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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.
Arjuna Subject : -
Articles 733 Documents
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PEMODELAN FAKTOR EKONOMI MAKRO TERHADAP HARGA SAHAM TELKOM MENGGUNAKAN REGRESI SPLINE TRUNCATED MULTIVARIABEL DILENGKAPI GUI R Lulu Maulatus Saidah; Suparti Suparti; Sudarno Sudarno
Jurnal Gaussian Vol 11, No 3 (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.11.3.344-354

Abstract

Stock prices are an important thing that investors should know before investing. Volatile stock prices require investors to know the factors that influence their changes. Stock price instability makes it very difficult for investors to make investments and affects the integrity they get. One of the factors that affect stock prices is macroeconomic factors consisting of rupiah exchange rate (X1), inflation (X2), and SBI interest rate (X3). A statistical method that can be used to model fluctuating data is spline nonparametric regression. This study aims to model macroeconomic factors against Telkom's stock price using multivariable truncated spline nonparametric regression with optimal knot point selection methods that minimize Generalized Cross Validation (GCV). Many knots used are a combination of 1 and 2 and the order used is a combination of 2, 3, and 4. The best multivariable truncated spline model is achieved on a knot combination (2,2,2) with the order X1, X2, X3 being 3, 2, 2 which results in an R2 value of 92.71% included in the strong model criteria. In the evaluation of model performance obtained a MAPE value of 1.857% which shows the model has excellent forecasting ability. In this study, a Graphical User Interface (GUI) program was formed R that can facilitate data analysis and produce more attractive display output.
ANALISIS REGRESI FAKTOR PANEL DINAMIS BLUNDELL-BOND DENGAN ESTIMASI SYSTEM-GENERALIZED METHOD OF MOMENT PADA SAHAM FARMASI DI BEI Hanifah Nur Aini; Dwi Ispriyanti; Suparti Suparti
Jurnal Gaussian Vol 11, No 3 (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.11.3.447-457

Abstract

The pharmaceutical sector has become a concern during the Covid-19 pandemic because of the large use of drugs. Companies need to improve financial performance to increase their share prices and investors need analysis to predict future stock prices. This study aims to analyze the influence of stock prices on 10 pharmaceutical companies on the Indonesia Stock Exchange during the third quarter of 2020 to the third quarter of 2021. Based on previous research, the factors that are thought to have an effect on changes in stock prices are internal financial ratios (ROA, ROE, NPM, GPM, EPS, PER, BV, PBV, DAR, DER, CR, QR, Cash Asset Ratio) and external inflation, exchange rates, interest rates. The method used in this research is dynamic panel factor regression analysis with GMM (Generalized Method of Moment) estimation. Factor analysis to reduce the independent variables to form a factor score which is then entered into the regression. The regression model was obtained from the comparison of Arellano-Bond GMM and Blundell-Bond System. The GMM system is the development of Arellano-Bond which will produce more efficient estimates when the sample time series is short. The results of the study were obtained 3 factor scores with a total variance of 81.757% from the elimination of 6 variables that had MSA <0.5. The best model is the Blundell-Bond Twostep System which fulfills the model assumptions with RMSE 803.276.
ANALISIS METODE ANTREAN DAN SIMULASI MONTE CARLO PADA ANTREAN DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL (DISDUKCAPIL) KOTA SALATIGA DILENGKAPI GUI-R Diyah Rahayu Ningsih; Sugito Sugito; Agus Rusgiyono
Jurnal Gaussian Vol 11, No 3 (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.11.3.418-428

Abstract

One of the services that often occurs in everyday life is the queue service. Queues can arise due to delays in a service system in providing a service, resulting in a row of a group of people to get a service. The queue analyzed in this study is a queue in The Salatiga City Disdukcapil. The parameters on which this research is based are the number of arrivals (λ) and service time (μ) of visitors who arrive. The methods used are queue analysis and Monte Carlo simulation. The Monte Carlo method provides more effective results at each counter than using queue analysis. The result of this study is a decrease in the utilization rate of service facilities, so that it is accompanied by a decrease in the size of system performance for the calculation of Lq, Ls, Wq, and Ws. Decreases in utilization rates and system performance measures at each counter make an increase in the probability of idle systems at each counter. The model generated by the sample data with the Monte Carlo simulation data tends to be the same, namely for counter 1,2,3,4, counter 5 model (G/G/c):(GD/¥/¥), and for counter 6 with queuing model ( G/M/1):(GD/¥/¥).
PENERAPAN DIAGRAM KONTROL MEWMA DALAM PENGENDALIAN KUALITAS PRODUKSI KERIPIK SINGKONG PADA UMKM DI KOTA SEMARANG Nesari Nesari; Mustafid Mustafid; Tatik Widiharih
Jurnal Gaussian Vol 11, No 3 (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.11.3.355-365

Abstract

Quality is the main thing that needs to be considered by every company. Ceriping Bintang Putra Bu Slamet is an UMKM (Usaha Mikro, Kecil dan Menengah) that produces cassava chips. During production, there are three quality characteristics, namely large crumbs defects, small crumbs, and chips sticking together. It is important to control these defects to produce quality products according to customer needs. This research was conducted from July to August 2021. The purpose of this study was to control the production quality of cassava chips using the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart and multivariate process capability analysis. The MEWMA control chart is used to detect the shift in the process average which is more sensitive using weights (λ), while the process capability analysis is used to determine the process performance. The implementation of the MEWMA control chart is carried out in two stages, namely phase I control to obtain the optimal weighting and control limits so that it can be used in phase II control to monitor the average process for the next period. Based on the results of the analysis, the optimal weighting is λ =0,4 with BKA=201,7434, GT=113,538, and BKB=0 in phase I control. Then, the results of phase II control show a shift in the average process in a better direction. In addition, the results of the process capability analysis show an improvement in the performance of the production process from July 2021 to August 2021 with MCpm values of 0,535 and 1,147
PERBANDINGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN EXTREME LEARNING MACHINE UNTUK PERAMALAN JUMLAH BARANG YANG DIMUAT PADA PENERBANGAN DOMESTIK DI BANDARA UTAMA SOEKARNO HATTA Kevin Togos Parningotan Marpaung; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 11, No 3 (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.11.3.439-446

Abstract

The loading of goods carried out at the airport is an essential part of the transporting goods system. In this regard, it is necessary to have a prediction to make the right policy or to solve the problems that occur. Holt Winter's Exponential Smoothing, which one of the classic methods of analyzing time series data, and Extreme Learning Machine which is part of the artificial neural network method, are methods that can be used as a tool for forecasting problems. Holt Winter's Exponential Smoothing uses three times of smoothing on related data, which are level smoothing, trend smoothing, and season smoothing, while Extreme Learning Machine goes through three stages, which are normalization, training, and denormalization. In measuring the error rate in related forecasting, the symmetric Mean Absolute Percentage Error (sMAPE) value is used. The Holt Winter's Exponential Smoothing method Additive model produces a sMAPE value of 26.14%; while the Multiplicative model with the same method resulted in the sMAPE value of 25.69%. For the Extreme Learning Machine method, the sMAPE value is 49.85%. Based on the accuracy test using the sMAPE value, Holt Winter's Exponential Smoothing method Multiplicative model is the better method than Extreme Learning Machine
PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI JAWA TENGAH MENGGUNAKAN METODE REGRESI RIDGE DAN REGRESI STEPWISE Erna Sulistianingsih; Suparti Suparti; Dwi Ispriyanti
Jurnal Gaussian Vol 11, No 3 (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.11.3.468-477

Abstract

The Human Development Index (HDI) is an important indicator in measuring the success of national development. Central Java with a high population can be considered as an obstacle and a driver of development. To find out the factors that affect HDI, it is necessary to make a model. One of the statistical methods that can be used is multiple linear regression analysis. However, in modeling multiple linear regression there are assumptions that must be met, namely linearity, normality, homoscedasticity, non-autocorrelation, and non-multicollinearity. If the non-multicollinearity assumption is not met, then another alternative is needed to estimate the regression parameters. Several methods that can be used are ridge regression and stepwise regression methods. The best model selection is done by looking at the smallest Mean Square Error (MSE) value. In this study, ridge and stepwise regression were applied to Central Java HDI data in 2021 and the factors that influence it, namely life expectancy at birth, expected years of schooling, average length of schooling, per capita expenditure, percentage of poor people, and unemployment open. Based on the Variance Inflation Factor (VIF) value of more than 10, it can be concluded that there is a multicollinearity violation. Modeling with stepwise regression produces the best model, with the smallest MSE value. The R square model value of 0,99 indicates that the model is included in the criteria for a strong model.
IMPLEMENTASI ALGORITMA K-MEDOIDS DAN K-ERROR UNTUK PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN JUMLAH PRODUKSI PETERNAKAN TAHUN 2020 Fahrur Rozzi Iskak; Iut Tri Utami; Triastuti Wuryandari
Jurnal Gaussian Vol 11, No 3 (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.11.3.366-376

Abstract

The livestock sub-sector is one of the sub-sectors that contribute to the national economy and can significantly absorb labour so that it can be relied upon in efforts to improve the national economy. One of the steps used to increase livestock production in each region in Central Java Province is regional mapping. Cluster analysis is one of the regional mapping methods that can increase livestock production by grouping regencies/cities with characteristics of the same level of livestock production based on the type of livestock production. The k-error and k-medoids method is a non-hierarchical cluster analysis method, where the k-error is a method developed to overcome the problem of data measurement errors in classical cluster analysis, while the k-medoids is a method used to overcome the problem of outliers contained in the data. The validity test of the standard deviation ratio and the WB Index was used to determine the quality of the clustering results. The small validity value of the standard deviation ratio and the WB Index shows the best results of clustering and selecting method. Based on the results of the clustering, the optimal cluster was obtained at k=7 using the k-medoids algorithm, where the validation value of the standard deviation ratio=0.773 and WB Index=0.531.
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.

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