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Yuliani Setia Dewi
Jurusan Matematika FMIPA Universitas Jember
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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The Efficiency of First (GEE1) and Second (GEE2) Order “Generalized Estimating Equations” for Longitudinal Data Rizka Dwi Hidayati; I Made Tirta; Yuliani Setia Dewi
Jurnal ILMU DASAR Vol 15 No 1 (2014)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (799.799 KB) | DOI: 10.19184/jid.v15i1.553

Abstract

The approach of GEE focuses on a linear model for the mean of the observations in the cluster without full specification  the distribution of full-on observation. GEE is a marginal model where is not based on the full likelihood of the response, but only based on the relationship between the mean (first moment) and variance (second moment) as well as the correlation matrix. The advantage of  GEE is that the mean of  parameter are estimated consistently regardless whether  the correlation structure is specified correctly or not, as long as the mean has the correct specifications. However, the efficiency may be reduced when the working correlation structure is wrong. GEE was designed to focus on the marginal mean and correlation structure as nuisiance treat. Implementation of GEE is usually limited to the number of working correlation structure (eg AR-1, exchangeable, independent, m-dependent and unstructured). To increase the efficiency of the GEE, has introduced a variation called the Generalized Estimating Equations order 2 (GEE2). GEE2 has been introduced to overcome the problem that considers correlation GEE as nuisiance, by applying the second equation to estimate covariance parameters and  solved simultaneously with the first equation. This study used simulation data which are designed based on the the AR-1 and Exchangeable correlation structure, then estimation are done  using theAR1 and exchangeable. For GEE2,  estimation done by adding model for correlation link. The result is a link affects the efficiency of the model correlation is shown with standard error values ​​generated by GEE2 method is smaller than the GEE method.
Comparison of Arima Method and Artificial Neural Network Method to Predict Productivity Rice In Panti District Fendi Setiawan; Yuliani Setia Dewi; Mohamad Fatekurohman
Edumaspul: Jurnal Pendidikan Vol 6 No 2 (2022): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (765.496 KB) | DOI: 10.33487/edumaspul.v6i2.4681

Abstract

Rice production is a community activity to produce rice, it is intended to maintain food security in the future. The aim of this research is to develop the best model for forecasting rice production based on ARIMA (Autoregressive Integrated Moving Averages) and ANN (Artificial Neural Network) approaches. The results will be compared with the error rate values of the ARIMA and ANN methods with the available data. The data used in this study is data on rice production in Panti District, Jember Regency. The level of forecasting accuracy produced by each forecasting method is measured by the criteria of MAPE (Mean Absolute Percentage Error), MSE (Mean Square Error) and RMSE (Root Mean Square Error). The results showed that from the forecasting method used in this study, the ARIMA (1,0,1) (1,0,2) method is the best forecasting method for the best rice harvest area in Panti District, Jember Regency with an average MAPE value is 0.05668374, MSE is 5.587553, and RMSE is 2.3638. Meanwhile, forecasting rice productivity using the ANN BP method (7,(7,3),1) is a fairly good forecasting method with an average MAPE value of 0.05703856 MSE of 4.828465, and RMSE of 2.197377. Therefore, the ARIMA model (1,0,1) (1,0,2)[12] is quite effective for predicting the amount of rice production in Panti District, Jember Regency, East Java Province for the next few years.
KLASIFIKASI DATA DIAGNOSIS COVID-19 MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN GENERALIZED LINEAR MODEL (GLM) Yeni Rismawati; I Made Tirta; Yuliani Setia Dewi
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Covid-19 is still a global concern. From the first time, this virus was detected, on December 31, 2019. As of March 20, 2022, there were 460 million positive cases of Covid-19, with 6.06 million deaths worldwide. The high number of Covid-19 cases is due to the rapid spread of this virus. One way to prevent the spread of this virus is by early detection of the disease and mapping the influence factors .The classification method with the support vector machine (SVM) method in machine learning can predict individuals diagnosed as positive for Covid-19 and who do not use the factors considered influential. Traditionally this can also be done with a generalized linear model (GLM). This study aims to compare two methods (SVM and GLM) in predicting individuals diagnosed as positive for Covid-19. In addition, this study also conducted an ensemble between SVM and GLM to determine whether the ensemble performed could produce better accuracy than the single classifier (SVM and GLM). The results showed that the accuracy with SVM and GLM was relatively high. However, SVM is slightly superior with 98.91% accuracy, and GLM with 95.64% accuracy. Meanwhile, the ensemble of both models achieved 98.91% accuracy, as high as SVM. Keywords: Covid-19, Klasifikasi, Machine Learning SVM, GLM
PENERAPAN MODEL LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM) UNTUK PERAMALAN KASUS COVID-19 DI INDONESIA Lutfi Ardining Tyas; I Made Tirta; Yuliani Setia Dewi
Jurnal Gaussian Vol 12, No 2 (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.12.2.304-313

Abstract

Forecasting is about predicting the future based on historical data and any information that might affects the forecasts. This article applies the LSSVM model to forecast Covid-19 cases in Indonesia. The purpose of this study is to find out how the LSSVM model applied and the model performances for forecasting Covid-19 cases in Indonesia, using time series data and the factors that influence it, as input features. The factor data used in this study are mobility data and daily fully vaccinated data. The research has three main objectives; first, calculate the correlation between confirmed cases data and past data (lag) of mobility and vaccination. Second, is the selection of input features based on the highest correlation coefficient value of each variable. Third, do LSSVM modeling and Covid-19 case forecasting with the optimal model. RBF kernel and grid-search algorithm with 10-fold cross-validation are used to tune model parameters. The results show that the LSSVM model provides good performance for Covid-19 forecasting and the optimal LSSVM model for forecasting Covid-19 cases in Indonesia is using time lag 14 for the mobility factor and time lag 24 for the vaccination factor.
PERAMALAN PERTUMBUHAN PENDUDUK KABUPATEN SITUBONDO DENGAN MODEL ARIMA, DERET ARITMATIK, DERET GEOMETRI DAN DERET EKSPONENSIAL “THE FORECASTING GROWTH OF THE POPULATION IN SITUBONDO BY USING ARIMA, ARITMATICS, GEOMETRICS AND EXPONENTIAL” As’ad, A; Tirta, I Made; Dewi, Yuliani Setia
Kadikma Vol 4 No 1 (2013): April 2013
Publisher : Department of Mathematics Education , University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/kdma.v4i1.1123

Abstract

Abstract. ARIMA models as the population forecasting in Situbondo is a model of ARIMA(3, 3, 3) and mathematically, it is stated as; =2,445–1,6632–0,148+0,9732–1,0746+ 0,4676+– 1,0635. Forecasting the population in Situbondo is 667646 people in 2012 and in 2013 is 677852 people. Some other approaches in determining the population is the Arithmetic growth formula, the result of forecasting in 2012 is 657540 people and in 2013 is 661626 people, Based on Geometric growth formula, the result of forecasting in 2012 is 19696459 people and in 2013 is 35211214 people and Based on Exponential growth formula the result of forecasting in 2012 is 657611 people and in 2013 is 661799 people. If we compare the data of the forecasted result of ARIMA model with the Aritmatics growth formula and Exponential growth formula, show that the data of the population with the last ten actual data is relatively similiar.The closed last ten actual data forecasting of population is the aritmatics growth formula, whereas the data of the population result for next two year based on the Geometric growth formula got the forecasted result which is different from the forecasted result of ARIMA model, Aritmatics growth formula and Exponential growth formula. Key Words:forecasting, arima models, arithmetic, geometric, exponential
POLA-POLA JALUR PADA PATH ANALISYS UNTUK ANALISIS FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP NILAI UN SMA DI KABUPATEN LUMAJANG Isdarmawan, Agus; Tirta, I Made; Dewi, Yuliani Setia
Kadikma Vol 4 No 1 (2013): April 2013
Publisher : Department of Mathematics Education , University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/kdma.v4i1.1118

Abstract

Abstract. Path analysis is a technique to analyze the effect of free and bound variables in which every variable correlates or associates with cause and effect directly or indirectly. This study was conducted to determine some factors which influenced National Examination at Senior High School in Lumajang. The Data were analyzed using path analysis. The results of the study were explained as follows: 1. The correlation of variables in path analysis followed the pattern of direct, indirect and mixed. 2. Path analysis could be applied to the analysis of the relationship between exogenous variables (Practical Training (X1), Assignment (X2), and Daily Test (X3)) with endogenous variables (Mid-Term Test (Y1), Final-Term Test (Y2), and National Examination (Z)). Daily Test (X3) contributed directly to Mid-Term Test (Y1). On the other hand, Practical Training (X1) and Daily Test (X3) did not contribute significantly to the Final-Term Test (Y2). 3. Assignment (X2) has direct and indirect influence on National Examination (Z) through Final-Term Test (Y2). 4. Daily Test (X3) did not have a direct influence to Final-Term Test (Y2) but it had a direct impact either through National (Z or through Mid-Term Test (Y1) and Final-Term Test (Y2) which contributed 19.6% of the total site. The direct contribution of Mid-Term Test (Y1) to National Examination (Z) was the highest direct contribution in this study with 40% of the total site. While, the contribution of Practical Training (X1), Assignment (X2), Daily Test (X3), Mid-Term Test (Y1), and Final-Term Test (Y2) simultaneously influenced National Examination (Z) with 93.5% . Abaut 6.5% was influenced by the other factors which could not be described in this study. Key Words : National Examination, Path Analysis, Variable Exogenous, endogenous variables
Cox Proportional Hazard Model for Analysis of Farmers Insurance Premium Payment Period Rosida, Ayu; Fatekurohman, Mohamat; Dewi, Yuliani Setia; Arif, M. Ziaul
BERKALA SAINSTEK Vol 12 No 3 (2024)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/bst.v12i3.47118

Abstract

The sub-sector of agriculture plays a significant role in the national economic order. The crop failure rate is one of the unexpected risks caused by natural disasters, including drought, pest attacks, and floods. Agricultural insurance has been used as a pilot project in several areas, such as Gresik and Palembang Regencies. This pilot project has not been carried out in many places and cannot be implemented optimally in Jember. Farmer insurance is a transfer of risk due to farming business losses so that the sustainability of the farming business can be guaranteed. Survival analysis is a statistical method for analyzing data with observed response variables in terms of the time until an event occurs. One survival analysis is to determine the factors that cause an event with a response variable, namely using the Cox Proportional Hazard Model. The results of the significance testing obtained the variable that had a significant influence on the model, namely the growing season variable (X4). Then, a hazard ratio comparison was made for the category of cultivation season variables, and the category with the lowest hazard value was selected, followed by the second category, the months of May until August. (X42), This significantly influenced the policyholder’s time spent paying farmer’s insurance premiums.
ANALISIS REGRESI KELAS LATEN UNTUK DATA KATEGORIK DENGAN SATU KOVARIAT Haeruddin, Haeruddin; Tirta, I Made; Dewi, Yuliani Setia
BERKALA SAINSTEK Vol 1 No 1 (2013)
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Analisis regresi kelas laten merupakan analisis multivariat untuk data kategorik. Estimasi parameter pada analisis regresi kelas laten menggunakan algoritma EM (ekspektasi-maksimisasi) yang dilanjutkan dengan metode Newton-Raphson. Dalam penelitian ini, analisis regresi kelas laten digunakan untuk mengklasifikasikan responden berdasarkan persepsinya terhadap peluang (opportunity) dan ancaman (treath) bagi distributor produk Unilever, PT. Panahmas Dwitama Distrindo Regional Jember. Lamanya responden berlangganan terhadap distributor ini dijadikan sebagai kovariat. Hasil analisis menunjukkan bahwa berdasarkan persepsinya terhadap opportunity, responden dikelompokkan menjadi tiga kelompok, sedangkan terhadap treath dikelompokkan menjadi dua kelompok.
Perbandingan Algoritma K-Medoids Dan K-Means Dalam Pengelompokan Kecamatan Berdasarkan Produksi Padi Dan Palawija Di Jember Khan, Akhmad Safrin Sadad; Fatekurohman, Mohamat; Dewi, Yuliani Setia
Jurnal Statistika dan Komputasi Vol. 2 No. 2 (2023): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v2i2.2301

Abstract

Latar   Belakang: Pengelolaan tanaman pangan sangat penting untuk mendukung ketahanan pangan. Dataset menunjukkan variasi hasil panen padi dan tanaman pokok lainnya. Variasi hasil panen tersebut memerlukan pengelompokan wilayah berdasarkan hasil panen. Algoritma yang umum digunakan dalam analisis clustering adalah K-means dan K-medoids. Terdapat pada kedua algoritma tersebut yiatu K-means kompleksitas waktu lebih cepat dan K-medoids lebih tahan dengan data outlier. Sehingga perbandingan kedua algoritma dapat membantu pemilihan algoritma yang lebih baik dalam kasus tertentu Tujuan: memperoleh hasil perbandingan cluster terbaik dengan menggunakan algoritma  K-means dan K-medoids di Kabupaten Jember berdasarkan produksi padi dan palawija dan mengetahui hasil clustering dengan algoritma pengelompokan terbaik Kecamatan Jember berdasarkan produksi padi dan palawija. Metode: Algoritma clustering yang digunakan yaitu K-means dan K-medoids. Metode evaluasi menggunakan Davies Bouldien Index. Sumber data berasal dari data sekunder dari BPS Kabupaten Jember tahun 2020. Hasil: Diperoleh algoritma terbaik yaitu K-means dengan DBI 0,648 lebih kecil dibandingan K-medoids 0,886 dibagi menjadi 6 klaster yaitu klaster satu sebanyak 1 kecamatan, klaster dua sebanyak 3 kecamatan, klaster tiga sebanyak 2 kecamatan, klaster klaster empat sebanyak 3 kecamatan, klaster lima sebanyak 8 kecamatan dan klaster 6 sebanyak 14 kecamatan. Kesimpulan: K-means dengan 6 cluster menjadi algoritma terbaik untuk pengelompokan produksi tanaman pangan di Kabupaten Jember.
NONPARAMETRIC REGRESSION MODELING USING THE SPLINE APPROACH TO STUNTING CASES IN INDONESIA Fatekurohman, Mohamat; Nur Khasanah, Siti; Setia Dewi, Yuliani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp697-708

Abstract

Indonesia is the fourth ranked country in the world and second in Southeast Asia with the highest stunting cases of 21.6%. According to the provisions of the World Health Organization (WHO), the maximum tolerance standard for stunted toddlers is 20 percent or one-fifth of the total number of toddlers, so the stunting rate in Indonesia is still relatively high. The high stunting rate in Indonesia can affect the quality of Indonesia's human resources, so early detection and immediate management of stunted toddlers are needed. Stunting is a condition of failure to grow due to chronic malnutrition which is caused by inadequate nutritional intake for a long time, resulting in being shorter than standard. This research aims to determine several factors that influence stunting in toddlers in Indonesia using the nonparametric spline regression method with one knot, two knots, three knots and the best model is found to be the one knot model. The results of regression nonparametric spline modeling with one knot are GCV of 14.32605 and of 81.1%. From the five variables, namely toddlers receiving complete basic immunization babies receiving exclusive breast milk for 6 months , babies born receiving IMD children aged 6-23 months consuming five of the eight food groups and drink throughout the day , households having access to proper sanitation , the following results were obtained: the variable that don’t have a significant effect was toddlers receiving complete basic immunization , while the other four has a significant effect.
Co-Authors A As’ad Agus Isdarmawan Agustina Pradjaningsih Alfian Futuhul Hadi Andayani, Nonik Arif, M. Ziaul As’ad, A B., Rika N. Dewi, Theresia Trias Candra Fendi Setiawan Haeruddin Haeruddin Haeruddin Haeruddin I Made Tirta Kasmuri Kasmuri Kasmuri Kasmuri, Kasmuri Khan, Akhmad Safrin Sadad Lutfi Ardining Tyas Marino Marino, Marino Mohamad Fatekurohman Nawal Ika Susanti, Nawal Ika Nurul Fittriyah, Nurul Rizka Dwi Hidayati Rosida, Ayu Solly Aryza Yeni Rismawati