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All Journal Media Statistika SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Matematika Sains dan Teknologi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) MATAPPA: Jurnal Pengabdian Kepada Masyarakat Jurnal Varian Inferensi ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) Variansi : Journal of Statistics and Its Application on Teaching and Research Amaliah: Jurnal Pengabdian Masyarakat SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research Indonesian Journal of Fundamental Sciences Seminar Nasional Hasil Penelitian LP2M UNM SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Aswi, Aswi
Prodi Statistika, FMIPA, Universitas Negeri Makassar
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Journal : Inferensi

 1 
Evaluating the Performance of Zero-Inflated and Hurdle Poisson Models for Modeling Overdispersion in Count Data Aswi Aswi; Sri Ayu Astuti; Sudarmin Sudarmin
Inferensi Vol 5, No 1 (2022): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i1.12422

Abstract

A Poisson regression model is commonly used to model count data. The Poisson model assumes equidispersion, that is, the mean is equal to the variance. This assumption is often violated. In count data, overdispersion (the variance is larger than the mean) occurs frequently due to excessive zeroes in the response variable. Zero-inflated Poisson (ZIP) and Hurdle models are commonly used to fit data with excessive zeros. Although some studies have compared the ZIP and Hurdle models, the results are inconsistent. This paper aims to evaluate the performance of ZIP and Hurdle Poisson models for overdispersion data through both simulation study and real data. Data were simulated with three different sample sizes, six different means, and three different probabilities of zero with 500 replications. Model goodness-of-fit measures were compared by using Akaike Information Criteria (AIC). Overall, the ZIP model performed relatively the same or better than the Hurdle Poisson model under different scenarios, but both ZIP and Hurdle models are better than the standard Poisson model for overdispersion in count data.
Factors Affecting the Covid-19 Risk in South Sulawesi Province, Indonesia: A Bayesian Spatial Model Aswi Aswi; Sukarna Sukarna
Inferensi Vol 5, No 1 (2022): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i1.12527

Abstract

The transmission of Coronavirus diseases 2019 (Covid-19) grows continuously around the world. Although a number of researches of modelling Covid-19 cases have been conducted, there was limited research implementing the Bayesian Spatial Conditional Autoregressive (CAR) model. Factors affecting the Covid-19 risk especially population density and distance to the capital city have been studied, but the results are inconsistent and limited research has been done in Indonesia. This study aims to assess the most appropriate Bayesian spatial CAR Leroux models and examine factors that affect the risk of Covid-19 in South Sulawesi Province. Data on the number of Covid-19 cases (19 March 2020 - 31 January 2022), population density, and distance to the capital city were used for every 24 districts. Several criteria were used in choosing the most appropriate model. The results depict that Bayesian spatial CAR Leroux with hyperprior IG (1, 0.01) model with the inclusion of population density were preferred. It is concluded that a factor that significantly affects the number of Covid-19 cases is population density. There was a positive correlation between the population density and Covid-19 risk. Makassar city has the highest relative risk (RR) among other districts while Bone has the lowest RR of Covid-19.
Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) dalam Peramalan Data Curah Hujan di Kota Makassar Nurul Ilmi; Aswi Aswi; Muhammad Kasim Aidid
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.14347

Abstract

Modeling of rainfall data using time series data involving location elements has not been widely carried out. One model that involves elements of time and location is Space Time Autoregressive (STAR). The development of the STAR model which assumes that each location has heterogeneous characteristics is the Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) model. The purpose of this research is to get the best GSTARIMA model and forecast rainfall data in Makassar City based on the best GSTARIMA model. This model incorporates time and geographic dependencies with different parameters for each location. The data used is Makassar city's monthly rainfall data at the Bawil IV/Panaikang, Biring Romang/Panakkukang and Stammar Paotere rain stations from January 2017 to September 2021. Autoregressive (AR) and Moving Average (MA) orders were identified using the Space Time Autocorrelation plot. Function (STACF) and Space Time Partial Autocorrelation Function (STPACF). The spatial order used in this study is spatial order 1 with an inverse distance weighting matrix and normalized cross-correlation. Parameters were estimated using the Generalized Least Squares (GLS) method. The best model for predicting rainfall in the city of Makassar is the GSTARIMA (1,0,0) (1,1,0)12  model using an inverse distance weighting matrix with the smallest average Root Mean Square Error (RMSE) of 132.9661.
Pemetaan Risiko Relatif Kasus Demam Berdarah Dengue di Kota Makassar Menggunakan Model Bayesian Spasial Andi Feriansyah; Idul Fitri Abdullah; Siti Choirotun Aisyah Putri; Mardatunnisa Isnaini; Aswi Aswi
Inferensi Vol 6, No 2 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i2.15931

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease that is still a main problem in public health in Indonesia. This study aims to map the relative risk (RR) of dengue cases in Makassar City using the Spatial Conditional Autoregressive (CAR) model with Bayesian approaches: Besag-York-Molliѐ (BYM) and Leroux models. The data used in this study is DHF case data from 2016 to 2018 for 15 sub-districts in Makassar City. The best model was based on the model fit criteria, namely Watanabe Akaike Information Criteria (WAIC) and Deviance Information Criteria (DIC). The results indicate that the best model used to map the RR for DHF cases in 2016 and 2017 is the BYM CAR model, while the best model for 2018 is the Leroux CAR model. Based on the results of the analysis, it was concluded that in 2016 the area with the highest RR was Manggala District and the lowest RR was Tamalate District. In 2017, the area with the highest RR was Ujung Pandang District and the lowest RR was Biringkanaya District. Meanwhile, in 2018, the area with the highest dan the lowest RR was Ujung Tanah and Tamalate Districts, respectively. The results of this study are expected to be able to assist the government in implementing the program to control dengue fever in Makassar City effectively and efficiently.Keywords⎯ Dengue Hemorrhagic Fever, Relative Risk Mapping, CAR BYM, CAR Leroux.
Spatial Survival Analysis of Stroke Hospitalizations: A Bayesian Approach Aswi, Aswi; Poerwanto, Bobby; Hammado, Nurussyariah
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.22252

Abstract

Survival analysis encompasses a range of statistical techniques used to evaluate data where the outcome variable represents the time until a specific event occurs. When such data is collected across different spatial regions, integrating spatial information into survival models can enhance their interpretive power. A widely adopted method involves applying an intrinsic conditional autoregressive (CAR) prior to an area-level frailty term, accounting for spatial correlations between regions. In this study, we extend the Bayesian Cox semiparametric model by incorporating a spatial frailty term using the Leroux CAR prior. This approach aims to enhance the model's capacity to analyze stroke hospitalizations at Labuang Baji Hospital in Makassar, with a particular focus on exploring the geographic distribution of hospitalizations, length of stay (LOS), and factors influencing patient outcomes. The dataset, derived from the medical records of stroke patients admitted to Labuang Baji Hospital between January 2022 and June 2024, included variables such as LOS, discharge outcomes, sex, age, stroke type, hypertension, hypercholesterolemia, and diabetes mellitus. The analysis revealed that stroke type was a significant determinant of hospitalization outcomes. Specifically, ischemic stroke patients exhibited faster recovery times than those with hemorrhagic strokes, with a hazard ratio of 1.892, representing an 89% greater likelihood of recovery. Additionally, stroke patients across all districts treated at Labuang Baji Hospital demonstrated similar average recovery rates and discharge durations.
Penerapan Metode Hybrid Dekomposisi-Arima dalam Peramalan Jumlah Wisatawan Mancanegara Aswi, Aswi; Rahma, Ina; Fahmuddin, Muhammad
Inferensi Vol 7, No 1 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i1.18738

Abstract

The Decomposition-ARIMA hybrid method is a combination of two methods used to predict future events in time series data. This method separates the data into three components: the seasonal component, the trend component, and the random component. The decomposition method is employed to forecast the seasonal and the trend components in a data series, while the ARIMA method is utilized to predict the random component within the data series. A tourist is an individual who visits an area for a specific period, making use of its facilities and infrastructure. In order to ascertain the growth of the number of foreign tourists, this study employs the decomposition-ARIMA hybrid method. The aim is to derive forecasting results from the data on the count of foreign tourists from January 2022 to December 2022. The research finding indicates that the best ARIMA model is ARIMA (0, 1, 1) with a Mean Absolute Percentage Error (MAPE) of 8.5% signifying a very high forecast accuracy.
Co-Authors A. Nurul Amalia AA Sudharmawan, AA Abdul Rahman Aidid, Muhammad Kasim Andi Feriansyah Andi Feriansyah Andi Gagah Palarungi Taufik Andi Muhammad Ridho Yusuf Sainon Andin P Andi Shahifah Muthahharah Ankaz As Sikib Annas, Suwardi Asrirawan Awaluddin Awaluddin Awi Awi Bobby Poerwanto Bobby Poerwanto Bobby Poerwanto Bustan, Muhammad Nadjib Fahmuddin, Muhammad Halimah Husain Hammado, Nurussyariah Hisyam Ihsan Idul Fitri Abdullah Irwan Irwan Isnaini, Mardatunnisa Kaito, Nurlaila M Nadjib Bustan Mahadtir, Muhamad Mardatunnisa Isnaini Mauliyana, Andi Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Ammar Naufal Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro, Muhammad Arif Muhammad Fahmuddin Muhammad Fahmuddin Muhammad Fahmuddin Sudding Muhammad Kasim Aidid Natalia, Derliani Nini Harnikayani Hasa Nur Aziza S Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah, Nurhilaliyah Nurkaila Kaito Nurul Fadilah Syahrul Nurul Ilmi Nusrang, Muhammad Oktaviana Oktaviana Poerwanto, Bobby Putri, Siti Choirotun Aisyah Rahma, Ina Rahman, Abdul Rahmawati Rahmawati Ramadani, Reski Aulia Rezki Amalia Idrus Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana, Ruliana Sahlan Sidjara Salsabila, Afifah Sapriani Shanty, Meyrna Vidya Siti Choirotun Aisyah Putri Sri Ayu Astuti Sri Rahayu Suardi, Shafira Suci Amaliah Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Supriadi Yusuf Susanna Cramb Suwardi Annas Suwardi Annas Syafruddin Side Syamsiar, Syamsiar Wahidah Sanusi Wea, Maria Dominggo Yassar, La Ode Salman Zulhijrah Zulhijrah Zulhijrah Zulkifli Rais