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 MIXED GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN ADAPTIVE BANDWIDTH UNTUK ANGKA HARAPAN HIDUP (Studi Kasus : Angka Harapan Hidup di Jawa Tengah) ]]>
<![CDATA[Rizki Faizatun Nisa]]>;
<![CDATA[Sugito Sugito]]>;
<![CDATA[Arief Rachman Hakim]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.33998
<![CDATA[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]]>
<![CDATA[PENGELOMPOKAN TWEETS PADA AKUN TWITTER TOKOPEDIA MENGGUNAKAN ALGORITMA DENSITY BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE ]]>
<![CDATA[Deanira Qinanty Alamsyah]]>;
<![CDATA[Sudarno Sudarno]]>;
<![CDATA[Puspita Kartikasari]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.33992
<![CDATA[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]]>
<![CDATA[ANALISIS SENTIMEN REVIEW APLIKASI CRYPTOCURRENCY MENGGUNAKAN ALGORITMA MAXIMUM ENTROPY DENGAN METODE PEMBOBOTAN TF, TF-IDF DAN BINARY ]]>
<![CDATA[Fadhilla Atansa Tamardina]]>;
<![CDATA[Hasbi Yasin]]>;
<![CDATA[Dwi Ispriyanti]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.34004
<![CDATA[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]]>
<![CDATA[KLASTERISASI PROVINSI DI INDONESIA BERDASARKAN FAKTOR PENYEBARAN COVID-19 MENGGUNAKAN MODEL-BASED CLUSTERING t-MULTIVARIAT ]]>
<![CDATA[Nor Hamidah]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Agus Rusgiyono]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.33999
<![CDATA[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]]>
<![CDATA[ANALISIS ANTREAN BUS NONPATAS JALUR TIMUR TERMINAL TIRTONADI KOTA SURAKARTA MENGGUNAKAN METODE BAYESIAN ]]>
<![CDATA[Rizka Nur Faizah]]>;
<![CDATA[Sugito Sugito]]>;
<![CDATA[Sudarno Sudarno]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.33993
<![CDATA[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]]>
<![CDATA[ANALISIS KLASIFIKASI REKAPITULASI PENGADUAN PELANGGAN UP3 PT. PLN SEMARANG MENGGUNAKAN ALGORITMA QUEST (QUICK, UNBIASED, AND EFFICIENT STATISTICAL TREE) ]]>
<![CDATA[Sang Nur Cahya Widiutama]]>;
<![CDATA[Budi Warsito]]>;
<![CDATA[Sudarno Sudarno]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.34000
<![CDATA[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.]]>
<![CDATA[PREDIKSI HARGA JUAL KAKAO DENGAN METODE LONG SHORT-TERM MEMORY MENGGUNAKAN METODE OPTIMASI ROOT MEAN SQUARE PROPAGATION DAN ADAPTIVE MOMENT ESTIMATION DILENGKAPI GUI RSHINY ]]>
<![CDATA[Yayan Setiawan]]>;
<![CDATA[Tarno Tarno]]>;
<![CDATA[Puspita Kartikasari]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.33994
<![CDATA[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]]>
<![CDATA[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.) ]]>
<![CDATA[Nidaul Khoir]]>;
<![CDATA[Di Asih I Maruddani]]>;
<![CDATA[Dwi Ispriyanti]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.33989
<![CDATA[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]]>
<![CDATA[PEMODELAN INDEKS HARGA PROPERTI RESIDENSIAL DI INDONESIA MENGGUNAKAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE ]]>
<![CDATA[Syazwina Aufa]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Suparti Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.34001
<![CDATA[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]]>
<![CDATA[IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA ]]>
<![CDATA[Ghina Nabila Saputro Putri]]>;
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Tatik Widiharih]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.v11i1.33996
<![CDATA[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.]]>
