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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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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.
Statistika Kategorik untuk Siswa: Meningkatkan Ketajaman Analisis dalam Karya Tulis Ilmiah Aswi, Aswi; Tiro, Muhammad Arif; Poerwanto, Bobby; Ikhwana, Nur; Rais, Zulkifli; Abidin, Muh. Zulkifli
SMART: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2025): Oktober
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/smart.v5i2.77214

Abstract

Tujuan dari kegiatan ini adalah untuk meningkatkan kamampuan analisis data guru dan siswa SMAN 7 Takalar khususnya dalam mengolah dan menganalisis data kualitatif atau kategorik dalam menyusun karya tulis ilmiah. Kegiatan ini diikuti oleh 18 orang siswa. Pelaksanaan kegiatan ini dimulai dari observasi, identifikasi kebutuhan, pelatihan, pendampingan, serta monitoring dan evaluasi. Hasil dari kegiatan ini adalah peningkatan pengetahuan dan keterampilan pada topik yang dibahas. Selain itu, sekitar 83,33% peserta merasakan pengetahuan dan keterampilannya meningkat secara signifikan. Artinya kegiatan yang dilakukan memberikan dampak kepada peserta sehingga setelah narasumber meninggalkan lokasi kegiatan terjadi sharing ilmu antar peserta sehingga peserta yang belum banyak berkembang juga dapat memahami dan mengimplementasikan materi yang telah diberikan. Peningkatan keterampilan ini diharapkan dapat membantu siswa dalam penyusunan karya tulis ilmiah.
Pendekatan Regresi Nonparametrik Spline Truncated untuk Mengidentifikasi Determinan Angka Kematian Ibu di Indonesia Hidayat, Rahmat; Annas, Suwardi; Aswi, Aswi; Putri, Siti Choiratun Aisyah; Vivianti, Vivianti
Indonesian Journal of Fundamental Sciences Vol 11, No 2 (2025)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/ijfs.v11i2.77643

Abstract

Kualitas kesehatan maternal di suatu negara umumnya diukur melalui indikator utama berupa Angka Kematian Ibu (AKI). Penelitian ini menganalisis pengaruh tiga faktor penting terhadap AKI di Indonesia, yaitu persentase perempuan usia 15–49 tahun yang pernah menikah dan memiliki anak hidup, persentase rumah tangga dengan akses sanitasi layak, serta rata-rata lama sekolah. Untuk mengidentifikasi pola hubungan nonlinier antara variabel-variabel tersebut yang tidak dapat dijelaskan secara optimal oleh model regresi parametrik, digunakan pendekatan regresi nonparametrik Spline Truncated. Model ini mampu menangani data dengan pola acak. Hasil estimasi menunjukkan bahwa model terbaik diperoleh dengan nilai Generalized Cross Validation (GCV) minimum sebesar 1,023 dan koefisien determinasi (R²) sebesar 0,9012. Temuan ini mengindikasikan bahwa ketiga variabel prediktor berpengaruh signifikan terhadap AKI dengan bentuk hubungan yang tidak sepenuhnya linier. Hasil penelitian diharapkan dapat menjadi dasar dalam perumusan kebijakan kesehatan yang lebih efektif dan berbasis data untuk menekan angka kematian ibu di Indonesia
Evaluasi Performa Model Regresi Poisson Tweedie dan Conway Maxwell Poisson dalam Menangani Masalah Dispersi: Studi Angka Kematian Ibu di Provinsi Sulawesi Selatan Aswi, Aswi; Sanusi, Wahidah; Tiro, Muhammad Arif; Sukarna, Sukarna; Haekal, Muh. Fahri; Palarungi, Andi Gagah; Putri, Siti Choirotun Aisyah; Oktaviana, Oktaviana
Indonesian Journal of Fundamental Sciences Vol 11, No 2 (2025)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/ijfs.v11i2.77506

Abstract

Model regresi Poisson digunakan untuk menganalisis hubungan antara satu atau lebih variabel independen dengan variabel dependen berupa data cacahan. Salah satu asumsi utamanya adalah kesamaan antara nilai mean dan variansi (equidispersi). Namun, dalam praktiknya, asumsi tersebut sering tidak terpenuhi. Kondisi ini menyebabkan model regresi Poisson kurang sesuai digunakan, karena dapat menghasilkan estimasi standar error yang terlalu kecil (underestimate). Model alternatif yang dapat digunakan untuk mengatasi masalah overdispersi adalah Regresi Poisson Tweedie dan Conway Maxwell Poisson (CMP). Penelitian ini bertujuan untuk mengevaluasi kinerja model regresi Poisson Tweedie dan regresi CMP dalam menangani masalah dispersi pada data Angka Kematian Ibu (AKI) di Provinsi Sulawesi Selatan, Indonesia. Estimasi parameter dilakukan dengan metode Estimasi Kemungkinan Maksimum (MLE), sedangkan kinerja model dinilai berdasarkan Akaike Information Criterion (AIC), Mean Square Error (MSE), dan signifikansi parameter. Hasil penelitian menunjukkan bahwa model regresi Poisson standar kurang sesuai karena adanya pelanggaran asumsi ekuidispersi. Sebaliknya, model CMP dan Poisson Tweedie memberikan alternatif yang lebih tepat, dimana Model CMP menunjukkan akurasi prediktif yang lebih tinggi dengan nilai MSE terendah. Faktor perdarahan, hipertensi, gangguan kardiovaskular, dan komplikasi pasca-aborsi ditemukan memiliki pengaruh yang signifikan terhadap kematian ibu, sementara infeksi tidak signifikan secara statistik. 
The Application of the K-Medoid Classification Method for Analyzing Crime Rates in South Sulawesi Annas, Suwardi; Aswi, Aswi; Irwan, Irwan
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.21464

Abstract

This research employs the k-medoid clustering method to analyze districts and cities in South Sulawesi based on their crime rates. As the population grows, employment opportunities tend to diminish, which can increase stress levels and, consequently, the likelihood of criminal behavior. To evaluate the distribution of criminal incidents across South Sulawesi, the k-medoid method is used to cluster regions. Unlike other clustering methods, k-medoid utilizes the median as the cluster center (medoid), which enhances its robustness against outliers. Specifically, the Partitioning Around Medoids (PAM) algorithm is applied, where initial objects are randomly selected to represent clusters. If the error value is high, the cluster centers are adjusted until the error is minimized. The dataset comprises crime incidence data for South Sulawesi in 2020, focusing on various types of crime. The analysis identified an optimal number of three clusters based on the Silhouette coefficient. Cluster 1 includes 11 regions, Cluster 2 consists of 8 regions, and Cluster 3 contains 5 regions. These clusters provide a comprehensive overview of the crime conditions across different regions within each cluster.
Bayesian Spatio-Temporal Conditional Autoregressive Modelling of Factors Affecting Pneumonia Cases in Indonesia Risma Mastory; Aswi, Aswi; Muhammad Fahmuddin; Lalu Ramzy Rahmanda
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.56315

Abstract

The Bayesian Spatio-Temporal Conditional Autoregressive (BST CAR) method is a statistical approach used to analyze data with both spatial and temporal components. While the BST CAR model has been widely applied in various studies, no research has yet explored using the Localized BST CAR model for pneumonia cases in Indonesia. This study aims to identify and model the factors influencing pneumonia incidence in Indonesia using the Localized BST CAR framework. The data analyzed in this study consist of the number of pneumonia cases in Indonesia from 2018 to 2022, along with variables believed to affect the incidence. The findings indicate that the Localized BST CAR model with G=3 provides the best fit for modeling the relative risk of pneumonia cases in Indonesia. Key factors found to significantly influence pneumonia cases include the percentage of exclusively breastfed infants, the percentage of infants with complete basic immunization, and the percentage of the population living in poverty. Notably, the percentage of exclusively breastfed infants and the percentage of fully immunized infants were positively associated with pneumonia cases, while the percentage of the poor population had a negative effect
Estimating the Relative Risk of Dengue Hemorrhagic Fever in Makassar City Using a Bayesian Spatial Localised Conditional Autoregressive Model Rahmawati; Aswi Aswi; Rahmat Hidayat; Andi Gagah Palarungi Taufik
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/z0mxxw06

Abstract

Dengue Hemorrhagic Fever (DHF) remains a significant public health challenge in Indonesia, including in Makassar City, which reported an increase of 291 cases in 2024. This study aimed to estimate the relative risk of DHF across 15 districts of Makassar by incorporating covariates such as population density, distance to the city center, and the number of hospitals, using a Bayesian Conditional Autoregressive (CAR) Localised approach. The data were obtained from the publication Makassar City in Figures 2025, issued by the Central Statistics Agency. Spatial autocorrelation analysis with Moran’s I indicated significant clustering of DHF cases. Model selection was conducted using the Deviance Information Criterion (DIC), Watanabe–Akaike Information Criterion (WAIC), and group-level area coverage. The results showed that the best-fitting model was the CAR Localised model with distance as a covariate (M9), specified at G = 3 with hyperprior IG (1; 0.01). Distance exhibited a negative association with DHF incidence, suggesting that the farther a district is from the city center, the lower its relative risk. Among the districts, Rappocini exhibited the highest relative risk followed by Panakkukang, while the lowest risks were observed in Sangkarrang Islands. These findings provide valuable insights for designing spatially targeted DHF prevention and control strategies in Makassar City.
Tourism Forecasting Using Chen and Singh Fuzzy Time Series Models Vivianti Vivianti; Aswi Aswi
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/w2554664

Abstract

The tourism sector is one of the main drivers of the national economy, which experienced a significant decline due to the COVID-19 pandemic. In the post-pandemic era, the recovery of international tourist arrivals shows a positive trend, thus requiring accurate forecasting methods to support tourism policy planning. ARIMA method are less effective in handling nonlinear and fluctuating data. This study applies the Fuzzy Time Series (FTS) approach, specifically the Chen and Singh models, which are capable of managing data uncertainty and representing linguistic patterns adaptively. The purpose of this study is to compare the accuracy of both models using two interval determination approaches, namely the Sturges method and the mean-based method, in forecasting international tourist arrivals through Sultan Hasanuddin International Airport during the period from January 2023 to September 2025. The analytical steps include defining the universe of discourse, performing fuzzification, constructing fuzzy logical relationships (FLR) and fuzzy logical relationship groups (FLRG), and applying defuzzification to obtain forecasted values. The forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that the choice of interval determination method significantly affects forecasting performance, with the mean-based method producing more detailed and accurate intervals. Based on the evaluation, the FTS Singh model demonstrated the best performance, with MAPE of 2.16% and RMSE of 31.05, outperforming the Chen model under both interval approaches. Therefore, the combination of the FTS Singh model with the mean-based interval method is recommended as the optimal approach for forecasting post-pandemic international tourist arrivals, as it can capture fluctuating data patterns more precisely and consistently.
A Systematic Simulation Study of Semiparametric Spline Estimators for Nonlinear Data Structures in R Rahmat Hidayat; Aswi Aswi; Zakiyah Mar'ah
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/7k3vqe29

Abstract

In many real-world applications, the assumption of linearity in classical regression models is often violated, leading to model misspecification and inaccurate estimation when data exhibit complex nonlinear patterns. Although nonparametric approaches provide flexibility, they frequently suffer from poor interpretability and instability in high-dimensional settings. To address these limitations, this study examines the implementation of semiparametric spline regression as a flexible yet interpretable alternative. The model integrates a linear component for certain predictors and a spline-based nonparametric component to capture local data fluctuations. Through a simulation study using the R programming language, the performance of the spline estimator was evaluated based on the Generalized Cross Validation (GCV) criterion for optimal knot selection. The results demonstrate that the semiparametric spline model achieves superior accuracy, with a coefficient of determination (R²) reaching 97.35%, compared to 81.18% for the classical linear model. In addition, the Mean Square Error (MSE) is significantly reduced from 2.158 to 0.303. Residual diagnostic analysis confirms that the model satisfies normality and homoscedasticity assumptions. These findings highlight the effectiveness of spline-based semiparametric regression in modeling complex nonlinear data structures.
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