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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.
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Articles 733 Documents
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PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH Endah Fauziyah; Dwi Ispriyanti; Tarno Tarno
Jurnal Gaussian Vol 10, No 4 (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.v10i4.33102

Abstract

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074. 
QUERY EXPANSION RANKING PADA ANALISIS SENTIMEN MENGGUNAKAN KLASIFIKASI MULTINOMIAL NAÏVE BAYES (Studi Kasus : Ulasan Aplikasi Shopee pada Hari Belanja Online Nasional 2020) Lutfiah Maharani Siniwi; Alan Prahutama; Arief Rachman Hakim
Jurnal Gaussian Vol 10, No 3 (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.v10i3.32795

Abstract

Shopee is one of the e-commerce sites that has many users in Indonesia. Shopee provides various attractive promos on special days such as National Online Shopping Day on December 12. Shopee site was a complete error on December 12, 2020. Complaints and opinions of Shopee users were also shared through various media, one of them was Google Play Store. Sentiment analysis was used to see the user's response to the Shopee’s incident. Sentiment analysis results can be extracted to obtain information regarding positive or negative reviews from Shopee users. Sentiment analysis was performed using the Multinomial Naïve Bayes classification. the simplest method of probability classification, but it is sensitive to feature selection so that the amount of data is determined by the results of feature selection Query Expansion Ranking. The algorithm that has the highest accuracy and kappa statistic is the best algorithm in classifying Shopee’s users sentiment. The results showed that the classification performance using Multinomial Naïve Bayes with 80% of the features (terms) which have the highest Query Expansion Ranking value was obtained at the accuracy and kappa statistics values are 89% and 77.62%. This means that Multinomial Nave Bayes has a good performance in classifying reviews and the number of features used affects the performance results obtained.
PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH Yashmine Noor Islami; Dwi Ispriyanti; Puspita Kartikasari
Jurnal Gaussian Vol 10, No 4 (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.v10i4.33096

Abstract

Infant mortality (0-11 months) and maternal mortality (during pregnancy, childbirth, and postpartum) are significant indicators in determining the level of public health. Central Java Province which has 35 regencies/cities is included in the top five regions with the highest number of infant and maternal mortality in Indonesia. The data characteristics of the number of infants and maternal mortality are count data. Therefore, the Poisson Regression method can be used to analyze the factors that influence the number of infants and maternal mortality. In Poisson regression analysis, there must be a fulfilled assumption, called equidispersion. Frequently, the variance of count data is greater than the mean, which is known as the overdispersion. The research, binomial negative bivariate regression is used as a solutions to overcome the problem of overdispersion in poisson regression. This method produce a global model. In reality, the geographical, socio-cultural, and economic conditions of each region will be different. This illustrates the effect of spatial heterogeneity, so it needs to be developed into Geographically Weighted Negative Binomial Bivariate Regression (GWNBBR). The model of GWNBBR provides weighting based on the position or distance from one observation area to another. Significant variables for modeling infant mortality cases included the percentage of obstetric complications treated (X1), the percentage of infants who were exclusively breastfed (X3), and the percentage of poor people (X5). Significant variable for modeling maternal mortality cases is the percentage of poor people (X5). Based on the AIC value, GWNBBR model is better than binomial negatif bivariat regression model because it has a smaller AIC value. 
PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN ADAPTIVE BANDWIDTH UNTUK ANGKA HARAPAN HIDUP (Studi Kasus : Angka Harapan Hidup di Jawa Tengah) Rizki Faizatun Nisa; Sugito Sugito; 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.v11i1.33998

Abstract

Life expectancy at birth (AHH) is an estimate of the years a person will take from birth. AHH is used as an indicator of public health and welfare. These two indicators are of concern to the government in relation to human development. It is hoped that the AHH value will continue to increase so that the quality of human development will also increase. Modeling of the factors that influence AHH needs to be done so that efforts to increase AHH become more effective.The AHH value for Central Java (Central Java) in 2020 is 74.37. Factors thought to influence AHH in Central Java are the percentage of poor people (X1), the percentage of households with proper sanitation (X2), the percentage of children under five who are fully immunized (X3) and the open unemployment rate (X4). The assumption of homoscedasticity in AHH modeling in Central Java using linear regression was not fulfilled, meaning that there was spatial heterogeneity between districts/cities, so the Geographically Weighted Regression (GWR) method was used. The weighting function used is the Bisquare and Tricube kernels with adaptive bandwidth. The GWR method will encounter problems if not all independent variables are local, so the Mixed Geographically Weighted Regression (MGWR) method is used. The results of the GWR analysis for the two weighting functions are that the X1 variable is not local, so the MGWR method is used. The results of MGWR modeling for the two weighting functions are that local variables and global variables have a significant effect. The best model is the MGWR model with Kernel Tricube weighting because it has the smallest AICc value. Keyword : AHH, GWR, MGWR, Adaptive Kernel Bisquare, Adaptive Kernel Tricube, AICc
PENGELOMPOKAN TWEETS PADA AKUN TWITTER TOKOPEDIA MENGGUNAKAN ALGORITMA DENSITY BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE Deanira Qinanty Alamsyah; Sudarno Sudarno; Puspita Kartikasari
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.v11i1.33992

Abstract

Social media has become a trend for Indonesian people to express opinions, socialize, and exchange ideas. Internet users in Indonesia in 2021 will reach 202.6 million, 84% of whom use the internet to access social media. Twitter is one of the popular social media in Indonesia. This phenomenon is an opportunity for companies to use Twitter as a marketing tool, one of which is a marketplace company in Indonesia, Tokopedia. This research is intended to cluster tweets uploaded by the @tokopedia Twitter account to find out the type of content that gets a lot of likes and retweets by followers of the @tokopedia Twitter account. Cluster formation is done by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN). DBSCAN is a clustering algorithm based on density. The DBSCAN algorithm requires two parameters, namely the radius (Eps) and the minimum number of objects to form a cluster (MinObj). This research conducted several experiments with different Eps and MinObj parameters on 1.344 tweets that had gone through the stages of removing duplication, text preprocessing, and feature selection. The quality of the cluster formed is measured using the Silhouette Coefficient. Based on the highest average Silhouette Coefficient, the parameter values of Eps=5 and MinObj=3 with Silhouette Coefficient = 0.575 are determined as the best parameters that produce 2 clusters and 7 noise. The type of content that has the highest average number of likes and retweets is the WIB (Indonesian Shopping Time) campaign, so Tokopedia can use this type of content as a marketing tool on Twitter social media because this type of content is preferred by followers of the @tokopedia Twitter account. Keywords: Twitter, Tokopedia, Clustering, DBSCAN, Silhouette Coefficient
ANALISIS SENTIMEN REVIEW APLIKASI CRYPTOCURRENCY MENGGUNAKAN ALGORITMA MAXIMUM ENTROPY DENGAN METODE PEMBOBOTAN TF, TF-IDF DAN BINARY Fadhilla Atansa Tamardina; Hasbi Yasin; 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.v11i1.34004

Abstract

Pandemi COVID-19 yang belum berhenti menyebabkan kondisi ekonomi Indonesia kian memburuk. Masyarakat yang terkena dampak pemotongan upah akibat pandemi harus mencari cara untuk mendapatkan pendapatan pasif. Salah satu cara untuk mendapatkan hal tersebut adalah berinvestasi. Cryptocurrency adalah salah satu instrumen investasi berbasis aplikasi yang memiliki return tinggi. Aplikasi Pintu  adalah aplikasi pertama yang menyediakan fasilitas mobile apps  pada penggunanya. Aplikasi yang dirilis pada tahun 2020 ini sudah memiliki banyak ulasan yang diberikan oleh penggunanya. Ulasan ini dibutuhkan untuk mengetahui apakah ulasan yang diberikan bersifat positif atau negatif. Analisis sentimen pada aplikasi Pintu dipilih untuk melihat sentimen pengguna yang akan dibagi menjadi dua kelas sentimen yaitu positif dan negatif. Klasifikasi dilakukan dengan algoritma Maximum Entropy dengan perbandingan metode pembobotan kata Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF) dan Binary. Model klasifikasi terbaik dilihat berdasarkan nilai akurasi yang dievaluasi dengan 5-Fold Cross Validation. Hasil klasifikasi model Maximum Entropy dengan Binary memiliki tingkat akurasi sebesar 83,21% sedangkan hasil klasifikasi model Maximum Entropy dengan Term Frequency hanya sebesar 83,01% dan model Maximum Entropy dengan Term Frequency-Inverse Document Frequency hanya sebesar 83,20%. Hal ini menunjukkan bahwa tidak terdapat perbedaan yang signifikan pada model algoritma Maximum Entropy dengan metode pembobotan kata Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IDF) dan Binary. Keywords: Cryptocurrency, Binary, Term Frequency, Term Frequency-Inverse Document Frequency, Maximum Entropy
KLASTERISASI PROVINSI DI INDONESIA BERDASARKAN FAKTOR PENYEBARAN COVID-19 MENGGUNAKAN MODEL-BASED CLUSTERING t-MULTIVARIAT Nor Hamidah; Rukun Santoso; Agus Rusgiyono
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.v11i1.33999

Abstract

The spread of Covid-19 had a significant impact in all sectors. Enforcement policies from the government that are appropriate with the conditions for the spread of the virus that are needed to prevent a bigger impact. Clusteritation by province based on data on the spread of Covid-19 is important for the government to set appropriate policies in order to prevent the spread of Covid-19. The data used include data on population density, testing rate, proportion of population 50 years and over, and proportion of population diligently hand-washing in each province. The data factors for the spread of Covid-19 tend to overlap and there are outliers in the data which causes the data not normally distributed. In this study, Model-Based Clustering t-multivariate was used for data clustering. The results show that using Integrated Completed Likelihood, two groups of optimal cluster were obtained. The second cluster has a higher risk of spreading Covid-19 than the first cluster. Keywords : Covid-19, Clustering, Model-Based Clustering t-Multivariat
ANALISIS ANTREAN BUS NONPATAS JALUR TIMUR TERMINAL TIRTONADI KOTA SURAKARTA MENGGUNAKAN METODE BAYESIAN Rizka Nur Faizah; Sugito Sugito; 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.v11i1.33993

Abstract

The queuing system relates to customers and service facilities. Queuing theory designs service facilities to address service requests. Queues occur if the service capacity is not sufficient to provide services to many customers. The queuing phenomenon occurs on non-patas buses on the eastern route of Tirtonadi Terminal, Surakarta with Surabaya, Karanganyar, Wonogiri, Purwodadi and Pedesaan buses. The Bayesian method combines information from current research and previous studies with similar cases, and produces a posterior distribution to form a queuing system model and measure of service system performance. The bus queuing system model for Surabaya, Karanganyar, Wonogiri and Purwodadi has a Gamma-distributed arrival and service pattern. Pedesaan buses has an arrival pattern with a Gamma distribution and a service pattern with an Inverse Gamma distribution. Each line has 1 bus line as a service system, FIFO queue discipline, the number of customer capacity and call sources is not limited. The Surabaya buses has the highest probability of 93.49% that the line is idle and the Pedesaan buses  has the highest probability that the line will be busy serving at 89.50%. The queuing system are considered good because the five lines of service facilities are able to meet customer needs. Keywords: Tirtonadi Terminal, Bayesian, Posterior Distribution, Queue Models, System Performance Measures
ANALISIS KLASIFIKASI REKAPITULASI PENGADUAN PELANGGAN UP3 PT. PLN SEMARANG MENGGUNAKAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE) Sang Nur Cahya Widiutama; Budi Warsito; 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.v11i1.34000

Abstract

Every company must have a way to solve the problems faced by its customers, PT. PLN Persero, the Indonesian national energy utility, must have a method to handle consumer complaints. PT. PLN Persero has a recovery time strategy for resolving consumer concerns, but it is not always effective in doing so. The QUEST algorithm (Quick, Unbiased, and Efficient Statistical Tree) approach is used to classify the problem of the recovery time policy failing on specific complaints. Classification of complaint data in order to obtain characteristics and factors as the main influence on the complaints and be able to provide new opinions for PT. PLN to address customer complaints. The QUEST method is a classification tree technique with two nodes per split that yields an unbiased variable. The QUEST method may be used with both category and numerical data. QUEST uses three stages to create a classification tree: picking the splitting variable, identifying the split point, and pausing the split. The classification tree generated has a tree depth of four layers and obtained three essential factors in the classification, namely weather, the number of customers experiencing the same event, and distance from the site. The classification tree accuracy level is 0.851 (or 85.1%), with a prediction error rate of 0.149 (or 14.9%).Keywords: binary classification tree, recovery time, QUEST algorithm.
PREDIKSI HARGA JUAL KAKAO DENGAN METODE LONG SHORT-TERM MEMORY MENGGUNAKAN METODE OPTIMASI ROOT MEAN SQUARE PROPAGATION DAN ADAPTIVE MOMENT ESTIMATION DILENGKAPI GUI RSHINY Yayan Setiawan; Tarno Tarno; Puspita Kartikasari
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.v11i1.33994

Abstract

Cocoa is a leading commodity from Indonesia. Cocoa prices from time to time fluctuate. Accurate Cocoa price predictions are very important to ensure future prices and help decision making. Cocoa price data is non-stationary and nonlinear, so to make accurate predictions, an Artificial Neural Network (ANN) model is applied. One type of ANN is Long Short-Term Memory (LSTM). LSTM has superior performance for time series based prediction. Optimization methods used are Root Mean Square Propagation, and Adaptive Moment Estimation. The best model was selected based on the Means Square Error (MSE) and Mean Absolute Percentage Error (MAPE) values. This study uses the R-Shiny GUI to facilitate the use of LSTM for users who are less proficient in programming languages. Based on the results, the Long Short-Term Memory model with the Adaptive Moment Estimation optimization method is more optimal than the Long Short-Term Memory with Root Mean Square Propagation seen from the smaller MSE and MAPE values. This study used 27 combinations of hyperparameters. Prediction results with LSTM using the R-Shiny GUI have different levels of accuracy in each experiment. The best accuracy value is experiment with MSE value of 491505.1 and MAPE value of 1.739155% . Cocoa Price Forecasting for the period November to December 2021 tends to decline.Keywords : Cocoa Prices, Forecasting, Long Short-Term Memory, Root Mean Square Propagation, Adaptive Moment Estimation, GUI R-Shiny

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