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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

Author-ID : 3131513

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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PENENTUAN MODEL RETURN HARGA SAHAM DENGAN MULTI LAYER FEED FORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA RESILENT BACKPROPAGATION (Studi Kasus : Harga Penutupan Saham Unilever Indonesia Tbk. Periode September 2007 – Maret 2015) Riza Adi Priantoro; Dwi Ispriyanti; Moch. Abdul Mukid
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Determination of a return of stock price model is often associated with a process of forecasting for future periods. A method that can be used is neural network. The use of neural network in the field of forecasting can be a good solution, but the problem is how to determine the network architecture and the selection of appropriate training methods. One possible option is to use resilent back propagation algorithm. Resilent back propagation algorithm is a supervised learning algorithm to change the weights of the layers. This algorithm uses the error in the backward direction (back propagation), but previously performed advanced stage (feed forward) to get the error. This algorithm can be used as a learning method in training model of a multi-layer feed forward neural network. From the results of the training and testing on the share return of stock price PT. Unilever Indonesia Tbk. data obtained MSE value of 0.0329. This model is good to use because it provides a fairly accurate prediction of the results shown by the proximity of the target with the output.Keywords : return, neural network, back propagation, feed forward, back propagation algorithm, weight, forecasting.

ANALISIS REGRESI KEGAGALAN PROPORSIONAL DARI COX PADA DATA WAKTU TUNGGU SARJANA DENGAN SENSOR TIPE I (Studi Kasus di Fakultas Sains dan Matematika Universitas Diponegoro) Oka Afranda; Triastuti Wuryandari; Dwi Ispriyanti
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

One of the goals of studying in Higher Education Institutionis to obtain a job as soon as possible. A graduate is not required to be an unemployed. In Indonesia, the average period of waiting time for undergraduate (S1) to get the first job is 0 (zero) to 9 (nine) months. There are several factors have influenced the length of an undergraduate to get a job. They are Grade Point Average (GPA), Length of Study, etc. Therefore, it is important to know the factors influencing the waiting time of undergraduates to get a job. One method that can be used is the analysis of survival. Survival analysis is the analysis of survival time data from the initial time of the study until certain events occur. One method of survival analysis is Cox Proportional Hazard Regression. It is used to determine the relationship between one or more independent variables and the dependent variable. Cases raised in this study were the factors influencing the waiting time of graduates of the Faculty of Science and Mathematics, University of Diponegoro by using Type I data censoring. The conclusions state that the factors influencing the waiting time of graduates are Organization, Department, and Gender.Keywords: Waiting time of undergraduate, survival analysis, Cox Proportional Hazard, Regression, University of Diponegoro.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI UPAH MINIMUM KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN MODEL SPATIAL AUTOREGRESSIVE (SAR) Rahmah Merdekawaty; Dwi Ispriyanti; Sugito Sugito
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Spatial regression is the result of the development of linear regression method, wherein the location or spatial aspects of the analyzed data are also must be considered. The phenomenon that includes spatial data of which is the deployment of a minimum wage. Minimum Wages District/City is a minimum standard that is used by employers to provide wages to employees in its business environment on a district/city in any given year. Minimum Wages District/City is determined by considering the welfare of workers and the state of the local economy. Factors in worker welfare such as Worth Living Needs and the Consumer Price Index (CPI), while one important indicator to determine the economic conditions in the region within a certain time period is Gross Domestic Product (GDP). Modeling the influence of these factors can be determined by using multiple linear regression and spatial regression. Based on the data processing result, there is a spatial dependence in the Minimum Wages District/City variable in Central Java, so Spatial Autoregressive (SAR) method is used in this study. Variables that significantly affect the UMK in Central Java through multiple linear regression method and SAR is the Worth Living Needs (X1) and CPI (X2). The SAR model generates the value of R2 at 72.269% and AIC at 66.393, better than the multiple linear regression model that generates the value of R2 at 68% and AIC at 68.482.Keywords : Minimum Wages District/City, Worth Living Needs, CPI, GDP, multiple linear regression, spatial dependence, Spatial Autoregressive

PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH Endah Fauziyah; Dwi Ispriyanti; Tarno Tarno
Jurnal Gaussian Vol 10, No 4 (2021): 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.v10i4.33102

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074.

ANALISIS ANTRIAN PASIEN INSTALASI RAWAT JALAN RSUP Dr. KARIADI BAGIAN POLIKLINIK, LABORATORIUM, DAN APOTEK Rany Wahyuningtias; Dwi Ispriyanti; Sugito Sugito
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Queue process is a process of the coming of a customer to a service facility, then waiting in line (queue) when the officers busy, and leaving the place after getting the service. Patient’s line at RSUP DR. Kariadi is a lot enough then it will making the service from the hospital isn’t optimal as a result. Hence, it needed a queue model to optimize the service to patient. From the result of the analysis in RSUP Dr. Kariadi it gives the best queue models is in polyclinic area second floor, laboratory, and pharmacy.

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

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

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

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

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

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.

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'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

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