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<![CDATA[MODEL BAYESIAN SPASIAL CAR LOCALISED: STUDI KASUS DEMAM BERDARAH DENGUE DI KOTA MAKASSAR ]]>
<![CDATA[Aswi, Aswi]]>;
<![CDATA[Sukarna, Sukarna]]>
<![CDATA[ Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika, FMIPA, Universitas Negeri Makassar ]]>
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<![CDATA[Berbagai model Bayesian telah digunakan untuk menggambarkan pola spasial untuk data area. Dalam tulisan ini, kami mengaplikasikan model Bayesian spasial Conditional Autoregressive (CAR) localised yang memungkinkan untuk pembentukan pengelompokkan risiko relatif suatu kasus penyakit dalam hal ini kasus Demam Berdarah Dengue (DBD). Data yang digunakan adalah data kasus DBD tahun 2013-2015 untuk 14 wilayah kecamatan di Kota Makassar. Formula model Bayesian spasial CAR localised yang berbeda beda dibandingkan dengan menggunakan beberapa kriteria kecocokan model yaitu Deviance Information Criteria, Watanabe Akaike Information Criteria, residu dari Modified Moran’s I dan banyaknya wilayah yang termasuk dalam suatu kelompok. Penggunaan model Bayesian spasial CAR localised direkomendasikan jika rata rata dan variansi peubah terikat antar wilayah relatif besar karena dapat mengidentifikasi kelompok area yang berisiko tinggi, sedang dan rendah. Jika nilai rata rata dan variansi antar wilayah relatif besar, pembentukan kelompok dan anggotanya dipengaruhi juga oleh pemilihan hyperprior pada deviasi standar. Kecamatan Rappocini, Manggala dan Tamalanrea merupakan kecamatan yang memiliki risiko relatif yang tinggi untuk terjangkit DBD. Hasil ini dapat dijadikan rujukan pagi para pengambil kebijakan khususnya di bidang kesehatan. Kata Kunci: Conditional Autoregressive, Demam berdarah Dengue, Pengelompokan, Risiko Relatif]]>
<![CDATA[EKSPLORASI LITERASI STATISTIKA DESKRIPTIF MAHASISWA PROGRAM STUDI STATISTIKA UNIVERSITAS NEGERI MAKASSAR DALAM SUASANA PEMBELAJARAN DARING AKIBAT DARURAT COVID-19 ]]>
<![CDATA[Tiro, Muhammad Arif]]>;
<![CDATA[Ruliana, Ruliana]]>;
<![CDATA[Aswi, Aswi]]>
<![CDATA[ Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020) ]]>
Publisher : <![CDATA[Program Studi Statistika, FMIPA, Universitas Negeri Makassar ]]>
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<![CDATA[Sejak Maret 2020, pembelajaran di Universitas Negeri Makassar dilaksanakan secara daring (online) akibat pandemi COVID-19. Situasi ini tentu mempengaruhi kegiatan pembelajaran di kampus. Penelitian eksploratif ini bertujuan untuk mengeksplorasi pencapaian peubah literasi statistika deskriptif bagi mahasiswa Program Studi Statistika Universitas Negeri Makassar di masa pandemi menurut lima kompetensi dasar literasi statistika. Peneliti mengadaptasi model pengembangan instrument 4-D menjadi model 4-P dalam pengembangkan instrumen penilaian literasi statistika deskriptif. Selanjutnya, instrumen penilaian yang diperoleh diterapkan untuk memetakan mutu literasi statistika deskriptif mahasiswa. Kelima kompetensi dasar literasi statistika tersebut adalah: (1) pemahaman konsep statistika deskriptif, (2) keterampilan menghitung nilai statistika deskriptif, (3) wawasan aplikasi statistika deskriptif, (4) kecermatan interpretasi nilai statistika deskriptif, dan (5) keterampilan visualisasi dan komunikasi informasi statistika deskriptif. Kompetensi yang mencapai tingkat capaian tertinggi adalah keterampilan visualisasi (60%) sedangkan kecermatan interpretasi merupakan capaian terendah (32%). Secara umum, capaian dalam hal literasi statistika deskriptif mahasiswa Program Studi Statistika UNM dalam suasana pembelajaran daring akibat darurat Covid-19 tergolong sedang. Materi statistika deskriptif yang diajarkan dan sistem penilaian di perguruan tinggi perlu menekankan pada lima kompetensi dasar yang telah dijelaskan. Kata Kunci: literasi statistika deskriptif, kompetensi literasi statistika deskriptif]]>
<![CDATA[RELATIVE RISK OF CORONAVIRUS DISEASE (COVID-19) IN SOUTH SULAWESI PROVINCE, INDONESIA: BAYESIAN SPATIAL MODELING ]]>
<![CDATA[Aswi, Aswi]]>;
<![CDATA[Mauliyana, Andi]]>;
<![CDATA[Tiro, Muhammad Arif]]>;
<![CDATA[Bustan, Muhammad Nadjib]]>
<![CDATA[ MEDIA STATISTIKA Vol 14, No 2 (2021): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.14.2.158-169
<![CDATA[The Covid-19 has exploded in the world since late 2019. South Sulawesi Province has the highest number of Covid-19 cases outside Java Island in Indonesia. This paper aims to determine the most suitable Bayesian spatial conditional autoregressive (CAR) localised models in modeling the relative risk (RR) of Covid-19 in South Sulawesi Province, Indonesia. Bayesian spatial CAR localised models with different hyperpriors were performed adopting a Poisson distribution for the confirmed Covid-19 counts to examine the grouping of Covid-19 cases. All confirmed cases of Covid-19 (19 March 2020-18 February 2021) for each district were included. Overall, Bayesian CAR localised model with G = 5 with a hyperprior IG (1, 0.1) is the preferred model to estimate the RR based on the two criteria used. Makassar and Toraja Utara have the highest and the lowest RR, respectively. The group formed in the localised model is influenced by the magnitude of the mean and variance in the count data between areas. Using suitable Bayesian spatial CAR localised models enables the identification of high-risk areas of Covid-19 cases. This localised model could be applied in other case studies.]]>
<![CDATA[Evaluating the Performance of Zero-Inflated and Hurdle Poisson Models for Modeling Overdispersion in Count Data ]]>
<![CDATA[Aswi Aswi]]>;
<![CDATA[Sri Ayu Astuti]]>;
<![CDATA[Sudarmin Sudarmin]]>
<![CDATA[ Inferensi Vol 5, No 1 (2022): Inferensi ]]>
Publisher : <![CDATA[Department of Statistics ITS ]]>
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DOI: 10.12962/j27213862.v5i1.12422
<![CDATA[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.]]>
<![CDATA[Factors Affecting the Covid-19 Risk in South Sulawesi Province, Indonesia: A Bayesian Spatial Model ]]>
<![CDATA[Aswi Aswi]]>;
<![CDATA[Sukarna Sukarna]]>
<![CDATA[ Inferensi Vol 5, No 1 (2022): Inferensi ]]>
Publisher : <![CDATA[Department of Statistics ITS ]]>
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DOI: 10.12962/j27213862.v5i1.12527
<![CDATA[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.]]>
<![CDATA[Pelatihan Analisis Spasial Menggunakan R Studio ]]>
<![CDATA[Aswi Aswi]]>;
<![CDATA[Bobby Poerwanto]]>;
<![CDATA[Sudarmin Sudarmin]]>
<![CDATA[ MATAPPA: Jurnal Pengabdian Kepada Masyarakat Volume 5 Nomor 1 Tahun 2022 ]]>
Publisher : <![CDATA[STKIP Andi Matappa ]]>
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DOI: 10.31100/matappa.v5i1.1396
<![CDATA[Pelatihan ini bertujuan untuk melatih para peserta melakukan analisis spasial dengan menggunakan software R Studio. Dalam pelaksanaannya, kegiatan terdiri dari 3 sesi yaitu sesi pendampingan instalasi software, pemberian materi, dan tanya jawab. Umpan balik yang diberikan peserta dalam hal kesesuaian materi dengan kebutuhan, kemudahan penerapan materi, sistematika penyampaian materi, dan penguasaan materi narasumber juga sangat baik.]]>
<![CDATA[PERBANDINGAN METODE MOMEN, MAXIMUM LIKELIHOOD DAN BAYES DALAM MENDUGA PARAMETER DISTRIBUSI PARETO ]]>
<![CDATA[A. Nurul Amalia]]>;
<![CDATA[Muhammad Arif Tiro]]>;
<![CDATA[Aswi Aswi]]>
<![CDATA[ VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021) ]]>
Publisher : <![CDATA[Universitas Negeri Makassar ]]>
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DOI: 10.35580/variansiunm26374
<![CDATA[This study examines the estimation of Pareto distribution parameters using three different methods, namely the Moment, Maximum Likelihood, and Bayesian methods. The Pareto distribution is a continuous distribution with parameters k > 0 and α > 0. These two parameters are estimated by using three distinct parameter estimation methods. The goodness of fit measure used in choosing the best estimation method is the Mean Square Error (MSE) value. The smallest MSE is the best method. A simulation study is carried out as well as the case study of the data on the number of Gross National Income (GNI) per capita in Southeast Asian countries in 2019. The estimation and simulation results indicate that the best estimation method in estimating the parameters of the Pareto distribution is the Maximum Likelihood in terms of MSE value.Keywords: Pareto distribution, Moment Method, Maximum Likelihood IMethod, Bayesian Method]]>
<![CDATA[Pemetaan Risiko Relatif Kasus Stunting di Provinsi Sulawesi Selatan ]]>
<![CDATA[Aswi Aswi]]>;
<![CDATA[Sukarna Sukarna]]>;
<![CDATA[Nurhilaliyah Nurhilaliyah]]>
<![CDATA[ Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 11, No 1 (2022): Maret ]]>
Publisher : <![CDATA[Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar ]]>
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DOI: 10.35580/sainsmat111325202022
<![CDATA[Indonesia merupakan negara dengan prevalensi balita stunting tertinggi ketiga di regional Asia Tenggara. Provinsi Sulawesi Selatan sebagai salah satu provinsi di Indonesia memiliki kasus stunting yang cukup tinggi. Pengimplementasian model Bayesian spasial Conditional Autoregressive (CAR) dalam menaksir risiko relatif (RR) kasus stunting belum dilakukan di Indonesia, khususnya di Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui RR kasus stunting dengan menggunakan model Bayesian spasial CAR Leroux serta membangun peta tematik RR kasus stunting di seluruh kabupaten/kota di Provinsi Sulawesi Selatan. Model Bayesian spasial CAR Leroux dengan hyperprior IG(0,5; 0,0005) merupakan model terbaik dalam pemodelan RR kasus balita stunting di Provinsi Sulawesi Selatan. Kabupaten Toraja, Kota Parepare, dan Kabupaten Enrekang merupakan tiga kabupaten/kota dengan RR stunting tertinggi. Sebaliknya, Kabupaten Gowa, Kota Makassar dan Kabupaten Pinrang merupakan tiga wilayah dengan RR stunting terendah.]]>
<![CDATA[THE INTERPLAY BETWEEN CLUSTERS, COVARIATES, AND SPATIAL PRIORS IN SPATIAL MODELLING OF COVID-19 IN SOUTH SULAWESI PROVINCE, INDONESIA ]]>
<![CDATA[Aswi Aswi]]>;
<![CDATA[Muhammad Arif Tiro]]>;
<![CDATA[Sudarmin Sudarmin]]>;
<![CDATA[Sukarna Sukarna]]>;
<![CDATA[Susanna Cramb]]>
<![CDATA[ MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.15.1.48-59
<![CDATA[A number of previous studies on Covid-19 have used Bayesian spatial Conditional Autoregressive (CAR) models. However, basic CAR models are at risk of over-smoothing if adjacent areas genuinely differ in risk. More complex forms, such as localised CAR models, allow for sudden disparities, but have rarely been applied to modelling Covid-19, and never with covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases with and without covariates, examine the impact of covariates and spatial priors on the identified clusters and which factors affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with population density included fits the data better than a corresponding model without covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively.]]>
<![CDATA[Pemetaan Risiko Relatif Kasus Stunting di Provinsi Sulawesi Selatan ]]>
<![CDATA[Aswi Aswi]]>;
<![CDATA[Sukarna Sukarna]]>;
<![CDATA[Nurhilaliyah Nurhilaliyah]]>
<![CDATA[ Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 11, No 1 (2022): Maret ]]>
Publisher : <![CDATA[Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar ]]>
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DOI: 10.35580/sainsmat111325202022
<![CDATA[Indonesia merupakan negara dengan prevalensi balita stunting tertinggi ketiga di regional Asia Tenggara. Provinsi Sulawesi Selatan sebagai salah satu provinsi di Indonesia memiliki kasus stunting yang cukup tinggi. Pengimplementasian model Bayesian spasial Conditional Autoregressive (CAR) dalam menaksir risiko relatif (RR) kasus stunting belum dilakukan di Indonesia, khususnya di Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui RR kasus stunting dengan menggunakan model Bayesian spasial CAR Leroux serta membangun peta tematik RR kasus stunting di seluruh kabupaten/kota di Provinsi Sulawesi Selatan. Model Bayesian spasial CAR Leroux dengan hyperprior IG(0,5; 0,0005) merupakan model terbaik dalam pemodelan RR kasus balita stunting di Provinsi Sulawesi Selatan. Kabupaten Toraja, Kota Parepare, dan Kabupaten Enrekang merupakan tiga kabupaten/kota dengan RR stunting tertinggi. Sebaliknya, Kabupaten Gowa, Kota Makassar dan Kabupaten Pinrang merupakan tiga wilayah dengan RR stunting terendah.]]>
