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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 Leroux CAR Models with Poisson and Binomial Likelihoods for Modeling Stunting Cases Rika Saniarti; Aswi Aswi
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/nykfnx57

Abstract

Stunting remains a chronic nutritional problem requiring precise spatial mapping to support effective policy interventions in East Java, Indonesia. Spatial disease mapping commonly applies the Poisson distribution with Conditional Autoregressive (CAR) effects; however, the Poisson distribution is sensitive to overdispersion. Alternatively, the Bayesian Spatial CAR model with a Binomial likelihood may offer a better framework, yet empirical comparisons remain limited. This study compares the performance of Bayesian Spatial Leroux CAR models with Poisson and Binomial likelihoods in modeling stunting cases and identifies associated factors. The data include stunting cases across 38 districts in East Java (2024) and predictors: low birth weight (LBW), prematurity, exclusive breastfeeding, complete basic immunization (CBI), pneumonia, and diarrhea. Performance was evaluated using the Deviance Information Criterion (DIC) and the Watanabe–Akaike Information Criterion (WAIC). Results indicate significant spatial dependence. The Bayesian Spatial Leroux CAR model with a binomial likelihood outperforms the Poisson-based model. LBW, exclusive breastfeeding, CBI, pneumonia, and diarrhea are significantly associated with stunting. Kediri Regency exhibits the highest relative risk (RR), followed by Probolinggo Regency and Batu City, while Kediri City and Ponorogo Regency show the lowest RR
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 : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

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.
Penerapan Algoritma Naive Bayes untuk Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Nunung Marlika; Aswi; Suwardi Annas
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm418

Abstract

Salah satu metode klasifikasi yang umum digunakan untuk menentukan kelayakan penerima bantuan Program Keluarga Harapan (PKH) adalah Algoritma Naive Bayes yang sering disebut juga Naive Bayes Classifier. Metode ini adalah probabilitas untuk mengklasifikasikan data secara cepat dan efisien untuk analisis kelayakan dalam program bantuan sosial. Naive Bayes adalah klasifikasi yang menggunakan pendekatan probabilitas dan statistik untuk mengelompokkan data. Pada penelitian ini, dilakukan penerapan algoritma Naive Bayes dalam mengklasifikasikan penerima bantuan Program Keluarga Harapan serta mengetahui tingkat akurasi, recall dan presisi dari metode Naive Bayes. Hasil dari penelitian ini adalah nilai akurasi yang dihasilkan dari metode Naive Bayes sebesar 90% pada pembagian data training dan testing 60%:40%, akurasi nilai 93% pada pembagian data training dan testing 70%:30%, serta nilai akurasi 90% pada pembagian data training dan testing 80%:20%.
Application of the Mixed Geographically Weighted Regression Model to Identify Influencing Factors for Literacy Development Index of Indonesian Society's in 2022 Zulhijrah Zulhijrah; Ruliana Ruliana; Aswi Aswi
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.88784

Abstract

The mixed geographically weighted regression (MGWR) method is a combination of a linear regression model and a geographically weighted regression (GWR) model. The MGWR model can produce parameter estimates that have global parameter estimates, and other parameters that have local parameters according to the observation location. This method can be used in epidemiological studies that are influenced by spatial heterogeneity. The aim of this research is to determine and model the factors that influence the Community Literacy Development Index (CLDI) in Indonesia based on MGWR modeling. The data used in this research is CLDI data in Indonesia in 2022 along with the factors that are thought to influence it. The results of this research indicate that the MGWR model outperforms both the linear regression and GWR models, as it yields the lowest Akaike information criterion (AIC) value and an ?² value of 96.54%. Based on the modeling results, several factors influencing CLDI were identified, including the percentage of libraries, the adequacy ratio of library collections, the average length of schooling, and the level of participation in organized learning. Keywords: Literacy; literacy development index; mixed geographically weighted regression; spatial
Determinants of Maternal Mortality in Indonesia: A B-Spline Nonparametric Regression Approach to Identify Nonlinear Relationship Patterns Suwardi Annas; Aswi Aswi; Rahmat Hidayat
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 11 No. 1 (2026): Mathline : Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v11i1.1015

Abstract

Maternal health quality is commonly assessed using the Maternal Mortality Ratio (MMR), which remains relatively high in Indonesia compared to regional and global targets. Understanding the determinants of MMR is therefore crucial for effective health policy formulation. This study aims to analyze the influence of three key factors on MMR in Indonesia: the percentage of women aged 15–49 who have ever been married and given birth to a live child, the percentage of households with access to proper sanitation, and the average years of schooling. To capture potential nonlinear relationships that may not be adequately addressed by conventional parametric regression models, this study employs a nonparametric B-spline regression approach. The analysis was conducted using the R statistical software. Model selection was based on the Generalized Cross-Validation (GCV) criterion to determine the optimal spline configuration. The results show that the optimal model achieves a minimum GCV value of 0.108 and an R² value of 0.8981, indicating a strong explanatory power and excellent model fit. The findings reveal that all three predictor variables have a significant and nonlinear effect on MMR. These results highlight the importance of considering flexible modeling approaches in maternal health studies and provide empirical evidence to support the development of more targeted and effective policies aimed at reducing maternal mortality in Indonesia.
Understanding the Causes, Consequences, and Effective Interventions for Illiteracy in Indonesia: A Systematic Literature Review Zakiah, Andi Sitti; Arismunandar, Arismunandar; Aswi, Aswi
Indonesian Educational Administration and Leadership Journal (IDEAL) Vol. 8 No. 2 (2026): On Progress
Publisher : Program Studi Adminsitrasi Pendidikan Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/ideal.v8i2.58286

Abstract

Illiteracy remains a significant educational challenge in Indonesia despite continuous improvements in educational access and national literacy initiatives. This study aims to systematically synthesize recent empirical evidence on the determinants of illiteracy, its multidimensional impacts, intervention strategies, and the effectiveness of literacy programs implemented in Indonesia. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines. Literature was retrieved using the Elicit academic search platform, with studies published between 2022 and 2026. After the screening and eligibility assessment, 28 studies were included in the qualitative synthesis. The findings indicate that illiteracy is influenced by interconnected individual, family, school, community, and policy-related factors. Beyond educational consequences, illiteracy negatively affects employment opportunities, social participation, health literacy, and human resource development. The reviewed studies identified five major categories of literacy interventions: technology-based, culture-based, instruction-based, policy/program-based, and materials-based strategies. Overall, the available evidence suggests that interventions integrating interactive learning, culturally relevant content, student-centered pedagogy, and collaboration among schools, families, communities, and government institutions tend to be more effective in improving literacy outcomes. However, the predominance of short-term intervention studies indicates the need for more longitudinal and large-scale research to strengthen the evidence base. This review suggests that sustainable literacy improvement is more likely to be achieved through comprehensive, context-sensitive, and multi-stakeholder approaches that address the diverse educational contexts across Indonesia.
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