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<![CDATA[Aplikasi Generalized Poisson Regression dalam Mengatasi Overdispersi pada Data Jumlah Penderita Demam Berdarah Dengue ]]>
<![CDATA[Arwini Arisandi]]>;
<![CDATA[Erna Tri Herdiani]]>;
<![CDATA[Sitti Sahriman]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 18, No 2 (2018) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v18i2.4542
<![CDATA[Asumsi dasar dalam regresi Poisson yaitu nilai variansi data sama dengan nilai mean data. Namun,asumsi tersebut umumnya tidak terpenuhi, misalnya terdapat kasus overdispersi. Overdispersidalam regresi Poisson terjadi apabila nilai variansinya lebih besar daripada nilai meannya. Jikaterjadi overdispersi pada data, maka model regresi Poisson kurang akurat digunakan karenaberdampak pada nilai standard error dari taksiran parameter yang dihasilkan cenderung menjadiunderestimate sehingga kesimpulan yang diperoleh menjadi kurang valid. Dalam penelitian ini,kasus overdispersi dapat diatasi dengan model generalized Poisson regression. Hasil penelitianmenunjukkan bahwa nilai AIC minimum diberikan oleh model generalized Poisson regression.Sehingga dalam penelitian ini disimpulkan bahwa pada penelitian terhadap data yang mengalamioverdispersi pada Jumlah Penderita DBD di Kota Makassar tahun 2016, pemodelan regresigeneralized Poisson mampu mengatasi terjadinya overdispersi yang terjadi pada pemodelan regresiPoisson. Nilai R2 yang dimiliki sebesar 67% yang artinya jumlah penderita DBD ditentukan olehpersentase tempat-tempat umum memenuhi syarat kesehatan, persentase penduduk yang memilikiakses air minum layak, persentase rumah tangga berprilaku hidup bersih dan sehat dan persentaserumah yang memenuhi syarat kesehatan. Selebihnya 33% ditentukan oleh faktor lain.]]>
<![CDATA[Peramalan Jumlah Penumpang Kapal Laut Menggunakan Metode Fuzzy Runtun Waktu Chen Orde Tinggi ]]>
<![CDATA[Rizki Adiputra]]>;
<![CDATA[Erna Tri Herdiani]]>;
<![CDATA[Sitti Sahriman]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 1, Januari, 2021 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v2i1.10328
<![CDATA[The port has become an important part of people's lives. On certain days there is an increase in the number of ship passengers which can slow down operational activities from the port, thus causing a buildup of passengers at the port. therefore, the port must be prepared to deal with the buildup of passengers at the port. Based on this, the researchers made a prediction or forecasting the number of ship passengers at Makassar Soekarno Hatta Port in the coming period to find out how much the estimated number of passengers at Makassar Soekarno Hatta Port. The results of these studies can be input to the PT. Pelabuhan Indonesia IV (Persero ) Makassar if there will be a surge in passengers in the future period. researchers used the fuzzy method of high order chen time series in forecasting or prediction in this study . The researcher divides the data onto training and testing data . The results of the study using fuzzy time series with the best high order chen are that the second order produces MAPE error size of 0,143 , MSE 13470993,9 and MAE of 9478,52 . The result of prediction of testing data onto one period in the future is 52.608.]]>
<![CDATA[Modeling of COVID-19 Cases in Indonesia with the Method of Geographically Weighted Regression ]]>
<![CDATA[Samsul Arifin]]>;
<![CDATA[Erna Tri Herdiani]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 2 (2023): JANUARY 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v19i2.23481
<![CDATA[The COVID-19 pandemic has spread to all corners of the world, including Indonesia. Various factors affect the spread of COVID-19 cases in an area so that the government and the community can make prevention and control efforts so that this pandemic does not spread. This study aims to model the number of COVID-19 cases in Indonesia using the Geographically Weighted Regression (GWR) method, which develops a linear regression model. The GWR model uses weights based on the location of each observation so that the model is obtained for that location. Determine the weighting on the bandwidth. Optimum bandwidth selection is obtained by minimizing the value of Cross-Validation (CV). The GWR model using a fixed bisquare kernel weighting function has an optimum bandwidth of 0.999948 with a minimum CV value of 397.076.128 with a coefficient of determination R2 of 85.1 %. The results show that the number of positive cases positively correlates with the number of patients who died from COVID-19. In contrast, the number of recovered patients negatively correlates with the number of patients who died from COVID-19.]]>
<![CDATA[Pemodelan Regresi Binomial Negatif Bivariat pada Data Jumlah Kematian Ibu dan Bayi di Provinsi Sulawesi Selatan Tahun 2020 ]]>
<![CDATA[Nurhidaya L]]>;
<![CDATA[Erna Tri Herdiani]]>;
<![CDATA[Georgina Maria Tinungki]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.25082
<![CDATA[In general, negative binomial regression is used for univariate discrete data that is overdispersive and follows the Poisson distribution. In the real world, a case is often influenced by two discrete variables that are correlated with each other. Therefore, in this paper we will examine the regression that is influenced by two independent variables, has overdispersion properties and follows a bivariate Poisson distribution. This regression is called bivariate negative binomial regression with model parameters estimated using the Maximum Likelihood Estimation (MLE) method and Newton Raphson iterations. The formation of this model is based on the Famoye method, while in general it uses the Cheon method. Furthermore, the results of this study were applied to data on the number of maternal and infant deaths in South Sulawesi Province in 2020. The results obtained were the number of puskesmas that had a significant effect on the number of maternal deaths and the proportion of handling obstetric complications, the proportion of pregnant women implementing the K4 program, the proportion of deliveries in facilities health services, the proportion of postpartum mothers implementing the KF2 program and the number of puskesmas have a significant effect on the number of infant deaths.]]>
<![CDATA[Peta Kendali Atribut Menggunakan Zero-Inflated Generalized Poisson ]]>
<![CDATA[Ratmila Mammi]]>;
<![CDATA[Erna Tri Herdiani]]>;
<![CDATA[Nasrah Sirajang]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v4i2.12932
<![CDATA[If the variable is a discrete random variable with Poisson distribution, the data analysis must fulfill the equidispersion assumption. In reality, these assumptions are not fulfilled because the variance is greater than the mean which is called overdispersion. Overdispersion in data can occur due to the proportion of excess zero values in these variables. To estimate the parameters, the MLE method can be used on data that has a certain distribution by maximizing the likelihood function, it obtained is implicit or nonlinear so that it cant be solved analytically. To get the numerical solution, it solved by using the EM algorithm. The estimation results of the ZIGP distribution parameters are used to create control chart limits for the 2016 Neonatal Mortality Rate data in Makassar with limits of , , and . The chart ARL value is , which is greater than the chart ARL value, which is which indicates that the chart is better at detecting outliers.]]>
<![CDATA[Peta Kendali p Berdasarkan Metode Peningkatan Transformasi Akar Kuadrat ]]>
<![CDATA[Rasyid, Riska]]>;
<![CDATA[Herdiani, Erna Tri]]>;
<![CDATA[Sunusi, Nurtiti]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.18487
<![CDATA[When the proportion of nonconformities is small, the effectiveness of the control chart performance becomes inadequate because it has a skewness that causes asymmetryc. Therefore, the Improved Square Root Transformation (ISRT) method is used to construct the attribute control chart to increase the accuracy of the chart control limit which is called the ISRT- control chart. In this study, the effectiveness of the ISRT- control chart perfomance is compared with the control chart after being applied to the data on the number of defects in the newspaper production process at PT. Radar Sulteng Membangun. The results showed that the production process at PT. Radar Sulteng Membangun was not in a statistically controlled and the ARL value obtained on the ISRT- control chart is much smaller than the ARL value for the control chart, so that the ISRT- chart is more effective and sensitive to detecting changes in the production process which produces in a small proportion of nonconformities.]]>
<![CDATA[Analisis Regresi Data Panel Dengan Model Efek Umum, Model Efek Tetap Dan Model Efek Acak (Studi Kasus: Inflasi Dan Indeks Pembangunan Manusia) ]]>
<![CDATA[ada, Nuralyatussa’]]>;
<![CDATA[Herdiani, Erna Tri]]>;
<![CDATA[Sirajang, Nasrah]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i2.33279
<![CDATA[Panel data regression analysis is a method for modeling the influence of independent variables on dependent variables, on a combination of cross-section and time-series data. This research aims to estimate a panel data regression model with a generalized effects model using the least squares method, estimate a fixed effects model with the Least Square Dummy Variable and estimate a random effects model with Generalized Least Square on inflation and human development index data. The results obtained show that the factors that have a significant influence at the 5% level on the inflation rate in 2014-2019 are the dollar exchange rate with a coefficient of determination of the general effects model of 61.06%, then the HDI level in South Sulawesi in 2011-2017 is significantly influenced by factors such as average length of schooling and life expectancy with a coefficient of determination of the fixed effects model of 89.73%, and the HDI level in South Sulawesi in 2016-2019 is significantly influenced by the factors of life expectancy, per capita expenditure and poverty with a coefficient of determination of the random effects model amounting to 63.07%.]]>
<![CDATA[Taksiran Parameter Multinomial Logit Dengan Menggunakan Generalized Method Of Moment ]]>
<![CDATA[Fathanah, Nur]]>;
<![CDATA[Herdiani, Erna Tri]]>;
<![CDATA[Tinungki, Georgina Maria]]>
<![CDATA[ Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 8 No 1 (2020): Volume 8 Nomor 1 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Alauddin Makassar ]]>
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DOI: 10.24252/msa.v8i1.12247
<![CDATA[Regresi logistik multinomial merupakan perluasan dari regresi logistik biner yang memungkinkan lebih dari dua kategori variabel dependen. Pada paper ini akan membahas penaksiran parameter regresi logistik multinomial melalui Generalized Method of Moment (GMM). Generalized Method of Moment (GMM) merupakan salah satu metode yang dapat mengatasi pelanggaran asumsi pada data seperti autokorelasi dan heteroskedastisitas]]>
<![CDATA[Peta Kendali Demerit Untuk Data Autokorelasi (Moving Centerline Demerit dan Moving Range) ]]>
<![CDATA[Nasruddin, Nurmasyita]]>;
<![CDATA[Erna Tri Herdiani]]>;
<![CDATA[Nasrah Sirajang]]>
<![CDATA[ Statistika Vol. 24 No. 2 (2024): Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung ]]>
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DOI: 10.29313/statistika.v24i2.3145
<![CDATA[ABSTRAK Proses industri seringkali menghasilkan data cacat yang bersifat autokorelasi, hal ini meyebabkan asumsi dasar penggunaan peta kendali tidak terpenuhi. Peta kendali demerit direkomendasikan untuk perusahaan yang terdapat berbagai macam tingkat kesalahan. Peta kendali demerit adalah metode pengendalian kualitas yang mengkategorikan jenis cacat ke dalam beberapa kelas berdasarkan tingkat keseriusannya. Peta kendali demerit sangat berguna dalam situasi di mana terdapat berbagai macam tingkat kesalahan, memungkinkan perusahaan untuk mengidentifikasi dan mengatasi cacat berdasarkan tingkat dampaknya terhadap kualitas produk. Penelitian ini bertujuan untuk memperoleh peta kendali Demerit pada data berautokorelasi dan menerapkan peta kendali Residual Demerit dan peta kendali Moving Centerline Demerit sebagai solusi dalam peta kendali Demerit autokorelasi terhadap pengendalian kecacatan produk pada data wadah plastik anti bocor. Metode yang digunakan adalah peta kendali demerit, peta kendali Residual, dan peta kendali Moving Centerline Demerit (MCD). Data yang digunakan merupakan data sekunder. Hasil penenelitian ini memperlihatkan bahwa peta kendali Residual dan peta kendali Moving Centerline Demerit sama unggulnya dalam mengatasi data autokorelasi pada peta kendali Demerit dimana sama-sama terdapat 4 out of control atau 4 titik yang mengindikasikan adanya masalah proses produksi yang tidak dapat diatasi oleh perusahaan. ABSTRACT Industrial processes often produce defect data that is autocorrelated, causing the basic assumptions of using control maps to not be met. If there are various levels of errors in the company, then the company is advised to use the Demerit control map. Demerit control map is a quality control method that categorizes defect types into several classes based on their seriousness. Demerit control maps are particularly useful in situations where there is a wide range of error rates, allowing companies to identify and address defects based on their level of impact on product quality. This study aims to derive Demerit control maps on autocorrelated data and apply the Residual Demerit control map and the Moving Centerline Demerit control map as solutions in the autocorrelated Demerit control map to product defect control on leak-proof plastic container data. The methods used are Demerit control map, Residual control map, and Moving Centerline Demerit (MCD) control map. The data used is secondary data. The results of this study indicate that the Residual control map and the Moving Centerline Demerit control map are equally superior in overcoming autocorrelated data on the Demerit control map where there are both 4 out of control or 4 points that indicate a production process problem that cannot be overcome by the company.]]>
<![CDATA[Perbandingan Kinerja Peta Kendali Exponentially Weighted Moving Average dan Peta Kendali Double Exponentially Weighted Moving Average dalam Pengendalian Kualitas Produksi Butsudan di PT. Maruki International Indonesia ]]>
<![CDATA[Sonya, Sonya]]>;
<![CDATA[Herdiani, Erna Tri]]>;
<![CDATA[Tinungki, Georgina Maria]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v6i1.25751
<![CDATA[Quality control is an effort in the production process to maintain product quality and minimize the occurrence of defects. One of the quality control tools is a control chart. An exponentially weighted moving average (EWMA) control chart is used to detect small shifts in the process mean. The result of the development of the EWMA control chart is the double exponentially weighted moving average (DEWMA) control chart, which increases the exponential smoothing process, where the control chart is considered more sensitive in detecting small shifts in the process mean. This study aims to obtain a comparison of the performance of the EWMA and DEWMA control charts in controlling the quality of butsudan production at PT. Maruki International Indonesia. The results obtained show that the DEWMA control chart has better performance in detecting small shifts compared to the EWMA control chart based on the smallest ARL value, at λ=0.1 the DEWMA control chart has an ARL value 1.1363 which is smaller than the ARL of EWMA control chart is 1.2268.]]>
