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All Journal Media Statistika Jurnal Ilmiah Pena: Sains dan Ilmu Pendidikan Jurnal PAJAR (Pendidikan dan Pengajaran) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Varian Variansi : Journal of Statistics and Its Application on Teaching and Research Seminar Nasional Pengabdian Kepada Masyarakat VARIANSI: Journal of Statistics and Its Application on Teaching and Research Indonesian Journal of Fundamental Sciences ARRUS Jurnal Pengabdian Kepada Masyarakat Pinisi: Journal of Teacher Proffesional Indonesian Journal of Statistics and Its Applications SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Annas, Suwardi
Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Implementation of K-Means Clustering on Poverty Indicators in Indonesia Suwardi Annas; Bobby Poerwanto; Sapriani Sapriani; Muhammad Fahmuddin S
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1289

Abstract

This study aims to cluster all districts/cities in Indonesia related to poverty indicators. The attributes used are poverty gap index and poverty severity index. The data used comes from BPS. The method used is K-Means clustering, and the results show that by using the elbow and silhouette index methods, the optimal number of clusters is 2, where for cluster 1, it can be defined as a cluster with an area with a high poverty gap index and poverty severity index compared to cluster 2. As a result, cluster 1 has 42 districts/cities, and 472 for cluster 2.
Implementasi K-Affinity Propagation dalam Pengelompokan Provinsi di Indonesia Berdasarkan Kasus Pencemaran Lingkungan Hidup Derliani Natalia D.; Suwardi Annas; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm475

Abstract

Indonesia memiliki tingkat pencemaran lingkungan yang berbeda di setiap provinsi. Penelitian ini bertujuan untuk mengetahui gambaran dan hasil pengelompokan provinsi di Indonesia berdasarkan indikator pencemaran lingkungan yang meliputi pencemaran air, tanah, dan udara akibat limbah rumah tangga maupun limbah pabrik. Metode yang digunakan adalah K-Affinity Propagation (K-AP) dengan uji validasi Davies-Bouldin Index. Hasil analisis menunjukkan bahwa jumlah cluster optimum adalah 2, dimana Cluster 1 yang terdiri atas 3 provinsi dengan tingkat pencemaran lingkungan tertinggi, serta Cluster 2 yang terdiri atas 35 provinsi lainnya dengan tingkat pencemaran lebih rendah. Oleh karena itu, pemerintah perlu memberikan perhatian khusus pada provinsi yang masuk dalam Cluster 1, melalui pengawasan industri, pengelolaan limbah, serta peningkatan kesadaran masyarakat mengenai pentingnya pelestarian lingkungan.
Workshop on Student Graduation Decisions Using Statistical Methods at Takalar State Senior High School 7 Suwardi Annas; Ansari Saleh Ahmar; Zulkifli Rais; Rahmat H.S; Agung Tri Utomo
ARRUS Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.abdiku4458

Abstract

This community service program was conducted at SMA Negeri 7 Takalar to enhance teachers’ ability to utilize statistical methods specifically logistic regression to support data-driven graduation decisions. The training addressed challenges related to manual graduation assessment processes that often lack objective analytical support. Participants were introduced to the basic concepts of logistic regression, followed by hands-on practice using an interactive R Shiny dashboard to analyze student data and estimate graduation probabilities. The results indicate that teachers were able to understand and apply statistical analysis procedures, interpret logistic regression outputs, and recognize the importance of evidence-based decision-making. This activity not only improved teachers’ data literacy but also supported digital transformation efforts in education and strengthened collaboration between Universitas Negeri Makassar and SMA Negeri 7 Takalar. The program is expected to contribute to more accurate, transparent, and data-informed graduation assessments in the future.
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%.
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.
SpatialEBLUPinSmallAreaEstimation Muhammad Nusrang; Suwardi Annas; Asfar; Hastuty; Jajang
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 6 No. 1 (2017): Volume 6 Nomor 1 (Maret 2017)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

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

Abstract

Empirical Best Linear Unbiased Prediction (EBLUP)is one of methods in small area estimation. The prediction used in EBLUP is that the effect of mutual free-random errors area. But in some cases, this is not always obeyed. Its causes is the heterogenic of an area influenced by the other area surrounded, so that the influence of spatial can be get into the effect of random. Because of the disobey mak e the estimation of EBLUP is refraction and have big difference. Solution to overcome those is to put information of the spatial effect into the model. The estimation of small area that pays attention the influence of spatial area well known as estimator of SpatialEmpirical Best Linear Unbiased Prediction (SEBLUP). It gives the better estimation compared with the estimator of EBLUP based on the comparison of ARRMSE value from each estimation method.
Co-Authors Adi Rahmat Faisal Agung Tri Utomo Ahmad Fudhail  Majid Ahmar, Ansari Saleh Alakbar Akbar Alin Liana Andi Muhammad Ridho Yusuf Sainon Andi Pandjajangi Asdar Ahmad Asfar Aswi, Aswi BAKHRANI RAUF Bobby Poerwanto Bobby Poerwanto Derliani Natalia D. Fajar Arwadi Fithriyah Azzahrah Hastuti Hastuti Hastuty Irwan Irwan Irwan Irwan Irwan Irwan Irwan Irwan Jajang Jajang Laksono Trisnantoro Luthfiah Arradiah Mahrani Mahrani Mega Fatimah Rosana Muh Yahya Muh.Nadjib Bustan Muhammad Abdy Muhammad Fahmuddin Muhammad Fahmuddin Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Nadjib Bustan Muhammad Nusrang Muharram Muharram Nunung Marlika Nur Aisyah Nur Isra Nurhamidah Mursyidin Nurul Fadhilah Syahrul Nurul Imania Kalla Nusrang, Muhammad P. Paramita Rahmat H.S Rahmat Hesha Safei Rahmat Hidayat Ridawati Ridawati Rika Nasir Ruliana Sapriani Sapriani Saripuddin D Sitti Faika Syafruddin Side Uca Sideng Wahidah Sanusi Zulkifli Rais