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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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PREDIKSI HARGA SAHAM MENGGUNAKAN GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION DAN ANALISIS RISIKO DENGAN EXPECTED SHORTFALL (Studi Kasus: Harga Penutupan Saham PT. Waskita Karya Persero Tbk.) Nidaul Khoir; Di Asih I Maruddani; Dwi Ispriyanti
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.33989

Abstract

Investment is an activity that is quite popular among investors in recent years. One of the forms of investment in the financial sector is investing in the capital market by buying stocks in a company. The level of profit from stock investment activities can be seen from the value of stock returns. Factors that can affect the value of stock returns are stock prices. However, stock prices often experience unpredictable changes so that they experience fluctuating movements with increasing time and developing situations, therefore a stock price model is needed to predict stock prices in the future period. The Geometric Brownian Motion with Jump Diffusion’s method is more appropriate to be used in predicting stock prices if there is a jump in stock price data. Predicted stock prices can be used as a basis for measuring the value of investment risk. The results of data processing indicate that the stock return data of PT. Waskita Karya Persero Tbk has a kurtosis value > 3 which means there is a jump in stock return data so that it is more accurately modeled using the Geometric Brownian Motion with Jump Diffusion’s method. The prediction results have a good level of accuracy based on the MAPE value of 18,733%. Furthermore, in order to measure the investment risk of the predicted stock price of PT. Waskita Karya Persero Tbk used the Expected Shortfall Historical Simulation’s method with a significance level of α = 5%, the results were 0,10939, and for the significance level α = 10%, the results were 0,07596. The calculation results show that the greater the trust level used, the greater the risk borne by investors.Keywords: Jump Diffusion Process, Expected Shortfall, Risk, Extreme Value
PEMODELAN INDEKS HARGA PROPERTI RESIDENSIAL DI INDONESIA MENGGUNAKAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE Syazwina Aufa; Rukun Santoso; Suparti Suparti
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.34001

Abstract

Generalized Space Time Autoregressive (GSTAR) is a model used for space time data analysis. Space time data is data related to events at previous times and different locations. GSTAR is an expansion of the Space Time Autoregressive (STAR) method. The STAR method is only suitable for homogeneous locations while GSTAR can be used for heterogeneous locations. This research uses Residensial Property Price Index (IHPR) data. IHPR data is in the form of a multivariate time series consisting of 18 cities/regions with a certain time span. In this study, the analysis of IHPR data is carried out by looking at the relationship between the previous time and other cities/regions. Therefore, the method that can be used is GSTAR method. Analysis of IHPR data in each city/region can help increase the supply of housing, thereby reducing the number of backlogs. The backlog of houses in Indonesia is still relatively high. Backlog is an indicator that is often used by the government to measure the number of housing needs in Indonesia. Based on the fulfillment of the assumptions and the smallest MSE value, the best model obtained is GSTAR(4;1,1,1,1) using cross-correlation normalized weight. The largest IHPR data on forcasting results is in the cities of Makassar, Manado, and Surabaya while the smallest IHPR data is in the city of Balikpapan. The GSTAR method produces forcasted data that is close to the actual data so it is good to use.Keywords : GSTAR, OLS, IHPR
IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA Ghina Nabila Saputro Putri; Dwi Ispriyanti; Tatik Widiharih
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.33996

Abstract

Social media has become the most popular media, which can be accessed by young to old age. Twitter became one of the effective media and the familiar one used by the public, thus making the company make Twitter one of the promotional tools, one of which is Tokopedia. The research aims to group tweets uploaded by @tokopedia Twitter accounts based on the type of tweets content that gets a lot of retweets and likes by followers of @tokopedia. Application of text mining to cluster tweets on the @tokopedia Twitter account using Fuzzy C-Means and Fuzzy Possibilistic C-Means algorithms that viewed the accuracy comparison of both methods used the Modified Partition Coefficient (MPC) cluster validity. The clustering process was carried out five times by the number of clusters ranging from 3 to 7 clusters. The results of the study showed the Fuzzy C-Means method is a better method compared to the Fuzzy Possibilistic C-Means method in clustering data tweets, with the number of clusters formed is 4. The content type formed is related to promo, discount, cashback, prize quizzes, and event promotions organized by Tokopedia. Content with the highest average number of retweets and likes is about automotive deals, sports tools, and merchandise offerings. So, that PT Tokopedia can use this content type as a tool for advertising on Twitter because it gets more likes by followers of @tokopedia.Keywords: Data Tweets, Clustering, Fuzzy C-Means, Fuzzy Possibilistics C-Means, Modified Partition Coefficient.
ESTIMASI PARAMETER DAN PENGUJIAN HIPOTESIS MODEL GEOGRAPHICALLY WEIGHTED GENERALIZED GAMMA REGRESSION Hasbi Yasin; Purhadi Purhadi; Achmad Choiruddin
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.33990

Abstract

Each location has unique characteristics, which are different from other locations which give rise to spatial effects between locations. Therefore, the Generalized Gamma Regression (GGR) model is not suitable to be applied to this problem. The solution is to use a Geographically Weighted Generalized Gamma Regression (GWGGR) model which produces different parameters for each observation location. This study aims to estimate GWGGR parameters using the Berndt-Hall-Hall-Hausman (BHHH) algorithm. After parameter estimation is performed, the hypothesis testing procedure is used to test the similarity of parameters between the generalized gamma regression and GWGGR and to test the significance of the independent variables in the model, either simultaneously using the Maximum Likelihood Ratio Test (MLRT) or partially using the Z-test. Keywords: BHHH, Generalized Gamma, GGR, GWGGR, MLRT.
METODE BAYESIAN PADA SISTEM ANTREAN PELAYANAN MENGGUNAKAN GUI R (Studi Kasus: Antrean Pelayanan di Kantor Dinas Kependudukan dan Pencatatan Sipil Kota Semarang) Atikah Mufidah; Sugito Sugito; Di Asih I Maruddani
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.34002

Abstract

The increase population of Semarang City has given many kinds of problem from births, deaths, marriages and other important events. The change of population identity data causes the number of visitors to the Semarang City Dispendukcapil to increase so that the service system becomes busy. The study aims to determine whether the service system in the Dispendukcapil is good or not. This can be known by determining the distribution of arrival patterns and service patterns to obtain a queuing system model and system performance measures. In this study, the distribution of arrival patterns and service patterns is determined by finding the posterior distribution using the Bayesian method. The Bayesian method was chosen because it is able to combine the distribution of the sample in the current study with previous information for the same case. Posterior distribution can be obtained if it has elements, namely prior distribution and likelihood function. The distribution of arrival patterns and service patterns obtained from prior information, follows the Discrete Uniform and Log-Normal distribution. Based on the calculation and analysis of the posterior distribution, the service system model of the Dispendukcapil Semarang City is obtained, namely for the Customer Service counter, and  for the legalization counter and the population document service counter with a good service system.Keywords:Population, Dispendukcapil Semarang City, queue, Bayesian, prior distribution, posterior distribution, queuing system model, Beta, Gamma, Inverse Gamma.
APLIKASI NAÏVE BAYES CLASSIFIER (NBC) PADA KLASIFIKASI STATUS GIZI BALITA STUNTING DENGAN PENGUJIAN K-FOLD CROSS VALIDATION Riza Rizqi Robbi Arisandi; Budi Warsito; Arief Rachman Hakim
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.33991

Abstract

The case of stunting in Indonesia is a problem that has been discussed for a long time. One of many efforts to overcome this problem is through an accelerated stunting reduction program to improve the nutritional status of the community and also to reduce the prevalence of stunting or stunted toddlers. Generally, the index used to determine the nutritional status of stunting toddlers height compared to age. This study aims to identify the classification results, evaluate the model, and predict the nutritional status of stunting toddlers using the Naïve Bayes Classifier algorithm with K-Fold Cross Validation testing. The data processing system used is the GUI-R (Graphical User Interface) in order to facilitate the analysis process by implementing the Shiny Package in the Rstudio program. The results of accuracy using Naïve Bayes Classifier with 10-Fold Cross Validation test obtained the highest accuracy on the 6th iteration with an accuracy 94.39%, while the lowest accuracy on the 8th iteration with an accuracy 82.08%. Overall, the average accuracy in each iteration is 88.46%, so it can be concluded that Naïve Bayes Classifier model considered good enough to classified data on the nutritional status of stunting toddlers.Keywords: Stunting, Data Mining, Naïve Bayes Classifier, K-Fold Cross Validation, Shiny Package
METODE K-HARMONIC MEANS CLUSTERING DENGAN VALIDASI SILHOUETTE COEFFICIENT (Studi Kasus : Empat Faktor Utama Penyebab Stunting 34 Provinsi di Indonesia Tahun 2018) Silvy ‘Aina Salsabila; Tatik Widiharih; Sudarno Sudarno
Jurnal Gaussian Vol 11, No 1 (2022): 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.v11i1.34003

Abstract

The k-harmonic means method is a method of using the cluster center point value, which is to determine each cluster from its center point based on the calculation of the harmonic average. The k-harmonic means determines the existence of each data point based on the membership function and weighting function by using a distance measure. in the clustering, which aims to increase the importance of data that is far from each central point. This causes the k-harmonic means to be insensitive in initialization in determining the cluster center point and significantly improves the quality of clustering compared to k-means. In determining the level of similarity, the determination of the level of similarity uses the distance measure and the distance measure used is the Euclidean distance measure. The distance measure used in cluster analysis can affect the cluster results obtained. Thus, to determine the quality of the results of the cluster analysis, validation tests were carried out using an internal criteria approach, namely silhouette coefficient. In this study, the k-harmonic means used to classify provinces in Indonesia based on the causes of stunting in 2018. The stunting in children under five in Indonesia has exceeded the limit set by WHO. In 2016-2017 there was an increase in the prevalence of stunting by 27.5% to 29.6%. The k-harmonic means method is used so that the four main factors causing stunting in every province in Indonesia can be seen and the prevention and cure of stunting can run optimally. This method is also used because the data on the four factors that cause stunting show a significant rate of change and as a measure of central tendency in 34 provincial objects in Indonesia. Four factors that cause stunting are used, namely the percentage of households that do not have access to clean drinking water, the percentage of exclusive breastfeeding, the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely and the percentage of households that do not have proper sanitation facilities. The results obtained by the cluster which is optimal at k= 3 using the Euclidean, where the silhouette coefficient = 0,3040722675 ≈ 0,3. Based on the results of the cluster analysis, it is known that in cluster one, the main factor that stands out the most is the percentage of exclusive breastfeeding. In cluster two, the main factor that stands out the most is the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely. In cluster three, the most prominent main factors are the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely and the percentage of households that do not have proper sanitation facilities with the highest average centroid among other clusters. Keywords: Clustering, K-Harmonic Means, Euclidean distance, Silhouette Coefficient, Stunting 
Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air Anestasya Nur Azizah; Tatik Widiharih; Arief Rachman Hakim
Jurnal Gaussian Vol 11, No 2 (2022): 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.v11i2.35470

Abstract

K-Means Clustering is one of the types of non-hierarchical cluster analysis which is frequently used, but has a weakness in processing data with non-linearly separable (do not have clear boundaries) characteristic and overlapping cluster, that is when visually the results of a cluster are between other clusters. The Gaussian Kernel Function in Kernel K-Means Clustering can be used to solve data with non-linearly separable characteristic and overlapping cluster. The difference between Kernel K-Means Clustering and K-Means lies on the input data that have to be plotted in a new dimension using kernel function. The real data used are the data of 47 rivers and 18 indicators of river water pollution from Dinas Lingkungan Hidup (DLH) of Semarang City in the first semester of 2019. The cluster results evaluation is used the Calinski-Harabasz, Silhouette, and Xie-Beni indexes. The goals of this study are to know the step concepts and analysis results of Kernel K-Means Clustering for the grouping of rivers in Semarang City based on water pollution factors. Based on the results of the study, the cluster results evaluation show that the best number of clusters K=4
EXPECTED SHORTFALL DENGAN EKSPANSI CORNISH-FISHER UNTUK ANALISIS RISIKO INVESTASI SEBELUM DAN SESUDAH PANDEMI COVID-19 DILENGKAPI GUI R Reyuli Andespa; Di Asih I Maruddani; Tarno Tarno
Jurnal Gaussian Vol 11, No 2 (2022): 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.v11i2.35457

Abstract

In financial analysis, risk measurement is critical. Stocks are a sort of financial asset investment that is in high demand by investors. Expected Shortfall is one of the strategies used to assess stock investing risk (ES). ES is a risk metric that considers losses in excess of the Value at Risk (VaR). Cornish-Fisher Expansion (ECF) is used to calculate ES with data that deviates from normality and takes into account skewness and kurtosis values. This study used data from the closing price of Sri Rejeki Isman Tbk (SRIL) shares before and during the Covid-19 Pandemic (14 January 2019 to 18 May 2021), with non-normally distributed returns. According to the calculations, the risk that investors will bear using the ES ECF value for the next day before the Covid-19 Pandemic is 1.1752 and after the Covid-19 Pandemic is 3.3177% at a 95% confidence level. The risk that investors will bear for the next day before the Covid-19 Pandemic is 5.8928%, and after the Covid-19 Pandemic is 10.3703%, based on a 99% confidence level. The findings of the study reveal that the higher the amount of trust, the higher the risk.
PERAMALAN HARGA EMAS DUNIA DENGAN MODEL GLOSTEN-JAGANNATHAN-RUNCLE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Uswatun Hasanah; Agus Rusgiyono; Rukun Santoso
Jurnal Gaussian Vol 11, No 2 (2022): 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.v11i2.35477

Abstract

Gold investment is considered safer and has less risk than other types of investment. One of the important knowledge in investing in gold is predicting the price of gold in the future through modeling the price of gold in the past. The purpose of this study is to model the gold price in the past so that it can be used to predict gold prices in the future. The world gold price data is a time series data that has heteroscedasticity properties, so the time series model used to solve the heteroscedasticity problem is GARCH. This study has an asymmetric effect, so the asymmetric GARCH model is used, namely the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model to model the world gold price data. The data is divided into in-sample data from January 3, 2012 to December 31, 2018 to create a world gold price model and out-sample data from January 1, 2019 to December 31, 2020, which is used to evaluate model performance based on MAPE values. The best model is the ARIMA(1,1,0) GJR-GARCH(1,1) model with a MAPE data out sample value of 18,93% which shows that the performance of the model has good forecasting abilities.

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