![CDATA[Elyasa, Fatiya Rahmita]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[rusgiyono, agus]]>
<![CDATA[ Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.13.1.70-78
<![CDATA[Good application quality can bring user satisfaction to form user’s loyalty and trust in the company. The level of user satisfaction is different between Gojek and Grab even though each them has similarities in application features. Gojek and Grab are trying to make position as the most popular by the community in getting people's needs quickly and efficiently with huge competitive. This study aims to analyze and compare the usability qualities of Gojek and Grab based on the User Experience Questionnaire (UEQ) approach on the dimensions of attractiveness, perception, efficiency, dependability, stimulation and novelty. Measurements are made using Importance-Performance Analysis to graphically measure customer satisfaction so that service quality improvement priorities can be established. The data was collected by questionnaire to 32 selected respondents who accessed the Gojek and Grab applications in the Greater Jakarta area in August-December 2022. Based on the results of the study, the position of the usability quality attributes in Gojek and Grab was visually almost the same for each IPA quadrant. The Gojek application is superior in 5 dimensions to Grab, in terms of attractiveness, perception, dependability, stimulation, and novelty. Grab has one dimension that is superior to Gojek, in terms efficiency.]]>
<![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[ANALISIS PERBANDINGAN SILHOUETTE COEFFICIENT DAN METODE ELBOW PADA PENGELOMPOKKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR IPM DENGAN K-MEDOIDS ]]>
<![CDATA[Rahmawati, Tias]]>;
<![CDATA[Wilandari, Yuciana]]>;
<![CDATA[Kartikasari, Puspita]]>
<![CDATA[ Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.13.1.13-24
<![CDATA[Development is a process of change that is carried out consistently with the aim of improvement in all aspects of life with the prevailing values in society to achieve predetermined life goals. The quality of life of the community is measured by the Human Development Index (HDI) at the provincial level through three indicators, including: economic level, health, and education. K-medoids is a method used to group objects that contain outliers. In determining the optimal number of clusters using the Silhouette Coefficient method which has the advantage of determining the best number of clusters that can measure how close the relationship between objects and measure how far a cluster is separated from other clusters. The technique called the Elbow method is employed to ascertain the optimal quantity of clusters, which is done by examining the percentage outcomes derived from comparing the quantity of clusters that form an elbow at a certain point. Then cluster evaluation is carried out using the Davies Bouldin Index (DBI) as a comparison for cluster validation. The results of this study using the Silhouette Coefficient method produced the best cluster, namely 3 clusters with a Silhoutte Coefficient value of 0,3129 and a DBI value of 1,3184. Meanwhile, using the Elbow method produced the best cluster, namely 4 clusters with an SSE value of 54,5548 with a DBI value of 1,1754. So that the best cluster is 4 clusters with the Elbow method with the smallest DBI value.]]>
<![CDATA[PEMODELAN ANTAR VARIABEL EKONOMI SECARA SIMULTAN MENGGUNAKAN PENDEKATAN VECTOR ERROR CORRECTION MODEL (VECM) ]]>
<![CDATA[Halim, Rossa Fitria]]>;
<![CDATA[Sudarno, Sudarno]]>;
<![CDATA[Tarno, Tarno]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.414-424
<![CDATA[The movement of the Jakarta Composite Index (IHSG) is influenced by internal factors such as inflation, the BI Rate, exchange rate, and external factors consisting of world gold prices and world crude oil prices. The six economic variables have a relationship simultaneously. Vector Error Correction Model (VECM) is a Vector Autoregressive (VAR) which has non-stationary but has a long-term cointegration. The purpose of this study is to analyze the cointegration among economic variables and determine the model of economic variables. Data for the variables is monthly data for the period January 2012 to December 2021 which has fulfilled stationarity at first level of difference. The optimum lag chosen is lag 1 so that the model to be used is VECM(1) and the resulting VAR system has less than one modulus for the VAR to be stable. Johansen's cointegration test yielded 5 cointegrations, so each short-term period adjusts simultaneously and tends to adjust with each other to achieve long-term equilibrium. The Mean Absolute Percentage Error (MAPE) value in the evaluation of model accuracy ranges below 10%, so the model’s performance is very good.]]>
<![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[IMPLEMENTASI METODE NAIVE BAYES CLASSIFIER UNTUK KLASIFIKASI SENTIMEN ULASAN PENGGUNA APLIKASI NETFLIX PADA GOOGLE PLAY ]]>
<![CDATA[Rieuwpassa, Jessica Athalia]]>;
<![CDATA[Sugito, Sugito]]>;
<![CDATA[Widiharih, Tatik]]>
<![CDATA[ Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.12.3.362-371
<![CDATA[The COVID-19 pandemic has led to restrictions on activities in public places or facilities, such as cinemas. This has resulted in increased users of streaming service applications such as Netflix where users can access videos or movies online. Netflix users continue to increase from year to year, but its users began to decrease along with other streaming applications. Related to this, sentiment analysis was carried out on the classification of positive and negative reviews given by users on the Google Play website. The classification is expected to produce good accuracy and be analyzed so that it can be useful information for Netflix and potential users of streaming applications. The Naive Bayes Classifier method is a classification algorithm that is easy to apply and has high effectiveness for classifying text. This method utilizes the concept of conditional probability and has a strong assumption of independence. This study uses 2.850 Netflix application review data on Google Play which is then processed and divided into training data and test data with a ratio of 80:20. Classification with the Naive Bayes Classifier produces an accuracy value of 87,33%, a precision value of 87,6%, a recall value of 93,53%, and an F-measure value of 90,47% so it can be concluded that the performance of the Naive Bayes method is good for classifying user reviews of the Netflix.]]>
<![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[PENENTUAN VALUE AT RISK (VAR) PADA PORTOFOLIO BIVARIAT DENGAN PENDEKATAN COPULA GUMBEL ]]>
<![CDATA[Febriani, Karina]]>;
<![CDATA[Tarno, Tarno]]>;
<![CDATA[Fakhriyana, Deby]]>
<![CDATA[ Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.13.1.79-87
<![CDATA[One way to minimize risk in stock investment is stock portfolio. Value at Risk (VaR) is a calculation method that can be used to estimate the risk of a stock portfolio. VaR can be measured by parametric and non-parametric approaches. Calculation of VaR with Monte Carlo simulation assumes the data is normally distributed. Stock return data generally has high volatility so that the residual variance of the model is not constant (heteroscedasticity) and not normally distributed. The ARIMA-GARCH model can be used to solve heteroscedasticity problems. Copula is a tool used to model the combined distribution of residuals from the ARIMA-GARCH model which does not require normality assumptions. Gumbel's copula is copula that has the best sensitivity to high risk. This study uses stock data of PT Bukit Asam Tbk (PTBA) and PT Chandra Asri Petrochemical Tbk (TPIA) for the period April 1 2020 – December 1 2022. The initial step of this research is model stock returns using the ARIMA-GARCH method and then calculate portfolio VaR using the Gumbel’s copula. The results showed that the best model for PTBA is ARIMA(2,0,2) GARCH(1,1) and for TPIA is ARIMA(1,0,0) GARCH (1,1). At the 95% confidence level, the portfolio risk is 2,41%.]]>
