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All Journal Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI ESTIMASI: Journal of Statistics and Its Application
Sitti Sahriman
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics

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Aplikasi Generalized Poisson Regression dalam Mengatasi Overdispersi pada Data Jumlah Penderita Demam Berdarah Dengue Arwini Arisandi; Erna Tri Herdiani; Sitti Sahriman
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 18, No 2 (2018)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v18i2.4542

Abstract

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.
Pemodelan Statistical Downscaling dengan Regresi Modifikasi Jackknife Ridge Dummy Berbasis K-means untuk Pendugaan Curah Hujan Dewi Santika Upa P.; Sitti Sahriman
ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 1, Januari, 2021 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v2i1.11189

Abstract

Indonesia is a tropical country, which only has two seasons throughout the year, namely the dry season and the rainy season. Thus, it is likely that rain will continue to fall during the dry season, which has a serious impact on various sectors of life. General Circulation Model (GCM) is used to deal with climate change, but the GCM cannot conduct simulations well for local scale climate variables. Therefore, Statistical Downscaling (SD) is used to predict local scale rainfall in the district of Pangkep based on square GCM (CMIP5) 8 × 8 grid data. Modified jackknife ridge regression is used to overcome multicollinearity problems that occur in GCM-lag data. Three dummy variables were added as predictor variables for the model to overcome the heterogeneity of the various forms. SD model MJR dummy regression gives good results based on the coefficient of determination and high correlation with lower root mean square error and root mean square error prediction.
Penerapan Metode Linearized Ridge Regression pada Data yang Mengandung Multikolinearitas Mukrimin Adam; Sitti Sahriman; Nasrah Sirajang
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.19081

Abstract

One of the assumptions that must be met in the multiple linear regression model is that there is no multicollinearity problem among the independent variables. However, if there is a multicollinearity problem, then parameter estimation can be done using the linearized ridge regression (LRR) method. The LRR method has the advantage of choosing an optimal constant that is easy to determine and also has a minimum PRESS value. In this study, the infant mortality rate in South Sulawesi Province will be modeled using the LRR method based on the variables of the amount of vitamin A given, the number of health services, the number of babies born with low weight, the number of mothers who give birth assisted by medical personnel, and the number of babies who are breastfed. exclusive. One measure to see the goodness of the regression model is the Prediction Error Sum of Squares (PRESS). Based on the t-test at a significance level of 5%, the total coverage of vitamin A administration and the number of babies born with low weight gave a significant effect on infant mortality with a PRESS value of 0.6846.
Performa Model Statistical Downscaling dengan Peubah Dummy Berdasarkan K-Means dan Average Linkage Fitri Annisa; Raupong Raupong; Sitti Sahriman
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v4i2.12658

Abstract

Climate change that occurs is often used to predict future climate conditions. For future climate predictions it is only possible to use climate models. One of the climate models used to predict climate conditions is the Global Circulation Models (GCM). GCM represents global climatic conditions but not on a regional or local scale. The approach that has been widely used to bridge the difference in scale is statistical downscaling. Large-scale GCM data allows for multicollinearity. estimation liu regression and principal component regression is used to solve the multicollinearity problem. In addition, dummy variables based on k-means and average linkage are used in the model to overcome the heterogeneous variance of residue. There are 4 dummy variables in the cluster technique. In this paper, Liu k-means regression model parameter estimation method is the best model.
Forecasting Dry Rubber Production in Indonesia for the Year 2022 Using Pegel's Exponential Smoothing Method with Modified Golden Section Optimization Rahmi Nurul Ainun Fitrah; Sitti Sahriman
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.37158

Abstract

Pegel’s Exponential Smoothing is a forecasting method that considers separating trend and seasonal aspects, with additive and multiplicative models. Pegel’s Exponential Smoothing has three parameters, α, β, and γ. Many possible parameter combinations may yield an optimal solution, so a modified Golden Section method is used. The principle of this method is to iteratively reduce the boundary area of x that may produce an optimal objective function value, systematically decreasing the number of search steps to minimize the number of trials. Data obtained from the Central Bureau of Statistics regarding the amount of dry rubber production in Indonesian plantations from January 2017 to December 2022 is assumed to contain a multiplicative seasonal effect due to the relatively unstable seasonal pattern heights. This study compares three trend models: no trend, additive trend, and multiplicative trend in the multiplicative seasonal Pegel’s Exponential Smoothing method. This study aims to predict the amount of dry rubber production in Indonesian plantations from January 2022 to December 2022. Forecast validation results show that the multiplicative trend in the multiplicative seasonal Pegel’s Exponential Smoothing method, with a MAPE of 3.389001% and an RMSE of 8,839.965080, has the best forecasting accuracy for this data compared to the other three trend models.
Perbandingan Metode Seasonal ARIMA dan Extreme Learning Machine dalam Prediksi Produksi Padi di Sulawesi Selatan Jamal, Rini; Baso, Andi M Alfin; Andi Febriyanti; Sitti Sahriman; Siswanto, Siswanto; Yunita, Andi Isna; Angriany, A. Muthiah Nur; Rahim, Rahmiati; Fadil, Muhammad
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.45821

Abstract

South Sulawesi is one of the provinces that significantly contributes to national rice production. Therefore, accurate forecasting of rice production is crucial for food security planning and agricultural policy-making. This study aims to compare the performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Learning Machine (ELM) methods in predicting rice production in South Sulawesi. SARIMA is a statistical forecasting method effective for data with seasonal patterns, while ELM is a machine learning approach capable of handling complex relationships among variables with high computational speed. Rice production data from the Central Statistics Agency (Badan Pusat Statistik) were used to evaluate the accuracy of both methods. The evaluation was conducted using forecasting error metrics such as Mean Absolute Percentage Error (MAPE). The results show that the SARIMA(1,1,0)(1,1,0)12 model outperformed ELM in predicting rice production in South Sulawesi. This is indicated by a lower MAPE value of 19.937%, compared to 21.632% for the ELM method.
Co-Authors Andi Febriyanti Angriany, A. Muthiah Nur Arwini Arisandi Baso, Andi M Alfin Dewi Santika Upa P. Fitri Annisa Herdiani, Erna Tri Jamal, Rini Muhammad Fadil Mukrimin Adam Nasrah Sirajang Rahim, Rahmiati Rahmi Nurul Ainun Fitrah Raupong Raupong S Siswanto Yunita, Andi Isna