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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Statistics and Its Applications
Sahriman, Sitti
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Public Health Transportation Other

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Pembelajaran Matematika Melalui Metode Bermain Erawaty, Nur; Thoha, Syamsuddin; B., Hasmawati; Kasbawati, Kasbawati; Aris, Naimah; Sirajang, Nasrah; Sahriman, Sitti; Anwar, Andi M.; Aidawayati, Aidawayati; Jusmawati, Jusmawati; Saputra, Edy
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol 3 No 2 (2019): JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : Dewan Pimpinan Daerah (DPD) Forum Dosen Indonesia JATIM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.788 KB) | DOI: 10.36339/je.v3i2.201

Abstract

The achievement of Mathematics contestants from the City of Makassar is very concerning. In March 2018 elementary school mathematics competition was held. Of the 186 participants from Makassar, there were only 56 students who got scores above zero. Only about 30%. Other students get zero or less than zero (negative). There was a decrease in interest and achievement in Mathematics in elementary school students in Makassar. The solution offered was training for Mathematics Elementary School teachers by emphasizing learning method with playing. This is intended so that children have enjoyed Mathematics from the beginning so that in the future the interest in learning Mathematics will be even greater.
Peningkatan Kualitas Pembelajaran Matematika Bagi Guru SMA Melalui Media Google Classroom dan Geogebra Aris, Naimah; Erawaty, Nur; Massalesse, Jusmawati; Sirajang, Nasrah; Wahda, Wahda; Kasbawati, Kasbawati; Thamrin, Sri Astuti; Sahriman, Sitti; Ramadhan, Muh. Nur Bahri; Jaya, A. Kresna
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol 3 No 2 (2019): JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : Dewan Pimpinan Daerah (DPD) Forum Dosen Indonesia JATIM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.483 KB) | DOI: 10.36339/je.v3i2.253

Abstract

The involvement of teachers in Bone Regency in using information and communication technology (ICT) to prepare teaching materials is very little or even never said, even though computer facilities and infrastructure are available in the computing lab. This activity aims to provide knowledge to Mathematics teachers about online learning Google Classroom and Geogebra. The use of Google Classroom will make learning more effective for teachers and students because learning is no longer limited by space and time, student can explore learning resources easily and utilize information technology properly. Likewise, Geogebra training is expected to overcome the difficulties of teachers in visualizing concept charts in mathematics dynamically. The target audience for community service is mathematics teachers who are members of the Mathematics MGMP in Bone Regency.
Metode Pembelajaran Matematika dengan Permainan di Kotamadya Pare-pare Erawaty, Nur; Amir, Amir Kamal; Aris, Naimah; Kasbawati, Kasbawati; Sahriman, Sitti; Rangkuti, Aidawayati
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol 2 No 2 (2018): JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : Dewan Pimpinan Daerah (DPD) Forum Dosen Indonesia JATIM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (187.302 KB) | DOI: 10.36339/je.v2i2.162

Abstract

The achievement of Mathematics contestants from the City of Pare-pare is very concerning. In February 2017elementary school mathematics competition was held. Of the 82 participants from Pare-pare, there were only 13 studentswho got scores above zero. Other students get zero or less than zero (negative). In March 2018, from 17 MathematicsEvent XVIII 2018 participants from Pare-pare, only 5 students had got positive scores. There was a decrease in interestand achievement in Mathematics in elementary school students in Pare-pare. The solution offered was training forMathematics Elementary School teachers by emphasizing learning method with playing. This is intended so that childrenhave enjoyed Mathematics from the beginning so that in the future the interest in learning Mathematics will be evengreater
Penerapan Model Regresi Hurdle Binomial Negatif Menggunakan Algoritma Broyden-Fletcher-Goldfarb-Shanno pada Data Jumlah Kematian Bayi di Kota Makassar Tahun 2017 Yusuf, Anisa Haura Salsa Fatih; Jaya, Andi Kresna; Sahriman, Sitti
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i1.22749

Abstract

Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.
Estimasi Parameter Regresi Ridge Robust pada Data Profil Kesehatan Sulawesi Selatan Waibusi, Hendriete Tiur Marowi; Tinungki, Georgina Maria; Sahriman, Sitti
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.25520

Abstract

ABSTRACT Multicollinearity is one of the assumption violations in regression analysis. The existence of multicollinearity causes the standard error to increase. Ridge regression is one of the regression analysis approaches that can overcome this problem. Besides multicollinearity, another problem that often occurs is outliers. The existence of outliers causes the data not to be normally distributed. Ridge Robust Least Trimmed Square Regression is a method that can be used to overcome multicollinearity and outlier problems in the data simultaneously in the regression analysis model. The purpose of this study was to obtain the estimation results of the least trimmed square ridge robust regression model on the Health Profile data of South Sulawesi in 2017. From the results and discussion it was found that the least trimmed square ridge robust regression method has an Rsquare value or ?2 which is 88% and an MSE value 1.96, thus indicating that the ridge robust least trimmed square model fits better in dealing with data containing multicollinearity and outliers. Keywords: Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality Rate.
Estimasi Model Regresi Spline Kubik Tersegmen dengan Metode Penalized Least Square Islamiyati, Anna; Anisa, Anisa; Raupong, Raupong; Massalesse, Jusmawati; Sirajang, Nasrah; Sahriman, Sitti; Wahyuni, Alfiana
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 10 No. 2 (2022): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam had Accr
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v10i2.3197

Abstract

Abstract:Nonparametric regression is used for data whose data pattern is non-parametric. One of the estimators that can be developed is a segmented cubic spline which is able to show several segmentation changes in the data. This article examines the estimation of segmented cubic spline nonparametric regression models using the Penalized Least Square estimation criteria. The method involves knot points and smoothing parameters simultaneously. In addition, the model is used to analyze data on BPJS claims based on patient age. The results show that the optimal model is at two-knot points, namely 26 and 52 with a smoothing parameter of 0.89. There are three segmentation changes from the cubic data, which consist of young people up to 26 years old, 26-52 years old, and 52 years and over. Abstrak:Regresi nonparametrik digunakan untuk data yang pola datanya bentuk non parametrik. Salah satu estimator yang dapat dikembangkan adalah spline kubik tersegmen yang mampu menunjukkan beberapa segmentasi perubahan pada data. Artikel ini mengkaji estimasi model regresi nonparametrik spline kubik tersegmen melalui kriteria estimasi menggunakan Penalized Least Square. Metode tersebut melibatkan titik knot dan parameter penghalus secara bersamaan. Selain itu, model digunakan untuk menganalisis data klaim BPJS berdasarkan usia pasien. Hasil menunjukkan bahwa model optimal pada dua titik knot yaitu 26 dan 52 dengan parameter penghalus sebesar 0,89. Terdapat tiga segmentasi perubahan data secara kubik, yaitu usia muda hingga 26 tahun, usia 26-52 tahun, dan usia 52 tahun ke atas. 
Penerapan Metode Exhaustive Chi-Square Automatic Interaction Detection pada Klasifikasi Penderita Diabetes dan Non Diabetes Nurhidayatullah, Nurhidayatullah; Sahriman, Sitti; Nirwan, Nirwan
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i1.33079

Abstract

Classification is a process of grouping an object into a certain category. One of classification method is the Exhaustive Chi-Square Automatic Interaction Detection (CHAID). The Exhaustive CHAID method is a classification method for categorical data by forming a classification tree. The classification tree interprets predictor variables that have a significant effect on the response variable based on the chi-square test. The purpose of this study was to obtain classification results for diabetics and non-diabetics using the Exhaustive CHAID method. The response variable used is the blood sugar level and the predictor variables consist of systolic blood pressure, diastolic blood pressure, length of sleep, working style, level of knowledge about diabetes, abdominal circumference, hereditary history of diabetes, age, exercise habits, and body mass index. The classification results show that the factors that have a significant influence at the 5% level are a hereditary history of diabetes, abdominal circumference, level of knowledge about diabetes, and diastolic blood pressure. Apart from that, the accuracy value of the Exhaustive CHAID classification tree is quite good, namely 86% based on the confusion matrix.
FORECASTING MONTHLY RAINFALL IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH ROBUST PRINCIPAL COMPONENT REGRESSION TECHNIQUE Sahriman, Sitti; Anisa, Anisa
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/barekengvol19iss2pp777-790

Abstract

A General Circulation Model (GCM) is a global climate model commonly used to predict local-scale climate patterns. However, the spatial resolution of GCMs is typically on a global scale, which is inadequate for predicting local climate. Statistical downscaling (SD) is used to transform climate information from a global scale to a smaller scale for local-scale climate predictions. GCM data have large dimensions and high correlations between grids, so principal component regression (PCR) is used in SD. The minimum covariance determinant (MCD) and minimum vector variance (MVV) methods are used in principal component analysis to obtain robust principal components (PCs). The data used in this study were the monthly rainfall data in Pangkep Regency for the period from January 1999 to December 2022 as the response variable, which were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) Region IV Makassar. The predictor variable data were GCM precipitation data (64 variables) for the same period and three dummy variables. This study aimed to obtain rainfall forecasts in Pangkep Regency for the year 2023 based on a robust PCR model using results from MCD and MVV. The modeling results indicated that both the MCD and MVV methods provided similar model accuracy, with a coefficient of determination of approximately 91%. The PCR model with two PCs from the MVV method and dummy variables was identified as the best model for explaining the variability in rainfall data in Pangkep Regency. Additionally, the 2023 rainfall forecast results showed that both methods yielded relatively similar accuracy. The addition of dummy variables in the PCR model improved both the model accuracy and rainfall forecasts. The PCR model with three PCs from MVV and dummy principal component variables produced accurate rainfall forecasts based on a high correlation value (0.974) and the smallest mean absolute percentage error (7.290).
Perbandingan Model Threshold Generalized utoregressive Conditional Heteroscedasticity dan Exponential Generalized Autoregressive Conditional eteroscedasticity pada Peramalan Curah Hujan Andrianingrum, Amalia; Sahriman, Sitti; Jaya, Andi Kresna
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.v6i2.43100

Abstract

Rainfall plays an important role in life and is closely related to other weather elements. Rainfall data is used for various purposes, including flood and drought risk mitigation and water resource planning. Makassar City has significant rainfall variability and requires accurate forecasting to manage its negative impacts. This study aims to predict rainfall in Makassar City from January 2021 to May 2023. The methods used are Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The results showed that the ARMA (2,1)-GARCH (1,2) model had MAPE and RMSEP values ​​of 1.234 and 33.411, respectively. The ARMA (2,1)-TGARCH (2,1) model had MAPE and RMSEP values ​​of 1.330 and 29.357, respectively. The ARMA (2,1)-EGARCH (1,2) model has MAPE and RMSEP values ​​of 0.924 and 32.641, respectively. The smallest MAPE and RMSEP values ​​are in the ARMA (2,1)-EGARCH (1,2) model. Thus, the ARMA (2,1)-EGARCH (1,2) model was selected as the best or optimal model for rainfall forecasting in Makassar City.
STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY Sahriman, Sitti; Yulianti, Andi Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.06 KB) | DOI: 10.30598/barekengvol17iss1pp0401-0410

Abstract

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).
Co-Authors Aidawayati, Aidawayati Amir Kamal Amir Andi Kresna Jaya Andrianingrum, Amalia Anisa Anisa Anna Islamiyati Anwar, Andi M. B., Hasmawati Edy Saputra Rusdi Georgina Maria Tinungki Hisyam, M. Zaky Gozhi Jaya, A. Kresna Jusmawati Jusmawati, Jusmawati Jusmawati Massalesse Kasbawati Kasbawati Naimah Aris Nirwan Nirwan Nur Erawaty Nurhidayatullah Nurhidayatullah, Nurhidayatullah Putra, Guntur Dwi Ramadhan, Muh. Nur Bahri Randa, Eunike Laurine Rangkuti, Aidawayati Raupong, Raupong Sirajang, Nasrah Sri Astuti Thamrin Surianda, Sitti Aisyah Taufik, Muh. Ikbal Thoha, Syamsuddin Upa, Dewi Wahda, Wahda Wahyuni, Alfiana Waibusi, Hendriete Tiur Marowi Yulianti, Andi Sri Yusuf, Anisa Haura Salsa Fatih