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 SMOTE DAN ADASYN PADA DATA IMBALANCE UNTUK KLASIFIKASI RUMAH TANGGA MISKIN DI KABUPATEN TEMANGGUNG DENGAN ALGORITMA K-NEAREST NEIGHBOR ]]>
<![CDATA[Dinda Virrliana Ramadhanti]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Tatik Widiharih]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.499-505
<![CDATA[Poverty is a global problem that has occurred in various countries with various impacts. Poverty conditions are characterized by the inability of a person or household to meet the basic needs of life. Socio-economic problems, such as poverty, can be handled using machine learning, one of which is classification. The classification of households based on poverty criteria is expected to assist the government in preparing programs that are right on target. K-Nearest Neighbor is one of the easy-to-use classification algorithms. this classification is based on the closest neighborliness. The problem that can be experienced when classifying is if the data used is imbalanced. The data imbalance will causing the classification process to focus more on the majority class. SMOTE and ADASYN are used to solve the problem of imbalanced data. This study resulted in the addition of SMOTE and ADASYN to imbalanced data can improve classification performance, especially on the G-mean value. G-mean is a performance measure that is widely used in the case of imbalanced data. The result of this study is that SMOTE can increase the G-mean value to 58.5%, while ADASYN is 57.3%. Therefore, it can be concluded that SMOTE-KNN is the best classification model for household poverty classification.]]>
<![CDATA[ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN ALGORITMA GRID SEARCH TIME SERIES CROSS VALIDATION UNTUK PREDIKSI JUMLAH KASUS TERKONFIRMASI COVID-19 DI INDONESIA ]]>
<![CDATA[Anindita Nur Safira]]>;
<![CDATA[Budi Warsito]]>;
<![CDATA[Agus Rusgiyono]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.512-521
<![CDATA[Coronavirus Disease 2019 or Covid-19 is a group of types of viruses that interfere with the respiratory tract associated with the seafood market that emerged in Wuhan City, Hubei Province, China at the end of 2019. The first confirmed cases of Covid-19 in Indonesia on March 2, 2020, were 2 cases and until the end of 2021, it continues to grow every day. The purpose of this study was to predict the number of confirmed cases of Covid-19 in Indonesia using the Support Vector Regression (SVR) method with linear kernel functions, radial basis functions (RBF), and polynomials. Support Vector Regression (SVR) is the application of a support vector machine (SVM) in regression cases that aims to find the dividing line in the form of the best regression function. The advantage of the SVR model is can be used on time series data, data that are not normally distributed and data that is not linear. Parameter selection for each kernel used a grid search algorithm combined with time series cross validation. The criteria used to measure the goodness of the model are MSE (Mean Square Error), MAPE (Mean Absolute Percentage Error) and R2 (Coefficient of Determination). The results of this study indicate that the best model is Support Vector Regression (SVR) with a polynomial kernel and the parameters used include Cost = 1, degree = 1, and coefficient = 0.1. The polynomial kernel SVR model produces a MAPE value of 0.4946215%, which means the model has very good predictive ability. The prediction accuracy obtained with an R2 value of 85.65011% and an MSE value of 161606.1.]]>
<![CDATA[ANALISIS PENGARUH KUALITAS PELAYANAN TERHADAP KEPUASAN PENUMPANG BRT TRANS SEMARANG MENGGUNAKAN PARTIAL LEAST SQUARE (PLS) ]]>
<![CDATA[Irma Dwi Tyana]]>;
<![CDATA[Tatik Widiharih]]>;
<![CDATA[Iut Tri Utami]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.591-604
<![CDATA[BRT Trans Semarang is an integrated bus transportation system that operates in Semarang City and parts of Semarang Regency. This transportation provides service facilities such as the availability of bus stops, air-conditioned rooms to travel route information. The facility is expected to be able to provide service satisfaction for its passengers. This study was conducted to determine the effect of service quality on the satisfaction of Trans Semarang BRT passengers using Partial Least Square (PLS), with a case study of Diponegoro University students. PLS is an alternative approach from covariance-based SEM to variance-based. The advantage of PLS is that it is able to handle covariance-based SEM problems such as small sample numbers, abnormal data and the presence of multicholinearity. The quality of this service is measured through the variables of Direct Evidence, Reliability, Responsiveness, Empathy and Guarantee. Passenger satisfaction is measured through a sense of pleasure, a positive impression and the absence of complaints. The results showed that the variables that had a significant effect on the satisfaction of Trans Semarang BRT passengers were the variables of Direct Evidence, Reliability and Responsiveness. Variables that do not have a significant effect on the satisfaction of Trans Semarang BRT passengers are the empathy and guarantee variables. The Adjusted R-Square value is included in the medium category with a value of 0.414, means that the variables of Direct Evidence, Reliability and Responsiveness affect the satisfaction of Trans Semarang BRT passengers by 41.4%. ]]>
<![CDATA[PENERAPAN ALGORITMA BACKPROPAGATION DAN OPTIMASI CONJUGATE GRADIENT UNTUK KLASIFIKASI HASIL TES LABORATORIUM ]]>
<![CDATA[Wahyu Tiara Rosaamalia]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Suparti Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.506-511
<![CDATA[A blood test is generally used to evaluate the condition of the blood and its components, conduct screening, and aid diagnosis. Blood tests in the laboratory are commonly used to deliberate whether a patient needs to be hospitalized or treated as an outpatient. Backpropagation algorithm was selected for its ability to solve complex problems. Conjugate gradient optimization is used because it facilitates faster solution search. An electronic medical record containing the results of patient laboratory examinations was obtained from Mendeley. The data was divided into training and testing with a 95:5 ratio, which was discovered to be the best ratio from the experiments. The best architecture was achieved by a combination of 10 neurons in the input layer, 16 neurons in the first hidden layer, 2 neurons in the second hidden layer, and a neuron in the output layer. Purelin is used as the activation function for both the first hidden and output layers, whereas the binary sigmoid is used for the second hidden layer. The analysis revealed that for 100 bootstraps in training data, the network worked with an average accuracy of 60.17% and a recall of 99.77%, while the accuracy results in testing data were 69.23%.]]>
<![CDATA[PENGARUH KONVEKSITAS TERHADAP SENSITIVITAS HARGA JUAL DAN DELTA-NORMAL VALUE AT RISK (VAR) PORTOFOLIO OBLIGASI PEMERINTAH MENGGUNAKAN DURASI EKSPONENSIAL ]]>
<![CDATA[Putri Devitasari]]>;
<![CDATA[Di Asih I Maruddani]]>;
<![CDATA[Puspita Kartikasari]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.532-541
<![CDATA[Bonds are one of the investment instruments issued by the issuer as proof of debt. Bond investment is relatively safe, but it is possible for investors to experience losses. Investors should always consider that trading a bond is always risky. One of the important bond risks is interest risk. The concept of duration can only explain well for small changes in interest rates but cannot explain well for large changes in interest rates. The estimation of the duration concept will have a larger calculation error with the greater changes in market interest rates that occur so it is necessary to add convexity to improve accuracy. This study aims to estimate the risk of government bonds based on the estimation of bond prices with the effect of convexity. Several studies have shown that exponential duration can predict bond prices more accurately than Macau duration. Exponential duration with convexity will be applied in this study to measure the accurate value of bond prices caused by changes in interest rates. The Delta-Normal VaR portfolio method is used to calculate risk based on estimated bond prices in the form of a portfolio. The formation of this portfolio aims to reduce the losses suffered by investors. This method is applied to four Indonesian government bonds with codes FR0056, FR0059, FR0074, and FR0080. The results showed that the bonds portfolio FR0056 and FR0074 had the smallest risk compared to other portfolios with a weight proportion of 15% for bonds FR0056 and 85% for bonds FR0074.]]>
<![CDATA[PENERAPAN SMOOTHING B-SPLINES PADA HUBUNGAN ANTARA PERTUMBUHAN EKONOMI DAN TINGKAT KEBAHAGIAAN ]]>
<![CDATA[Muhammad Fajar]]>;
<![CDATA[Eko Fajariyanto]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.605-615
<![CDATA[This study aimed to model the smoothing of B-splines on the relationship between happiness and economic growth. The method used in this research is smoothing B-splines, which were in the process of determining knots and smoothing parameters (λ) based on the minimum GCV. The data used in the study came from Badan Pusat Statistik-Statistics Indonesia. The results of this study concluded that the smoothing of B-splines is quite good at modeling the relationship between the level of happiness (response variable) and economic growth (predictor variable). The smoothing B-splines model can explain the variation in the level of happiness by 71.583 percent.]]>
<![CDATA[PEMODELAN KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT MENGGUNAKAN REGRESI NONPARAMETRIK CAMPURAN KERNEL DAN SPLINE ]]>
<![CDATA[Khansa Amalia Fitroh]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Suparti Suparti]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.522-531
<![CDATA[Exchange currency is one way for a country to be able to transact with the outside world. Fluctuating movement of the rupiah exchange currency was caused by many influencing factors, such as exports, imports, the money supply (JUB), inflation, and JCI. To find out the relationship, nonparametric regression modeling was carried out with a mixed kernel estimator and a multivariable truncated linear spline. Import variables were approached with kernel regression because the data patterns were random and spread out while the export variables, JUB, inflation, and the Jakarta Composite Index (JCI) were approached with spline regression because the data patterns changed at certain sub-intervals. The purpose of this study is to model exchange currency of the rupiah against the US dollar with a mixed kernel and spline truncated estimator. The parameter estimation method used is Ordinary Least Square (OLS). The multivariable linear truncated spline and kernel mix estimator depends on knot points and bandwidth. The best model is seen from the knot point and optimal bandwidth obtained by selecting the minimum Generalized Cross Validation (GCV). The best model is applied to data on the exchange currency of the rupiah against the US dollar with two optimal knot points resulting in value of 0.7627. The model performance evaluation was calculated using MAPE and the resulting MAPE value was 0.598%.]]>
<![CDATA[ANALISIS SENTIMEN PADA ULASAN APLIKASI INVESTASI ONLINE AJAIB PADA GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN MAXIMUM ENTROPY ]]>
<![CDATA[Fath Ezzati Kavabilla]]>;
<![CDATA[Tatik Widiharih]]>;
<![CDATA[Budi Warsito]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.542-553
<![CDATA[Investment is money or asset to earn profits in the future. Online investment applications are already available, one of which is Ajaib. A review of Ajaib’s application is needed to find out reviews given are positive or negative. Sentiment analysis in Ajaib is used to see the user's response to Ajaib’s performance which is divided into positive and negative classes. Sentiment analysis of the Ajaib’s reviews classification can be used with the Support Vector Machine and Maximum Entropy methods. Support Vector Machine on non-linear problems inserts the kernel into a high-dimensional space, to find a hyperplane that can maximize the distance between classes. The kernel used in SVM is the Radial Basis Function (RBF) kernel with gamma parameters of 0.002 and Cost (C) of 0.1; 1; 10. Maximum Entropy is a classification technique that uses the entropy value to classify data with the evaluation model used, namely 5-fold cross-validation. The algorithm which has the highest accuracy and kappa statistics is the best algorithm for classifying the sentiments of Ajaib users. The results using the Support Vector Machine algorithm show the overall accuracy is 85.75% and the kappa accuracy is 58.07%. The results using the Maximum Entropy algorithm show an overall accuracy of 83% and kappa accuracy of 50.5%. This shows that sentiment using the Support Vector Machine has a better performance than Maximum Entropy.]]>
<![CDATA[MENGATASI OVERDISPERSI DENGAN REGRESI BINOMIAL NEGATIF PADA ANGKA KEMATIAN IBU DI KOTA BANDUNG ]]>
<![CDATA[Hilma Mutiara Winata]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.616-622
<![CDATA[The maternal mortality rate in the city of Bandung is still a concern for the government, even though various health programs have been held to handle it. The very slight reduction in maternal mortality is a reason for further research to look for factors that have a significant effect. The data on maternal mortality cases usually contain a lot of zeros and follow the Poisson distribution so that they are solved with a Poisson regression model, however the model formed cannot be used because the model shows overdispersion with a deviation value of more than one. Therefore, to overcome this problem, negative binomial regression is used as a solution. This negative binomial regression model produces three predictor variables out of seven variables that have a significant effect on maternal mortality in the city of Bandung including pregnant women receiving FE1 (30 tablets), deliveries assisted by health personnel and postpartum service coverage. Then tested the goodness of the model from the negative binomial regression model by looking at the AIC value. The true negative binomial regression model is better because the AIC value is 109.4 which is smaller than 121.65 which is the AIC value of the Poisson regression model.]]>
<![CDATA[PEMODELAN TOPIK ULASAN APLIKASI NETFLIX PADA GOOGLE PLAY STORE MENGGUNAKAN LATENT DIRICHLET ALLOCATION ]]>
<![CDATA[Gina Rosalinda]]>;
<![CDATA[Rukun Santoso]]>;
<![CDATA[Puspita Kartikasari]]>
<![CDATA[ Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/j.gauss.11.4.554-561
<![CDATA[The vast amount of review data available on the Google Play Store can be utilized to extract hidden essential information. These reviews have an unstructured format that requiring particular methods to automatically collect and analyze the review data. Topic modeling is an extension of text analysis that can find main themes or trends hidden in large sets of unstructured documents. This study applies topic modeling with the Latent Dirichlet Allocation (LDA) method to Netflix application review data sourced from the Google Play Store web. The Latent Dirichlet Allocation (LDA) method is a generative probabilistic model from textual data that can explain the hidden semantic themes in the review document. This research aims to analyze hidden topics that application users discuss. These hidden topics contain essential valuable information for Netflix users and the company. Users can use this information to decide before using Netflix services. Meanwhile, Netflix can use this information to improve the quality of its services. This research use data from a web scraping Netflix review on the Google Play Store from January 2021–August 2021. The results of topic modeling show that of the twelve topics generated, the most discussed topic by users is payment methods.]]>
