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All Journal Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Ilmu Dasar Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan
Dwi Ispriyanti
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Transportation Other

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PEMODELAN JUMLAH WISATAWAN DI JAWA TENGAH MENGGUNAKAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE - SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR) Innosensia Adella; Dwi Ispriyanti; Hasbi Yasin
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35473

Abstract

Space-time model is a model that can explain data with spatial and time characteristics. The Generalized Space Time Autoregressive (GSTAR) model is one of the generalized space-time models from the Space Time Autoregressive (STAR) model. The GSTAR model is more flexible when dealing with areas that have heterogeneous characteristics than the STAR model. The GSTAR model models time series data in multiple regions at once. This model can then be used to model data on the number of tourists in four regions in Central Java, namely Semarang, Jepara, Magelang and Semarang district for the 2014 to 2019 period. in Central Java. On the residual model, the Lagrange Multiplier Test is carried out and it is known that there is a correlation between the residuals. The modeling was continued by using the Generalized Space Time Autoregressive – Seemingly Unrelated Regression (GSTAR-SUR) model. GSTAR-SUR is one of the more efficient models used to model GSTAR with correlated residuals. Residual through the white-noise assumption test, it is found that the appropriate model is the GSTAR-SUR(2,1) model. This model can then be used in forecasting data on the number of tourists in Semarang, Jepara, Magelang and Semarang district in the next period
ANALISIS SURVIVAL PADA DATA KEJADIAN BERULANG MENGGUNAKAN PENDEKATAN COUNTING PROCESS Ulya Tsaniya; Triastuti Wuryandari; 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.11.3.377-385

Abstract

Asthma is a disorder that attacks the respiratory tract and causes bronchial hyperactivity to various stimuli characterized by recurrent episodic symptoms such as wheezing, coughing, shortness of breath, and heaviness in the chest. Asthma sufferers will experience exacerbations, namely episodes of asthma recurrence which gradually worsens progressively accompanied by the same symptoms. The length of time a person experiences an exacerbation can be influenced by various factors. To analyze this, the Cox regression model can be used which is within the scope of survival analysis where time is the dependent variable. In the survival analysis, asthma exacerbations were identical/recurrent events where the individual experienced the event more than once during the study. If the survival data contains identical/recurrent events, the analysis uses a counting process approach. Counting Process is an approach used to deal with survival data with identical recurrent events, meaning that recurrences are caused by the same thing, which in this case is the narrowing of the bronchioles in asthmatics. The purpose of this study was to determine the factors that cause asthma exacerbations by using a counting process approach as a data treatment for recurrent events at Diponegoro National Hospital. Based on the results of the analysis, the factors that influence the length of time a patient experiences an exacerbation are the age, gender, and type of cases
PENGARUH KUALITAS LAYANAN DAN CITRA MEREK TERHADAP KEPUASAN PENGGUNA YOUTUBE PREMIUM MENGGUNAKAN PARTIAL LEAST SQUARE Ajeng Dwi Rizkia; Dwi Ispriyanti; Sugito Sugito
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.11.3.323-331

Abstract

As one of the largest digital service providers in the world, YouTube certainly makes breakthroughs to maintain user interest in accessing videos through YouTube, one of which is by creating the YouTube Premium service. This research was conducted to determine the extent to which these services can provide a sense of satisfaction for its users, because as a digital service provider company, YouTube is very dependent on user satisfaction. User satisfaction is influenced by service quality and brand image. In this study, service quality, brand image, and service user satisfaction act as latent variables. To test the predictive relationship between indicator variables and variables that cannot be measured directly (latent variables) by seeing whether there is a relationship or influence between these variables using the obtained modeling can be done using the Partial Least Square method. Therefore, to determine the effect of service quality and brand image on YouTube Premium user satisfaction, an analysis was conducted using the Partial Least Square method. The research data was obtained by distributing questionnaires to 150 YouTube Premium users in Indonesia. The results of the analysis show that service quality and brand image have a significant effect on YouTube Premium user satisfaction.
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.
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.
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%.
PENERAPAN METODE POISSON EXPONENTIALLY WEIGHTED MOVING AVERAGE (PEWMA) UNTUK MEMBUAT BAGAN PENGENDALI VARIABEL BERDISTRIBUSI POISSON Nida Adelia; Mustafid Mustafid; Dwi Ispriyanti
Jurnal Gaussian Vol 12, No 1 (2023): 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.12.1.71-80

Abstract

Airplane is a mode of transportation that has an accident risk. Aircraft accidents are recorded to occur almost every year in Indonesia. The Poisson distribution is used to model the number of aircraft accidents that occur each year because they have a fixed time and independent. Statistical quality control is applied as a method to monitor the number of fatal aircraft accidents in Indonesia that are within control limits. One method to carry out quality control is to use a control chart. This study aims to apply the Poisson Exponentially Weighted Moving Average (PEWMA) method to create a control chart with a case study of the number of fatal airplane accidents in Indonesia from 1962 to 2021 with a Poisson distribution. The EWMA control chart is used to monitor the average or process variability and is considered effective in detecting small shifts in the process (the shift is said to be small if the shift is less than 1.5σ). The calculation of Average Run Length (ARL) is performed to test the performance of the PEWMA control chart. Control charts with smaller out-of-control ARLs are considered superior and can detect process shifts more quickly than other control charts. Based on the results of the calculation of the ARL value, it was found that the weight of 0.3 is the optimal weight with the smallest ARL value of 1.138 which is able to describe the state of the data on fatal aircraft accidents in Indonesia. The control chart with the optimal weight shows the data on fatal aircraft accidents in Indonesia that are tolerated equal to one.
PEMODELAN INDEKS HARGA PERDAGANGAN BESAR (IHPB) SEKTOR EKSPOR MENGGUNAKAN ARFIMA-GARCH Gandhes Linggar Winanti; Dwi Ispriyanti; Sugito Sugito
Jurnal Gaussian Vol 12, No 1 (2023): 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.12.1.52-60

Abstract

Indonesia's price index serves as a barometer for the nation's economic condition. One of the Indonesia’s price index is Wholesale Price Index (WPI). WPI is a price index that tracks the average change in wholesale prices over time. Time series analysis can be used for forecasting because WPI is one of the time series data. WPI is long memory, which is a condition in which data from different time periods have a high link despite being separated by a large amount of time. The Autoregressive Fractional Integrated Moving Average (ARFIMA) model can be used to overcome this feature when modeling time series data. The assumption of constant error variance is not fulfilled in the IHPB data analysis, indicating that the data is heteroscedastic. The GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) model is one of the models used to overcome heteroscedasticity. The data used is the export sector of WPI from January 2003 to June 2021. The best model for forecasting WPI is ARFIMA(1,b,2) – GARCH(1,1) with b=0,7345333,  and MAPE value is 3,150875%.
PENERAPAN METODE FUZZY TIME SERIES MENGGUNAKAN PARTICLE SWARM OPTIMIZATION ALGORITHM UNTUK PERAMALAN INDEKS SAHAM LQ45 Arya Despa Ihsanuddin; Dwi Ispriyanti; Tarno Tarno
Jurnal Gaussian Vol 12, No 1 (2023): 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.12.1.10-19

Abstract

Stocks have a volatile nature and it is difficult to predict the ups and downs. Therefore, stock data forecasting is done by investors to get a picture of future results. Fuzzy Time Series is a time series method that is suitable for forecasting fluctuating stock data because it does not require the fulfillment of assumptions such as normality and stationarity, but the Fuzzy Time Series method has weaknesses in determining intervals. So that in this study, interval optimization will be carried out on Fuzzy Time Series with Particle Swarm Optimization algorithm to predict LQ45 stock index data, Particle Swarm Optimization algorithm is used because it produces more optimal interval values compared to other optimization methods such as Genetic Algorithm. The data to be used is the closing price of the LQ45 stock index on January 5, 2020 to December 26, 2021. Forecasting using the Fuzzy Time Series method produces a SMAPE value of 1.53%, then after optimization using the Particle Swarm Optimization algorithm, the SMAPE value decreases to 1, 27%. Therefore, it can be concluded that optimization using Particle Swarm Optimization on Fuzzy Time Series produces a more optimal forecasting value. 
Co-Authors A Rusgiyono Abdul Hoyyi Agus Rusgiyono Agustinus Salomo Parsaulian Ain Hafidita Ajeng Dwi Rizkia Alan Prahutama Alan Prahutama Alvi Waldira Ana Kartikawati Anisa Septi Rahmawati Anjan Setyo Wahyudi Annisa Ayu Wulandari Arief Rachman Hakim Arkadina Prismatika Noviandini Taryono Arya Despa Ihsanuddin Arya Huda Arrasyid Atika Elsadining Tyas Aulia Ikhsan Avia Enggar Tyasti Azizah Mulia Mawarni Berta Elvionita Fitriani Bitoria Rosa Niashinta Budi Warsito Budi Warsito Cylvia Evasari Margaretha Dedi Nugraha Di Asih I Maruddani Di Asih I Maruddani Diah Safitri Diah Safitri Diah Wulandari Dita Ruliana Dwi Rahmayani, Dwi Dyan Anggun Krismala Dydaestury Jalarno Eis Kartika Dewi Endah Fauziyah Erna Sulistianingsih Erna Sulistio Evi Yulia Handaningrum Fadhilla Atansa Tamardina Firda Dinny Islami Firdha Rahmatika Pratami Fithroh Oktavi Awalullaili Gandhes Linggar Winanti Gera Rozalia Ghina Nabila Saputro Putri Hanifah Nur Aini Hasbi Yasin Hasbi Yasin Henny Widayanti, Henny Ilham Maggri Imam Desla Siena Innosensia Adella Irawati Tamara Iut Tri Utami Jesica, Haniela Puja Kishatini Kishartini Lifana Nugraeni Lingga Bayu Prasetya M. Ali Ma&#039;sum Marlia Aide Revani Masfuhurrizqi Iman Maulida Azkiya, Maulida Maulida Najwa, Maulida Merinda Pangestikasari Moch. Abdul Mukid Moch. Abdul Mukid Muhammad Fitri Lutfi Anshari Muhammad Rosyid Abdurrahman Muhammad Zidan Eka Atmaja Mustafid Mustafid Mustafid Mustafid Nanci Rajagukguk, Nanci Nandang Fahmi Jalaludin Malik Nida Adelia Nidaul Khoir Nova Nova Noviana Nurhayati Nurwihda Safrida Umami Oka Afranda Pandu Anggara Pritha Sekar Wijayanti Puput Ramadhani Pusphita Anna Octaviani Puspita Kartikasari Putri Fajar Utami Rafida Zahro Hasibuan Rahafattri Ariya Fauzannissa Rahmah Merdekawaty Rahmaniar, Ratna Rany Wahyuningtias Ratih Nurmalasari, Ratih Ratna Pratiwi Ria Sutitis Rio Tongaril Simarmata Riszki Bella Primasari Rita Rahmawati Rita Rahmawati Riza Adi Priantoro Riza Fahlevi Sa'adah, Alfi Faridatus Sania Anisa Farah Setiani Setiani Sherly Candraningtyas Sindy Saputri Sisca Agustin Diani Budiman Sri Maya Sari Damanik Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sugito Sugito Suhendra, Muhammad Arif Suparti Suparti Suparti Suparti Suparti, S. Suryaningrum, Fahlevi Syilfi Syilfi Sylvi Natalia P P Tarno Tarno Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Tatik Widiharih Tiani Wahyu Utami Triastuti Wuryandari Triastuti Wuryandari Trimono Trimono Ulya Tsaniya Umiyatun Muthohiroh Warsito Budi Yani Puspita Kristiani Yashmine Noor Islami Yuciana Wilandari Yuciana Wilandari