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All Journal Media Statistika Manajemen Sumber Daya Manusia SIGMA: Jurnal Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL PengaMAS EDUKATIF : JURNAL ILMU PENDIDIKAN EduInovasi: Journal of Basic Educational Studies ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sukarna Sukarna
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CONWAY-MAXWELL POISSON REGRESSION MODELING OF INFANT MORTALITY IN SOUTH SULAWESI Oktaviana, Oktaviana; Sanusi, Wahidah; Aswi, Aswi; Sukarna, Sukarna; Folorunso, Serifat Adedamola
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.45-56

Abstract

Overdispersion is a common problem in count data that can lead to inaccurate parameter estimates in Poisson regression models. Quasi-Poisson and negative binomial regressions are often used to address overdispersion but have limitations, especially with small samples. The Conway-Maxwell Poisson (CMP) regression model, an extension of the Poisson distribution, effectively addresses both overdispersion and underdispersion, even with limited data, due to additional parameters that better control data dispersion. The Infant Mortality Rate (IMR) is a critical public health indicator, reflecting healthcare quality and broader social, economic, and environmental factors. Accurate IMR estimation is essential for evaluating health policies. This study aims to (1) identify overdispersion in IMR data from South Sulawesi, (2) model IMR using CMP regression, and (3) identify factors influencing IMR. The dataset includes IMR, Low Birth Weight (LBW), diarrhea, asphyxia, pneumonia, and exclusive breastfeeding. Analysis showed significant overdispersion with a ratio of 4.639, making CMP the optimal model with an AIC of 186.845. Significant factors identified were LBW, asphyxia, pneumonia, and exclusive breastfeeding. These findings advance statistical methodologies for count data analysis and offer a more accurate approach to evaluating public health policies, supporting efforts to reduce infant mortality in South Sulawesi Province.
Peranan Unit Payment Collection PT. Telkom Witel Makassar dalam Meningkatkan Kualitas Pelayanan Jaringan Indihome Harpiani, Harpiani; Kurnadipare, Aleytha Ilahnugrah; Firdausa, Farah Putri; Sukarna, Sukarna
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Publisher : LPPM UNIVERSITAS KHAIRUN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/pengamas.v7i1.6120

Abstract

Telkom Makassar is the largest telecommunications company in Indonesia with the most popular service currently being IndiHome as a network-based facility. This service is not free but is paid through a good payment system. Payment Collection is one of the units engaged in receivables collection activities that require customers to make payments in the current month or in arrears in the previous month. PKL students are involved in the success of this Payment Collection program so that they know in detail about aspects related to Payment Collection. This paper aims to describe the role of Payment Collection in improving the service quality of IndiHome products. The method used in this activity is collaborative participatory qualitative descriptive, which is a new method used by involving oneself in the process of collecting actual and detailed information so that it can be used as an evaluation for the future. The results obtained from this activity are the process of increasing the quality of IndiHome network services at Telkom Makassar, although this increase still involves the Payment Collection unit itself. Thus, it can be concluded that the success of the Payment Collection in its role in dealing with and handling customer complaints is greatly helped by our involvement as the executor of customer service assistants for IndiHome products
Mapping the Relative Risk of Tuberculosis in Indonesia Using the Bayesian Spatial Conditional Autoregressive Leroux Model Aswi, Aswi; Nurhikmawati, Nurhikmawati; Shanty, Meyrna Vidya; Herman, Nur Taj Alya’; Sukarna, Sukarna
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6814

Abstract

Tuberculosis (TB) is an infectious disease caused by infection with the Mycobacterium Tuberculosis bacteria. Indonesia ranks second globally in terms of the number of TB cases, after India, followed by China. Modeling is needed to evaluate the relative risk (RR) of TB cases in Indonesia to identify areas that have a high RR of being infected with the bacteria. One approach used to estimate the RR of TB in Indonesia is Bayesian Conditional Autoregressive (CAR). This research aims to identify the RR rate of TB cases in Indonesia using the Bayesian spatial CAR Leroux approach based on TB case data from 2021 to 2022. The best model selection is based on Deviance Information Criteria values, the Watanabe Akaike Information, and residuals from Modified Moran's I. Analysis results shows that in 2021, the Bayesian spatial CAR Leroux Model with Inverse Gamma prior (0.5; 0.5) is the best model. DKI Jakarta Province has the highest while Bali Province has the lowest RR. In 2022, the Bayesian spatial CAR Leroux Model with Inverse Gamma prior (1;0.01) is the best model, with DKI Jakarta Province still having the highest RR, while Bali still has the lowest RR.
ANALISIS SPASIAL SEBARAN PENYAKIT MENULAR KOTA MAKASSAR TAHUN 2018 Sukarna, Sukarna; Awi, Awi; Sutamrin, Sutamrin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 1 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (668.269 KB) | DOI: 10.30598/barekengvol14iss1pp113-122

Abstract

Population density correlates spatially with various aspects of community life, such as the number of people with infectious and non-infectious diseases, seasonal (endemic) or non-seasonal (epidemic), mild or severe, and regular or rare. This research focuses on 8 infectious diseases, namely measles, diphtheria, diarrhea, typhoid, malaria, helminthiasis, TB, and DHF. Data on infectious diseases in 2018 were obtained from the Makassar City Health Service Office, which was sourced from 46 puskesmas throughout Makassar City. The results of this study are (1) The limited data, thus, this study focuses on only 8 diseases; (2) Based on the results of the analysis of univariate cluster Moran index, there are (a) the two highest warnings of disease distribution attributed to the districts, namely diarrhea (in Sangkarrang, Ujungtanah, and Mariso) and helminthiasis (in Singkarang); (b) patterns of spread of infectious diseases (this article shows only one thematic map of disease) and spread from nothing significant to very significant; (c) the spread of infectious diseases appears uneven in each district and tends to occur in certain districts, such as (i) Measles have 3 districts (Biringkanaya, Tamalanrea, and Bontoala), (ii) Diphtheria there are 2 districts (Tamalanrea and Biringkanaya), ( iii) Diarrhea only in Panakkukang District, (iv) Malaria only in Ujungtanah, (v) Worms in 4 districts (Manggala, Rappocini, Makassar, and Ujungpandang), (vi) DHF only in Panakkukang District, (vii) Typhoid and TB are not significant.
QUANTILE REGRESSION MODEL ON RAINFALL IN MAKASSAR 2019 Sanusi, Wahidah; Sukarna, Sukarna; Harisahani, Nur
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 (360.168 KB) | DOI: 10.30598/barekengvol17iss1pp0001-0008

Abstract

Makassar is an area that has a monsoon rainfall pattern. This study aims to find a quantile regression model and to determine the factors that significantly influence rainfall in the city of Makassar. This applied research applies a quantile regression model to rainfall data which is seasonal data. The advantage of this quantile regression model is that it is able to detect extreme conditions of rainfall, such as heavy rain. The data used is daily data in 2019. The estimation results obtained 9 (nine) models from each quantile used. The best model is obtained based on the largest coefficient of determination ( ), namely the 0,8th quantile ( ) of 0.28%. Furthermore, based on the model, it is found that the factor that significantly influences rainfall in the city of Makassar is humidity. At the same time, the air temperature and wind speed have no significant effect on rainfall in the city of Makassar.
MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION Aswi, Aswi; Tiro, Muhammad Arif; Sudarmin, Sudarmin; Sukarna, Sukarna; Awi, Awi; Nurwan, Nurwan; Cramb, Susanna
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.148-159

Abstract

Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.
COMPARISON OF LOGISTIC MODEL TREE AND RANDOM FOREST ON CLASSIFICATION FOR POVERTY IN INDONESIA Sukarna, Sukarna; Notodiputro, Khairil Anwar; Sartono, Bagus
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.112-123

Abstract

Classification methods are commonly employed to ensure homogeneous data within each group, facilitating the prediction of specific categories. The most frequently used classification models are Logistic Model Tree (LMT) and Random Forest (RF). This study aims to assess the accuracy rate in predicting the poverty status of regencies or towns across Indonesia, utilizing eight independent variables. The entire dataset was obtained from the official Central Bureau of Statistics website. The study investigates the accuracy of various iterations and combinations of training data. The results indicate that RF outperforms LMT in terms of accuracy, achieving a 100% improvement in iterations k=10 and k=500 and a 75% improvement in iteration k=100. Consequently, the RF proves to be more effective than the LMT for analyzing Indonesian poverty data, especially when incorporating all eight independent variables.
Co-Authors Aswi, Aswi Aswi, Aswi Awi, Awi Bagus Sartono Cramb, Susanna Fahmuddin, Muhammad Firdausa, Farah Putri Folorunso, Serifat Adedamola Harisahani, Nur Harpiani, Harpiani Herman, Nur Taj Alya’ Kurnadipare, Aleytha Ilahnugrah Muh Tawil, Muh Muhammad Arif Tiro, Muhammad Arif Nurhikmawati, Nurhikmawati Nurhilaliyah, Nurhilaliyah Nurmah, Nurmah Nurwan, Nurwan Oktaviana Oktaviana Poewanto, Bobby Rahmat Syam Rosadi, Muh. Irham Shanty, Meyrna Vidya Sudarmin Sudarmin Suddin, Alwi Sutamrin, Sutamrin Sutarno Sutarno Syafaruddin Syafaruddin Wahidah Sanusi Wahyuni, Maya Sari