Article Per Year (5 Year)
p-Index From 2020 - 2025
6.623
P-Index
This Author published in this journals
All Journal Media Statistika SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Ekonomi Pembangunan STIE Muhammadiyah Palopo 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
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 48 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 48 Documents
Search

 1   2   3   4   5 
Penerapan Metode Generalized Space Time Autoregressive (GSTAR) Untuk Memprediksi Sebaran Covid-19 di Pulau Jawa Wea, Maria Dominggo; aswi; Aidid, Muhammad Kasim
Journal of Mathematics, Computations and Statistics Vol. 7 No. 1 (2024): Volume 07 Nomor 01 (April 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i1.1944

Abstract

Virus Corona merupakan keluarga besar virus (antara hewan dan manusia) yang ditularkan secara zoonik dan menimbulkan gejala ringan hingga berat. Kasus COVID-19 terus bertambah secara global dan berfluktuasi, termasuk di Indonesia dan sebaran kasus infeksi virus terbesar di Pulau Jawa. Penelitian ini bertujuan untuk memprediksi sebaran kasus COVID-19 di Pulau Jawa dengan data spasial yaitu 6 provinsi sehingga menggunakan model ruang waktu yaitu Model Generalized Space Time Autoregressive (GSTAR). Matriks pembobot yang digunakan adalah invers jarak dan queen contiguity. Tahapan pembentukan model terdiri dari uji stasioneritas data, penentuan urutan ruang-waktu, penggunaan kedua matriks pembobot, estimasi parameter dengan Ordinary Least Square, uji kesesuaian model dengan uji asumsi white noise dan uji asumsi normalitas, perhitungan Root Mean Square Error (RMSE), pemilihan model terbaik dengan matriks pembobotan queen contiguity yang menghasilkan nilai RMSE terkecil, peramalan berdasarkan model GSTAR terbaik, dan pemetaan total hasil peramalan. Model GSTAR terbaik yang dihasilkan adalah GSTAR (1;1) I(1) dengan menggunakan matriks pembobotan queen contiguity dengan nilai RMSE terkecil. Hasil peramalan dan pemetaan berdasarkan hasil ramalan total kasus COVID-19 di Pulau Jawa pada tanggal 1 April 2022 – 7 April 2022 menunjukkan bahwa hampir semua provinsi mendekati nilai pada data aktual dilihat dari plot dan peta sebaran.
The Impact of Covid-19 on Stunting Cases in Indonesia: A Bayesian Spatial Modeling Approach Ankaz As Sikib; Aswi, Aswi; Ruliana
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.3065

Abstract

The high number of COVID-19 cases has impacted various sectors. One of the notable consequences of the COVID-19 pandemic is its effect on food security and nutrition. Social restrictions implemented to curb the spread of the virus have resulted in worsening economic conditions, limited access to healthcare facilities, difficulties in obtaining nutritious food, and school closures. Changes in the routines and activities of COVID-19 patients may contribute to an increase in the prevalence of stunting in Indonesia. While research has been conducted on the impact of COVID-19 on the rise of stunting cases in Indonesia, previous studies have typically focused on individual provinces and have not utilized the Bayesian Conditional Autoregressive (CAR) model. This study aims to investigate the relationship between COVID-19 and the increase in stunting cases across Indonesia. We analyze data on stunting cases in each Indonesian province and the number of COVID-19 patients between March 23, 2020, and December 31, 2021. To assess the relationship, we employ the Bayesian spatial CAR Leroux model with several Inverse-Gamma hyperpriors. We compare these models using various fit criteria. The results indicate that the Bayesian spatial CAR Leroux model with Inverse-Gamma hyperpriors (0.1;0.1) performs best, as it yields the smallest Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) values. In conclusion, our analysis reveals a positive correlation between the number of COVID-19 cases and the increase in stunting cases in Indonesia. Approximately 50% of the regions in Indonesia face a high relative risk of stunting, with Nusa Tenggara Timur having the highest relative risk, followed by Kalimantan Barat and Sulawesi Barat
Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Sulawesi Selatan Menggunakan Model ARFIMA Sukarna; Abdy, Muhammad; Aswi; Kaito, Nurlaila
Journal of Mathematics, Computations and Statistics Vol. 5 No. 2 (2022): Volume 05 Nomor 02 (Oktober 2022)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tourism is a potential and strategic asset to encourage the development of a region, especially for areas that have potential tourist objects. Exchange rates, inflation, and geography influence foreign tourist visits to an area. What may be unexpected is the increase in the number of tourists, which makes tourist workers have difficulties in providing the best services, and vice versa if there is a sudden drop, it will increase the number of unemployed. Therefore, we need a scientific study of forecasting that can provide information on the number of tourists. The ARFIMA model is an ARIMA whose differencing value is a fraction. The main goal of this research is to discover the best ARFIMA model to predict the number of foreign tourist arrivals in South Sulawesi. From the data of foreign tourists in South Sulawesi from 2015 to 2020, the result of this research is the AIC value of 710.44 for ARFIMA([1,8],d,0) with. The average difference between the actual and forecasted data in the out sample data for the two models is 38.6667 points. Therefore, the two models can still be classified as the best for forecasting foreign tourists from South Sulawesi. It depends on who applied this models into this cases.
Pemodelan Spasial Bayesian dalam Menentukan Faktor yang Mempengaruhi Kejadian Stunting di Provinsi Sulawesi Selatan Aswi, Aswi; Sukarna, Sukarna
Journal of Mathematics, Computations and Statistics Vol. 5 No. 1 (2022): Volume 05 Nomor 01 (April 2022)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia is a country with a high prevalence of stunting. One of the provinces in Indonesia that has a fairly high number of stunting cases is South Sulawesi Province. Research on stunting cases and their causes has been done. However, these researches have not implemented the Bayesian Spatial Conditional Autoregressive (CAR) model. This study aims to determine the factors that influence the incidence of stunting in South Sulawesi Province by implementing various Bayesian spatial CAR Leroux models with and without covariates included in the model. The results showed that the best model for modeling stunting cases in South Sulawesi Province in 2020 is the Bayesian spatial CAR Leroux model with hyperprior Inverse-Gamma IG (0.5;0.0005) by including the covariates of the percentage of poverty and the percentage of children under five 0-59 months of malnutrition. The percentage of poverty and the percentage of children under five 0-59 months of malnutrition have a positive effect on the incidence of stunting. The higher the percentage of poverty and the percentage of children aged 0-59 months with malnutrition in an area, the higher the risk of stunting in that area. 50% of districts/cities in South Sulawesi Province are in the high-risk category of stunting. Parepare City is the city with the highest Relative Risk (RR) value for stunting, followed by Toraja and Enrekang Regencies. On the other hand, Wajo Regency is the district with the lowest RR, followed by Luwu Timur and Bone Regencies.
Pemetaan Kasus Tuberkulosis di Provinsi Sulawesi Selatan Tahun 2020 Menggunakan Model Bayesian Spasial BYM dan Leroux Aswi, Aswi; Sukarna, Sukarna; Nurhilaliyah, Nurhilaliyah
Journal of Mathematics, Computations and Statistics Vol. 4 No. 2 (2021): Volume 04 Nomor 02 (Oktober 2021)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tuberculosis (TB) is an infectious disease that is one of the ten leading causes of death in the world. Indonesia is a country with the second-highest number of TB sufferers in the world. This study aims to identify areas with a high and low relative risk (RR) of TB by using the Bayesian Spatial Conditional Autoregressive (CAR) Besag-York-Molliѐ (BYM) and Leroux models. TB case data in every 24 districts/cities in South Sulawesi province in 2020 is used. The best model was selected based on three criteria, namely Deviance Information Criteria (DIC) and Watanabe Akaike Information Criteria (WAIC). The results show that the Bayesian Spatial CAR BYM and CAR Leroux with hyperprior IG (0.5; 0.0005) are the best models that have the same RR value. Makassar City is the area with the highest RR value (1.70) which indicates that Makassar City has a TB risk 70% higher than the average. On the other hand, the Toraja district has the lowest TB risk (0.43) which indicates that Toraja has a TB risk 43% lower than the average.
ANALISIS REGRESI SPASIAL PENDEKATAN AREA INDEKS PEMBANGUNAN PEMUDA TAHUN 2022 DI INDONESIA Imam Akbar Muttaqin; Aswi Aswi; Muhammad Fahmuddin S
Jurnal Ekonomi Pembangunan STIE Muhammadiyah Palopo Vol 10, No 2 (2024)
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah (LPPI) Universitas Muhammadiyah Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35906/jep.v10i2.2278

Abstract

ABSTRAK Indeks Pembangunan Pemuda menjelaskan tentang bagaimana kemajuan kualitas pemuda pada suatu daerah dengan menggunakan 15 indikator penyusun. Capaian angka IPP di Indonesia dalam 5 tahun terakhir menunjukkan fluktuasi yang mencerminkan kualitas pemuda yang belum konsisten mengalami peningkatan. Penelitian ini bertujuan untuk mengetahui model terbaik regresi spasial dengan pendekatan area pada Indeks Pembangunan Pemuda tahun 2022 di Indonesia dan mengetahui indikator yang berpengaruh secara signifikan pada Indeks Pembangunan Pemuda tahun 2022 di Indonesia. Metode analisis yang digunakan pada penelitian ini adalah analisis regresi spasial. Data yang digunakan adalah data sekunder. Hasil penelitian menunjukkan bahwa model regresi terbaik adalah Spatial Error Model (SEM) dengan indikator yang signifikan memiliki pengaruh adalah rata-rata lama sekolah pemuda memberi pengaruh penurunan angka IPP sebesar 0.339, angka partisipasi kasar sekolah menengah memberi pengaruh peningkatan angka IPP sebesar 0.274, angka partisipasi kasar perguruan tinggi memberi pengaruh peningkatan angka IPP sebesar 0.870, dan angka kesakitan pemuda memberi pengaruh penurunan angka IPP sebesar 0.223.Kata Kunci: Regresi Spasial; Spatial Error Model; Indeks Pembangunan Pemuda ABSTRACT The Youth Development Index explains how the quality of youth in a region is progressing using 15 constituent indicators. The achievement of IPP figures in Indonesia in the last 5 years shows fluctuations which reflect the quality of youth which has not consistently improved. This study aims to determine the best spatial regression model with an area approach on the 2022 Youth Development Index in Indonesia and to determine the indicators that have a significant influence on the 2022 Youth Development Index in Indonesia. The analytical method used in this research is spatial regression analysis. The data used is secondary data. The results of the research show that the best regression model is the Spatial Error Model (SEM) with indicators that have a significant influence are the average length of youth schooling had the effect of decreasing the IPP figure by 0.339, the gross secondary school enrollment rate had the effect of increasing the IPP figure by 0.274, the gross college enrollment rate had the effect of increasing the IPP figure by  0.870, and the youth morbidity rate had the effect of decreasing the IPP figure by 0.223.Keywords: Spatial Regression; Spatial Error Model; Youth Development Index
Binary Logistic Regression Model of Stroke Patients: A Case Study of Stroke Centre Hospital in Makassar Suwardi Annas; Aswi Aswi; Muhammad Abdy; Bobby Poerwanto
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p161-169

Abstract

This paper aimed to determine factors that affect significantly types of stroke for stroke patients in Dadi Stroke Center Hospital. The binary logistic regression model was used to analyze the association between the types of stroke and some covariates namely age, sex, total cholesterol, blood sugar level, and history of diseases (hypertension/stroke/diabetes mellitus). Maximum Likelihood Estimation was used to estimate parameters. Combinations of covariates were compared using goodness-of-fit measures. Comparisons were made in the context of a case study, namely stroke patients (2017-2020). The results showed that a binary logistic model combining the history of diseases and blood sugar level provided the most suitable model as it has the smallest AIC and covariates included are statistically significant. The coefficient estimation of the history of diseases variable is -0.92402 with an odds ratio value exp(-0.92402)=0.4. This means that stroke patients who have a history of diseases experience a reduction of 60% in the odds of having a hemorrhagic stroke compared to stroke patients that do not have a history of diseases. In other words, stroke patients who have a history of diseases tend to have a non-hemorrhagic stroke. Furthermore, the coefficient estimation of blood sugar level is 0.74395 with an odds ratio value exp(0.74395)=2. It means that stroke patients who do not have normal blood sugar levels tend to have a hemorrhagic stroke 2 times greater than stroke patients with normal blood sugar levels. A history of diseases and blood sugar level were factors that significantly affect the types of stroke.
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.
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 Awi Dassa Bobby Poerwanto Bobby Poerwanto Bobby Poerwanto Bustan, Muhammad Nadjib Halimah Husain Hammado, Nurussyariah Hisyam Ihsan Idul Fitri Abdullah Imam Akbar Muttaqin Ina Rahma 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 Muhammad Fahmuddin Sudding Muhammad Kasim Aidid Mutmainnah Mutmainnah 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 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 Wahidah Sanusi Wea, Maria Dominggo Yassar, La Ode Salman Zulhijrah Zulhijrah Zulhijrah Zulkifli Rais