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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,
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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 15 Documents
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Search results for , issue "Vol 11, No 4 (2022): Jurnal Gaussian" : 15 Documents clear
PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) DENGAN JARAK EUCLIDEAN DAN JARAK MANHATTAN (STUDI KASUS : KEMATIAN BAYI NEONATAL DI JAWA TENGAH TAHUN 2018-2020) Riszki Bella Primasari; Agus Rusgiyono; Dwi Ispriyanti
Jurnal Gaussian Vol 11, No 4 (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.11.4.478-487

Abstract

Neonatal is a condition of babies from birth to 28 days. Data on Indonesia's health profile in 2020 showed that 72% of the number of deaths of toddlers occurred during the neonatal period and Central Java became the highest province of cases. Factors that are suspected to influence are the number of low birth weight babies (X1), the number of obstetric complications (X2), the number of Puskesmas (X3), the number of Posyandu (X4), the number of exclusive breastfeeding babies 0-6 months (X5), the number of pediatricians (X6), the number of ambulance cars (X7). Linear regression modeling on the number of neonatal infant deaths in Central Java has a heteroskedasticity problem so that Geographically Weighted Regression (GWR) is used. The distances used are Euclidean and Manhattan as well as the weighting function using Exponential and Tricube Kernel with Fixed Bandwidth. GWR modeling shows that not all independent variables are local, so Mixed Geographically Weighted Regression (MGWR) is used. The results of the GWR analysis with both distances and the two variable weighting functions are not local, including X2, X5, and X7. MGWR distance Manhattan Fixed Tricube Kernel became the better model, as the AICC value was smaller.
KLASIFIKASI PENYAKIT HIPERTENSI MENGGUNAKAN METODE SVM GRID SEARCH DAN SVM GENETIC ALGORITHM (GA) Fithroh Oktavi Awalullaili; Dwi Ispriyanti; Tatik Widiharih
Jurnal Gaussian Vol 11, No 4 (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.11.4.488-498

Abstract

Hypertension is an abnormally high pressure that occurs inside the arteries. Hypertension increased by 8.3% from 2013 based on health research in 2018. Some of the factors that cause hypertension include gender, age, salt consumption, cigarette consumption, cholesterol levels and a family history of hypertension. The data in this study are data on normal and hypertensive patients at the Padangsari Health Center for the period of July – December 2021. This study will classify blood pressure with the aim of obtaining the results of the accuracy of the classification of the methods used. The method used in this study is a support vector machine (SVM). SVM is a well-known algorithm, producing optimal solutions to classification problems. SVM uses kernel functions for separable nonlinear data. The displacement kernels used in this study are linear and RBF. SVM has the disadvantage of determining the best parameters, to overcome these weaknesses developed the method of finding the best parameters. The search for the parameters of this study used grid search and genetic algorithm (GA).  Grid search has the advantage of producing parameters that are close to the optimal value, while GA has the advantage of being easy to find global optimum values. This study will compare the classification results of the SVM grid search and SVM GA methods. The results of this study obtained the method that has the best accuracy, namely SVM grid search using a radial base function (RBF) kernel with an accuracy of 89.22%.
ANALISIS SENTIMEN PENERAPAN PPKM PADA TWITTER MENGGUNAKAN NAÏVE BAYES CLASSIFIER DENGAN SELEKSI FITUR CHI-SQUARE Pualam Wahyu Ratiasasadara; Sudarno Sudarno; Tarno Tarno
Jurnal Gaussian Vol 11, No 4 (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.11.4.580-590

Abstract

Dissemination of information related to the implementation of PPKM takes place very quickly, especially on social media networks. Positive and negative news certainly has an impact on public opinion or sentiment on the implementation of PPKM. Sentiment analysis is needed to determine behavior or opinions in the form of reviews, ratings, or tendencies of the author towards a particular topic. In this study, the data used is public opinion on Twitter social media with the keyword "PPKM" from November 2, 2021 to November 8, 2021 and obtained data as many as 12,616 tweets which then deleted duplicate data to become 6,465 data. Data classification was performed using Naïve Bayes with Chi-Square feature selection and the data were classified into positive and negative classes. The results of the classification performance using Nave Bayes with Chi-Square feature selection obtained an accuracy of 83% which means that the Nave Bayes classification model with Chi-Square feature selection is quite effective in classifying public opinion on the implementation of PPKM.
PERBANDINGAN SMOTE DAN ADASYN PADA DATA IMBALANCE UNTUK KLASIFIKASI RUMAH TANGGA MISKIN DI KABUPATEN TEMANGGUNG DENGAN ALGORITMA K-NEAREST NEIGHBOR Dinda Virrliana Ramadhanti; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 11, No 4 (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.11.4.499-505

Abstract

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.
ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN ALGORITMA GRID SEARCH TIME SERIES CROSS VALIDATION UNTUK PREDIKSI JUMLAH KASUS TERKONFIRMASI COVID-19 DI INDONESIA Anindita Nur Safira; Budi Warsito; Agus Rusgiyono
Jurnal Gaussian Vol 11, No 4 (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.11.4.512-521

Abstract

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.
ANALISIS PENGARUH KUALITAS PELAYANAN TERHADAP KEPUASAN PENUMPANG BRT TRANS SEMARANG MENGGUNAKAN PARTIAL LEAST SQUARE (PLS) Irma Dwi Tyana; Tatik Widiharih; Iut Tri Utami
Jurnal Gaussian Vol 11, No 4 (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.11.4.591-604

Abstract

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%. 
PENERAPAN ALGORITMA BACKPROPAGATION DAN OPTIMASI CONJUGATE GRADIENT UNTUK KLASIFIKASI HASIL TES LABORATORIUM Wahyu Tiara Rosaamalia; Rukun Santoso; Suparti Suparti
Jurnal Gaussian Vol 11, No 4 (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.11.4.506-511

Abstract

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%.
PENGARUH KONVEKSITAS TERHADAP SENSITIVITAS HARGA JUAL DAN DELTA-NORMAL VALUE AT RISK (VAR) PORTOFOLIO OBLIGASI PEMERINTAH MENGGUNAKAN DURASI EKSPONENSIAL Putri Devitasari; Di Asih I Maruddani; Puspita Kartikasari
Jurnal Gaussian Vol 11, No 4 (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.11.4.532-541

Abstract

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.
PENERAPAN SMOOTHING B-SPLINES PADA HUBUNGAN ANTARA PERTUMBUHAN EKONOMI DAN TINGKAT KEBAHAGIAAN Muhammad Fajar; Eko Fajariyanto
Jurnal Gaussian Vol 11, No 4 (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.11.4.605-615

Abstract

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.
PEMODELAN KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT MENGGUNAKAN REGRESI NONPARAMETRIK CAMPURAN KERNEL DAN SPLINE Khansa Amalia Fitroh; Rukun Santoso; Suparti Suparti
Jurnal Gaussian Vol 11, No 4 (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.11.4.522-531

Abstract

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%.

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