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All Journal Jambura Journal of Mathematics 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
Sitti Sahriman
Statistics Department, Faculty Of Mathematics And Natural Sciences, Hasanuddin University
Humanities Computer Science & IT Education Engineering Mathematics Public Health Other

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Studi Longitudinal Pada Analisis Data Gula Darah Pasien Diabetes melalui Principal Component Analysis Anna Islamiyati; Sitti Sahriman; Sakinah Oktoni
Jambura Journal of Mathematics Vol 4, No 1: January 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.063 KB) | DOI: 10.34312/jjom.v4i1.11407

Abstract

Multicollinearity is a relationship or correlation between predictor variables. Multicollinearity can also occur in longitudinal data, which is a combination of cross-section data and time-series data. The impact of multicollinearity causes the influence of the predictor variable on the response variable to be insignificant, the least-squares estimator, and the error to be sensitive to changes in the data. Therefore, the procedure to overcome multicollinearity uses the principal component analysis method. This study aims to model PCA longitudinal data regression with a fixed-effect model that is applied to blood sugar data of diabetic patients with a time span of January 2019 to July 2019 at Ibnu Sina Hospital Makassar City. The results of this study indicate that there are two main components formed from PCA longitudinal data regression modelling with a fixed-effect model. Obtained variable values are systolic blood pressure of -0.007, diastolic blood pressure of -0,016, the body temperature of -0.098, and platelets of 0.005 which affect blood sugar in patients with diabetes.
Peramalan Jumlah Penumpang Kapal Laut Menggunakan Metode Fuzzy Runtun Waktu Chen Orde Tinggi Rizki Adiputra; Erna Tri Herdiani; 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.v2i1.10328

Abstract

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.
Model ARIMA dengan Variabel Eksogen dan GARCH pada Data Kurs Rupiah Ririn Arianti; Sitti Sahriman; La Podje Talangko
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.11603

Abstract

Autoregressive integrated moving average with exegenous variable (ARIMAX) model is the development of ARIMA model with addition of other time series data as exogenous variable that affect the dependent variable. ARIMAX model is used to analyze and predict data on the rupiah exchange rate against the US dollar with inflation as an exogenous variabel. The exchange rate has an residual variance that is not constant  so that the GARCH model is used to overcome the problem of heteroscedasticity. The results of this research show that forecasting the rupiah exchange rate against the US dollar fot the period January 2010 – December 2019 with the ARIMAX(0,1,1) – GARCH(1,0) model is the best model with a MAPE (1,1655) value which shows a low percentage compared to the ARIMAX model.
Penerapan Metode Stepwise dan Dominance Analysis Pada Regresi Logistik Biner (Studi Kasus: Data Hipertensi Di Indonesia) Muhammad Idman; La Podje Talangko; Sitti Sahriman
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.12211

Abstract

Binary logistic regression is a method to describe the relationship between response variable that has two categories and one or more predictor variables. One of methods that can be used to obtain best model in logistic regression is stepwise. Stepwise method is a method that sets  and  as criteria to build model. Dominance analysis is used in this research to determine the importance rank of predictor variables by comparing the coefficient of determination  value before and after the predictor variable entered the model. Binary logistic regression can be used to find the relationship between hypertension and the factor risks. This study aims to obtain best model and to obtain the importace rank each predictor variable of binary logistic regression on data of hypertension in Indonesia. The result of this study shows that best model which is obtained is model with predictor variable of Heart Problems, High Cholesterol, Kidney Disease, Imperfect Vision, Breathlessness, and Nausea/ Vomitting. According to the value of  McFadden, predictor variable of High Cholesterol infests first rank in the importance of predictor variable or gives the greatest contributions in explaining variety of Status of Hypertension than other predictor variables.Binary logistic regression is a method to describe the relationship between response variable that has two categories and one or more predictor variables. One of methods that can be used to obtain best model in logistic regression is stepwise. Stepwise method is a method that sets  and  as criteria to build model. Dominance analysis is used in this research to determine the importance rank of predictor variables by comparing the coefficient of determination  value before and after the predictor variable entered the model. Binary logistic regression can be used to find the relationship between hypertension and the factor risks. This study aims to obtain best model and to obtain the importace rank each predictor variable of binary logistic regression on data of hypertension in Indonesia. The result of this study shows that best model which is obtained is model with predictor variable of Heart Problems, High Cholesterol, Kidney Disease, Imperfect Vision, Breathlessness, and Nausea/ Vomitting. According to the value of  McFadden, predictor variable of High Cholesterol infests first rank in the importance of predictor variable or gives the greatest contributions in explaining variety of Status of Hypertension than other predictor variables.
Peningkatan Kreatifitas dan Efektifitas Pembelajaran Daring Matematika Bagi Guru SMP di Kecamatan Pattalassang melalui ISpring dan GeoGebra Naimah Aris; Nur Erawaty; Irma Andriani; Jusmawati Massalesse; Kasbawati Kasbawati; Sri Astuti Thamrin; Okta Nofri; Muhammad Zakir; Sitti Sahriman
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol 5 No 3 (2021): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : Dewan Pimpinan Daerah (DPD) Perkumpulan Dosen Indonesia Semesta (DIS) Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36339/je.v5i3.511

Abstract

Daring mathematics study leaves pupils with troubles and anxiety after more than a year of daring learning. Furthermore, students often struggle to understand instructional materials that need graphic interpretation when learning mathematics. This activity aims to improve the competence of mathematics teachers with skills using the iSpring and GeoGebra so that they can create creative and effective learning media so that their virtual mathematics learning becomes more interesting and understandable for students. This activity is conducted daring via Zoom and live on YouTube, as well as offline meetings that are carried out with due observance of health protocols. Lectures, demonstrations on the use of iSpring and GeoGebra, as well as monitoring and evaluation are methods used in daring and offline activities. The target audience for this community service is Middle School Mathematics teachers in Pattalassang District. The results of the training showed an increase in the understanding and skills of mathematics teachers in utilizing iSpring and GeoGebra multimedia. This improvement in IT skills can contribute to increasing creativity and effectiveness in learning mathematics in the classroom.
Strengthening Junior High School Members in Maros Regency in Supporting Adiwiyata Schools Naimah Aris; Jusmawati Massalesse; Nur Erawaty; Nurdin Nurdin; Kasbawati Kasbawati; Edy Saputra; Anisa Anisa; Anna Islamiyati; Sri Astuti Thamrin; Sitti Sahriman; Ainun Mawaddah Abdal; Najhah Aris; Muralia Hustim; Afifah Afifah
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol 7 No 1 (2023): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : Dewan Pimpinan Daerah (DPD) Perkumpulan Dosen Indonesia Semesta (DIS) Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36339/je.v7i1.711

Abstract

Maros Regency as an area that often receives Adipura award certificates should have schools that are also capable of achieving the Adiwiyata school title, a program that collaborates education with the environment. However, according to partners, out of 76 junior high schools in Maros Regency, only 5 have received this award. Starting from this, a team of lecturers from the Mathematics, Statistics, and Environmental Engineering study programs in collaboration with the Center for Development and Control of the Sulawesi and Maluku Ecoregions held training and mentoring activities for junior high schools in Maros Regency so that they were able to get the adiwiyata school title. Several aspects of the adiwiyata school assessment include curriculum development and environment-based learning, in this case specifically for mathematics. Organize the management of land, facilities and infrastructure in the environment around the school, in order to create an atmosphere that contributes to the formation of the character of students who are environmentally sound, build an extra-curricular climate that can contribute to environmental conservation, provide creativity and innovation for school residents in environmental protection and management efforts. The target audience for this service are students, teachers, and the junior high school environment in Maros Regency. The training activities was take place at SMP Negeri 16 Mandai, Maros Regency. The methods used include lectures, FGDs accompanied by demonstrations/practices, as well as monitoring and evaluation in class.
Estimasi Model Regresi Spline Kubik Tersegmen dengan Metode Penalized Least Square Anna Islamiyati; Anisa Anisa; Raupong Raupong; Jusmawati Massalesse; Nasrah Sirajang; Sitti Sahriman; Alfiana Wahyuni
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol 10, No 2 (2022): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam had Accre
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. 
Model Regresi Kuantil Spline Orde Dua Dalam Menganalisis Perubahan Trombosit Pasien Demam Berdarah Anisa Anisa; Anna Islamiyati; Sitti Sahriman; Jusmawati Massalesse; Bunga Aprilia
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1762.188 KB) | DOI: 10.34312/jjom.v5i1.16086

Abstract

Quantile regression can be used to analyze data containing outliers including DHF data. The spline is able to identify several patterns of change in the regression model, so this study uses a second-order quantile spline regression model in analyzing DHF data that occurred in Makassar City. In this article, the authors analyze the pattern of changes that occur in platelets based on changes in the hematocrit content of DHF patients. The selected quantiles are quartiles 0.25; 0.50; and 0.75 with 3-knot points. Based on the results of the analysis, the minimum GCV value obtained at the use of knot points is 30.30; 44.80; 47.10 for the 0.25 quartile; 0.50; and 0.75. This shows that in each quartile, there are four patterns of quadratic changes that occur in the platelet count of DHF patients. The parabolic curve formed in each pattern segmentation shows that there are times when platelets are increasing and there are times when platelets are decreasing. However, the average platelets decreased drastically, especially when the hematocrit reached 47.10%.
PEMODELAN STATISTICAL DOWNSCALING DENGAN PEUBAH DUMMY BERDASARKAN TEKNIK CLUSTER HIERARKI DAN NON- HIERARKI UNTUK PENDUGAAN CURAH HUJAN Sitti Sahriman; Anisa Kalondeng; Vieri Koerniawan
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.471

Abstract

Statistical downscaling (SD) is a statistical technique used to predict local scale rainfall based on global atmospheric circulation. The global scale climate variable used is precipitation from GCM (Global Circulation Model). However, the precipitation data of GCM outputs have a large dimension, giving rise to multicollinearity in the data. This problem is handled by the Principal Component Regression (PCR) method. In addition, the SD models have heterogeneous error variances. The dummy variable is added to the PCR models to solve the problem. Hierarchical (k-means) and non-hierarchical cluster techniques (average linkage, median linkage, and ward linkage) are used in modeling to determine rainfall data groups. Furthermore, the group formed is the basis of the formation of dummy variables. This study aims to estimate local rainfall data in Pangkep district as a salt-producing area in South Sulawesi. There are 4 dummy variables based on the 5 groups formed. Dummy variables are able to improve predictions from the PCR models. R2 values of the PCR-dummy models (ranging from 89.89% to 95.58%) are relatively higher than the PCR models (ranging from 55.87% to 57.61%). This result is also consistent with the model validation stage. The PCR-dummy models based on non-hierarchical cluster techniques (k-means) are better than the PCR-dummy models based on cluster hierarchy techniques. In general, the best model is the PCR-dummy model of the non-hierarchical cluster technique (k-means ) and involves 4 main components.
Co-Authors Afifah Afifah Ainun Mawaddah Abdal Alfiana Wahyuni Anisa Anisa Anisa Anisa Anisa Kalondeng Anna Islamiyati Anna Islamiyati Bunga Aprilia Edy Saputra Rusdi Herdiani, Erna Tri I Gusti Agung Komang Diafari Djuni Hartawan Irma Andriani Jusmawati Massalesse Jusmawati Massalesse Kasbawati Kasbawati La Podje Talangko Muhammad Idman Muralia Hustim Naimah Aris Najhah Aris Nasrah Sirajang Nur Erawaty Nurdin Nurdin Okta Nofri Raupong, Raupong Ririn Arianti Rizki Adiputra Sakinah Oktoni Sri Astuti Thamrin Vieri Koerniawan