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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang Buletin Poltanesa Eksponensial
Suyitno Suyitno
Dosen Program Studi Statistika FMIPA Universitas Mulawarman
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Other

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Geographically Weighted Poisson Regression Model with Adaptive Bisquare Weighting Function (Case study: data on number of leprosy cases in Indonesia 2020) Ineu Sintia; Suyitno Suyitno; Memi Nor Hayati
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21879

Abstract

Abstract Geographically Weighted Poisson Regression (GWPR) is a Poisson regression model which is applied on spatial data. The parameter estimation of GWPR is done in each observation location through spatial weighting. This study aims to determine the GWPR model of the number of leprosy cases in each province of Indonesia 2020 and to find the influencing factors. The research uses secondary data collected from Indonesian Ministry of Health and Central Statistics Agency. The spatial weighting is calculated by using the adaptive bisquare function, while the optimum bandwidth is determined by using Generalized Cross-Validation criteria (GCV). The parameter estimation of GWPR uses Maximum Likelihood Estimation (MLE) method. The result of research show that the closed form of Maximum Likelihood (ML) estimator can not be found analytically and that the approximation of ML estimator is found by using Newton-Raphson iterative method. Based on the parameter significance test of the GWPR model, the factors that influenced the number of leprosy cases locally are the percentage of households that have access to proper sanitation, population density, the percentage of people who experience health complaints and outpatient, the number of health workers, the percentage of poor people, the percentage of districts/cities that carry out healthy living community movement (GERMAS) and the percentage of habitable houses. While the factors that globally affected the number of leprosy cases are  the percentage of households that have access to proper sanitation, population density, the percentage of people who experience health complaints and outpatient, the number of health workers, the percentage of poor people, the percentage of districts/cities that carry out GERMAS.  
Pemodelan Geographically Weighted Panel Regression pada Data Indeks Pembangunan Manusia di Provinsi Kalimantan Timur Tahun 2017-2020 Ni Made Shantia Ananda; Suyitno Suyitno; Meiliyani Siringoringo
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 2 (2023): JANUARY 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i2.23775

Abstract

Geographically Weighted Panel Regression (GWPR) model is a panel regression model applied on spatial data. This research applied Fixed Effect Model (FEM) on panel regression as the global model and GWPR as the local model for Human Development Index (HDI) regencies/municipalities in East Kalimantan Province data over the years 2017-2020. The aim of this research is to obtain the GWPR model of HDI data, as well as to acquire factors that influence it. The parameter of GWPR model was estimated on each observation location using the Weighted Least Square (WLS) method, namely Ordinary Least Square (OLS) with addition of spatial weighting. The spatial weighting on GWPR model was calculated using fixed bisquare, fixed tricube, adaptive bisquare and adaptive tricube. After the selection process, the optimum weighting function is adaptive tricube which provides a minimum Cross Validation (CV) value of 5.1419. Based on GWPR parameter testing, factors that affect HDI are local and diverse in each 10 regencies/municipalities in East Kalimantan Province. These factors are the labor force participation rate, number of health facilities, Gini ratio, population growth rate, open unemployment rate, poverty gap index and percentage of food expenditure. The coefficient of determination of the GWPR model obtains a value of 94.36% with the RMSE value of 0.1221.
Penaksiran Parameter Model Mixed Geographically Weighted Regression (MGWR) Data Indeks Pembangunan Manusia di Kalimantan Tahun 2016 Mita Asti Wulandari; Suyitno Suyitno; Wasono Wasono
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (795.642 KB)

Abstract

Mixed Geographical Regression (MGWR) model is a combination of global linear regression model and GWR model. Some MGWR parameters are global (the same value) and the other parameters are local (different values) ​​at each observation location. The purpose of this study is to obtain MGWR model for every District’s HDI and to obtain the factors that significantly influence District HDI in East Kalimantan, Central Kalimantan and South Kalimantan Provinces. Estimating parameters for global parameters use Ordinary Least Square (OLS) method. Estimating parameters for local parameters use Weighted Least Square (WLS) method, where weighting spatial is determined by using gaussian adaptive function. Based on the result of MGWR parameters testing, it was concluded that the school enrollment rates (SMP) affected the HDI of all districs in East Kalimantan, Central Kalimantan and South Kalimantan provinces. The population density and the percentage of poor people influence locally to HDI.
Penaksiran Parameter dan Pengujian Hipotesis Model Regresi Weibull Univariat Suyitno Suyitno
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (509.861 KB) | DOI: 10.30872/eksponensial.v8i2.41

Abstract

In this study, a univariate Weibull regression model is discussed. The Weibull regression is a regression model developed from the Weibull distribution, that is the Weibull distribution depending on the covariates or the regression parameters. The univariate Weibull regression (UWR) model can involve the survival function model and the mean model of the response variable with the scale parameter stated in the terms of the regression parameters. The aim of this study is to estimate the UWR model parameters using the maximum likelihood estimation (MLE) method, and to test the regression parameters. The result shows that the closed form of the maximum likelihood estimator can not be found analytically, and it can be approximed by using the Newton-Raphson iterative method. The regression parameters testing involves simultaneous and partial test. The test statistic for simultaneous test is Wilk's likelihood ratio. Wilk statistic follows Chi-square distribution, which can be derived from the likelihood ratio test (LRT) method. The test statistic for partial test is Wald and it follows standard normal distribution. The alternative test statistik for partial test is squared of Wald statistic, where it follows Chi-square distribution with one degree of freedom.
K-Means Algorithm for Grouping Provinces in Indonesia Based on Macroeconomic and Criminality Indicators Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Meirinda Fauziyah; Sifriyani Sifriyani; Suyitno Suyitno; M Fathurahman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.12-21

Abstract

Cluster analysis is a method in multivariate analysis to group n observations into K groups (K ≤ n) based on their characteristics. One of the well-known algorithms in cluster analysis is K-Means. K-Means uses the non-hierarchical principle where at the initial initiation, it is necessary to determine the number of groups in advance. The K-Means algorithm can be applied to classify provinces in Indonesia based on macroeconomic indicators (percentage of poor people, open unemployment rate, and Gini ratio) and crime rate (Crime rate). The ultimate goal of this research is of course to get optimal grouping results. The similarity measure used is Euclidean Distance. The number of groups tested K=2,3,4,…,10 and the optimal number of groups with the highest Silhouette value was selected. Based on the results of the analysis, the optimal number of clusters is four. These four clusters have characteristics that distinguish one cluster from another.
Pemodelan Peluang Pencemaran Air Sungai Menggunakan Model Geographically Weighted Logistic Regression (Studi Kasus: Data DO Air Sungai di Kalimantan Timur) Adelia Miranda; Suyitno Suyitno; Meirinda Fauziyah
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.v21i2.40346

Abstract

Geographically Weighted Logistic Regression (GWLR) is a local binary logistic regression model, and it’s applied to the spatial heterogeneity data. The parameter estimation of GWLR model in this study uses Maximum Likelihood Estimation (MLE) method, and it’s conducted at each observation location with spatial weighting. The spatial weight in this study was calculated using the adaptive tricube function. The spatial weighting function depends on distance between observation location and bandwidth, where the determination of optimal bandwidth uses the Akaike Information Criterion (AIC). The aim of this research is to identify the factors influencing the probability of river water pollution in East Kalimantan Province through GWLR modelling to Dissolved Oxygen (DO) data 2022, and to interpret it based on the best model. The research data is secondary data provided by Life Environment Department of East Kalimantan Province. Research concludes that the GWLR was fit model based on the results of similarity testing of the GWLR model and global model, as well as simultaneous parameter testing, with the model fitting measure was a McFadden R-Squared value of 61,1%, and an AIC value of 29,629. Based on partial parameter testing, local factors influencing chance of river water pollution in East Kalimantan can be identified, namely nitrate concentration and water color degree. Based on the GWLR modelling to DO data 2022, it can be interpreted that increasing nitrate concentration and water colour degree respectively will increase the probability of river water pollution
River Water Quality Analysis in the Tropical Rainforest of East Kalimantan Using a Mixed Geographically Weighted Regression Model Devita Dwi Putri; Suyitno Suyitno; Desi Yuniarti
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3852

Abstract

Rivers in East Kalimantan serve as the primary raw water source for most of the population, making water quality monitoring an urgent priority. This study aims to model Dissolved Oxygen (DO) as a river water quality indicator in East Kalimantan, and to identify the factors influencing DO levels through the Mixed Geographically Weighted Regression (MGWR) approach. MGWR is a useful tool for modeling regression relationships where the impact of some explanatory variables on the response variable is global, while the influence of others varies spatially. This study uses secondary data, namely the 2025 surface water quality monitoring analysis report  sourced from the Environmental Agency of East Kalimantan. The sample size consists of 27 observation points of Class I river water quality in East Kalimantan in 2025. The spatial weighting is computed using an adaptive Bi-Squares kernel function,  and the optimal bandwidth is selected using Generalized Cross-Validation (GCV) criterion . The results showed that the MGWR model outperformed both the linier regression and Geogrpahically Weighted Regression (GWR) models, yielding  a GCV of 0,003,  R² of  0,940, an AIC of  −75,762, and  RMSE of 0,175. BOD, sulfate concentration, and TDS were found to globally influence DO levels, while pH exhibited a spatially varying local influence across observation sites. The results of this study can serve as a basis for policy-making in river water quality monitoring in East Kalimantan. These findings are expected to assist the government in formulating more effective and targeted policies for river water quality management in East Kalimantan.
Peramalan Nilai Transaksi Uang Elektronik Di Indonesia Menggunakan Double Exponential Smoothing Brown Dengan Optimasi Nonlinier Anna Putri Aritonang; Meiliyani Siringoringo; Wiwit Pura Nurmayanti; Sri Wahyuningsih; Suyitno Suyitno
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/8hjsey81

Abstract

The Double Exponential Smoothing (DES) Brown method is one of the forecasting methods used for data that exhibited a trend pattern, in which the smoothing process was performed twice. The determination of the optimal smoothing parameter in the DES Brown method is usually carried out through a trial-and-error process. Another way to obtain the optimal smoothing parameter value more quickly and accurately is by using nonlinear optimization. In this study, two optimization methods were used: the Golden Section and the Levenberg-Marquardt methods. The objectives of this research were to obtain the optimal smoothing parameter of the DES Brown method using the Golden Section and Levenberg-Marquardt optimizations, to forecast the value of electronic money transactions in Indonesia for the period of January to March 2025 using the DES Brown method with the optimal smoothing parameter, and to identify the best optimization method for determining the optimal smoothing parameter of DES Brown method were obtained based on the MAPE value. The results of the study showed that the optimal smoothing parameter of the DES Brown method using the Golden Section optimization was 0.4634178 and the Levenberg-Marquardt optimization was 0.3498674. 
Co-Authors Adelia Miranda Andrea Tri Rian Dani Anna Putri Aritonang Desi Yuniarti Devita Dwi Putri Fachrian Bimantoro Putra Ineu Sintia M. Fathurahman Meiliyani Siringoringo Meirinda Fauziyah Meirinda Fauziyah Memi Nor Hayati Mita Asti Wulandari Ni Made Shantia Ananda Nur - Azizah Sifriyani Sifriyani Sri Wahyuningsih Wasono Wasono Wiwit Pura Nurmayanti