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All Journal Media Statistika SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Ekonomi Pembangunan STIE Muhammadiyah Palopo Jurnal Matematika Sains dan Teknologi Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) MATAPPA: Jurnal Pengabdian Kepada Masyarakat M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Review Pendidikan dan Pengajaran (JRPP) 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 Jurnal Statistika dan Aplikasinya ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat 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 Indonesian Educational Administration and Leadership Journal (IDEAL) SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Aswi, Aswi
Department Of Statistics, Universitas Negeri Makassar
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Journal : sainsmat

 1 
Pemetaan Risiko Relatif Kasus Stunting di Provinsi Sulawesi Selatan Aswi Aswi; Sukarna Sukarna; Nurhilaliyah Nurhilaliyah
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 11, No 1 (2022): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat111325202022

Abstract

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.
Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Modeling in Forecasting Covid-19 Cases in Indonesia Rahmawati Rahmawati; Suwardi Annas; Aswi Aswi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 12, No 1 (2023): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat121442822023

Abstract

Covid-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The spread of Covid-19 in Indonesia has grown rapidly that the World Health Organization (WHO) has declared Covid-19 a pandemic. Covid-19 cases have spread to 34 provinces in Indonesia. Covid-19 data in Indonesia involves space and time so the appropriate modeling is the space-time model. Space-time modeling of the Covid-19 case in 34 provinces in Indonesia using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model has not been carried out. The purpose of this research is to get the best GSTARIMA model and forecat the Covid-19 case for the next several times. This model incorporates time and location interdependence with different parameters for each location. Identification of the order of the AR and MA was carried out through the STACF and STPACF plots. For simplicity of interpretation, the spatial order is chosen first order. In this study, the queen contiguity and the inverse distance location weighting matrix were used. The parameter estimation used is Ordinary Least Square (OLS). The results show that the best model for predicting Covid-19 cases in 34 provinces in Indonesia is the GSTARIMA model (1,1,0)1 using an inverse distance weighting matrix with the smallest RMSE value of 1.22.Keywords: Covid-19, GSTARIMA, Queen Contiguity, Inverse Distance, OLS.
Modeling Factors Influencing Covid-19 Cases in South Sulawesi Using Bayesian Conditional Autoregressive Localised Yassar, La Ode Salman; Shanty, Meyrna Vidya; Mahadtir, Muhamad; Aswi, Aswi; Annas, Suwardi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 13, No 1 (2024): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat131606362024

Abstract

South Sulawesi Province is listed as the province with the highest number of Covid-19 cases in the Sulawes island. Research on Covid-19 modeling has been carried out by many researchers, but until now, there has been no research using the Bayesian spatial Conditional Autoregressive Localized model which involves a combination of factors such as distance to the provincial capital, population density, and the number of elderly people in each district in South Sulawesi Province. The aim of this research is to get the best Bayesian Conditional Autoregressive Localized model. The best model is based on four criteria, namely: Deviance Information Criteria, Watanabe Akaike Information Criteria, residuals from Modified Moran's I, and the number of areas included in a group. It was found that model with G=3 by including population density covariates was the best model. A significant factor influencing the increase in Covid-19 cases is the population density factor which has a positive effect. This shows that the more densely populated an area is, the greater the chance of being infected with Covid-19. Makassar has the highest relative risk value for Covid-19 followed by Toraja district and Pare-Pare City. Meanwhile, Bone district has the lowest relative risk value for Covid-19, followed by Wajo district and Enrekang district.
Statistical Modeling and Factors Influencing School Dropout in Indonesia: A Review Shanty, Meyrna Vidya; Mahadtir, Muhamad; Awaluddin, Awaluddin; Natalia, Derliani; Ramadani, Reski Aulia; Aswi, Aswi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 13, No 1 (2024): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat131608032024

Abstract

The education enrollment rate is crucial for Indonesia to improve its human resources and sustain its economic development. In reality, the dropout student rate is still relatively high. Previous research has highlighted several factors and models related to the dropout student rate in Indonesia. The purpose of the study is to identify the most popular statistical modeling and factors influencing school dropout in Indonesia. We searched in February 2023 using ScienceDirect, ProQuest, and Google Scholar. The search was restricted to refereed journal articles published in English from January 2013 to December 2022. This study underwent four stages: identification, screening, eligibility, and inclusion. The study finds that the most popular statistical modeling is the Logistic Regression Model, and the most significant factor increasing the school dropout rate in Indonesia is family and economic factors. The findings suggest that children who were not attending school came from families with lower levels of education. The well-being of these families was directly linked to their children's educational status. The primary reasons for young students dropping out of elementary and junior schools include an inability to pay school fees and a desire to work on farms to support their parents.
Intervention Analysis In Time Series Data For Forecasting Bbri Stock Prices Mangkona, Andi Ilham Azhar; Aswi, Aswi; Ruliana, Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141670452025

Abstract

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are typically divided into two types: the step function and the pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis concluded that the best-fitting model was ARIMA (2, 1, 0), with the intervention order characterized by a step function where b = 0, s = 2, and r = 0. The accuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.
Comparison Of Bayesian Spatial Car Models For Estimating The Risk Of Diarrhea Cases In Makassar City Bakri, Nurul Aulya; Yudi, Wanda; Aswi, Aswi; Hidayat, Rahmat
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142774752025

Abstract

Diarrhea continues to pose a significant public health challenge in Makassar City, with incidence varying across sub-districts. Mapping diarrhea risk is essential for public health planning, as it helps identify high-risk areas and allocate resources efficiently. Accurate spatial risk assessment supports targeted interventions and informs evidence-based health policies. This study aimed to identify areas with high and low relative risks (RR) of diarrhea cases using Bayesian spatial Conditional Autoregressive (CAR) models, specifically the Besag–York–Mollié (BYM) and Leroux approaches. The analysis was based on case data from 15 sub-districts in Makassar City in 2023. Model performance was assessed using the Deviance Information Criterion (DIC) and the Watanabe–Akaike Information Criterion (WAIC). The CAR-Leroux model with an Inverse Gamma (IG) hyperprior (0.5; 0.0005) was identified as the best-fitting model, providing the most reliable estimation of relative risk. Kepulauan Sangkarrang exhibited the highest RR, indicating a markedly elevated risk of diarrhea relative to the city average, while Biringkanaya District showed the lowest RR, reflecting a substantially lower risk compared to the average.Keywords: Bayesian spasial models, CAR BYM, CAR Leroux, Diarrhea, Relative risk.
Bayesian Spatio Temporal Car Localized Model For Mapping The Relative Risk Of AIDS In South Sulawesi Province Taufik, Andi Gagah Palarungi; Aswi, Aswi; Annas, Suwarni
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142776802025

Abstract

Acquired Immune Deficiency Syndrome (AIDS) remains a major public health issue in Indonesia, with South Sulawesi showing a marked rise in cases from 2022 to 2024. This study aims to estimate and visualize the relative risk of AIDS across 24 districts and municipalities in the province by incorporating population density as a spatial covariate. Data were obtained from the Central Bureau of Statistics (BPS) and the South Sulawesi Provincial Health Office. A Bayesian Localised Conditional Autoregressive (CAR) spatio-temporal framework was applied to account for both spatial dependence and temporal variation. Model selection was guided by the Deviance Information Criterion (DIC) and the Watanabe–Akaike Information Criterion (WAIC), with the best-fitting model identified at G = 3 using an Inverse-Gamma (1; 0.01) prior. The analysis revealed that population density had a significant positive association with AIDS incidence. Areas with higher density exhibited elevated relative risk values, particularly Makassar City (RR = 1.95) and Gowa Regency (RR = 1.82), whereas the lowest risks were found in Selayar (RR = 0.41) and East Luwu (RR = 0.45). These findings indicate distinct spatial clustering patterns and underscore the need for geographically focused intervention policies.
Co-Authors A. Nurul Amalia AA Sudharmawan, AA Abdul Rahman Abdul Rahmat Abidin, Muh. Zulkifli Abidin, Muhammad Rais Ahmar, Ansari Saleh Aidid, Muhammad Kasim Aisyah Putri , Siti Choirotun Ambo Upe Andi Feriansyah Andi Feriansyah Andi Gagah Palarungi Taufik Andi Gagah Palarungi Taufik Andi Muhammad Ridho Yusuf Sainon Andin P Andi Shahifah Muthahharah Ankaz As Sikib Annas, Suwardi Annas, Suwardi Annas, Suwardi Annas, Suwarni Aprilia Wardani Syam , Dewi Arismunandar Arismunandar Asrirawan Assagaf, Said Fachry Awi Awi Awi Dassa, Awi Awi, Awi Bakri, Nurul Aulya Besse Sulfiani Bobby Poerwanto Bobby Poerwanto Bobby Poerwanto Bustan, Muhammad Nadjib Cramb, Susanna Diana Eka Pratiwi Fahmuddin, Muhammad Fahmuddin, Muhammad Fajar Arwadi Folorunso, Serifat Adedamola Haekal, Muh. Fahri Halimah Husain Hammado, Nurussyariah Herman, Nur Taj Alya’ Hidayat , Rahmat Hisyam Ihsan Idul Fitri Abdullah Ikhwana, Nur Irwan Irwan Irwan, Irwan Ishma Azizah S Isnaini, Mardatunnisa Isnaini, Wulan Maulia Kaito, Nurlaila Lalu Ramzy Rahmanda M Nadjib Bustan M. Miftach Fakhri Mahadtir, Muhamad Mangkona, Andi Ilham Azhar Mar'ah, Zakiyah 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 Muttaqin, Imam Akbar Natalia, Derliani Nini Harnikayani Hasa Novianti, Andi Rima Nunung Marlika Nur Aziza S Nurhikmawati, Nurhikmawati Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah, Nurhilaliyah Nurkaila Kaito Nurlia Nurlia Nurul Fadilah Syahrul Nurul Ilmi Nusrang, Muhammad Oktaviana Oktaviana Oktaviana Oktaviana Palarungi, Andi Gagah Panessai Sir Poerwanto, Bobby Poerwanto, Bobby Poewanto, Bobby Putri Ananda, Elma Yulia Putri, Siti Choiratun Aisyah Putri, Siti Choirotun Aisyah Rahma, Ina Rahman, Abdul Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmawati Rahmawati Rahmawati Ramadani, Reski Aulia Rezki Amalia Idrus Rika Saniarti Riska Saputri Risma Mastory Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana, Ruliana S, Muhammad Fahmuddin Sahlan Sidjara Saleh, Andi Rahmat Salsabila, Afifah Sapriani Shanty, Meyrna Vidya Siti Choirotun Aisyah Putri Sitti Aminah Sri Ayu Astuti Sri Rahayu Stevani Stevani Suardi, Shafira Suci Amaliah Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna, Sukarna Sulistiawaty Sulistiawaty, Sulistiawaty Sumarni Sumarni Supriadi Yusuf Susanna Cramb Suwardi Annas Suwardi Annas Syafruddin Side Syamsiar, Syamsiar Taufik, Andi Gagah Palarungi Vivianti Vivianti Vivianti Wahidah Sanusi Wea, Maria Dominggo Yassar, La Ode Salman Yudi, Wanda Yunus, Sitti Rahma Zakiah, Andi Sitti Zakiyah Mar'ah Zulhijrah Zulhijrah Zulhijrah Zulhijrah Zulhijrah Zulkifli Rais