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All Journal SAINSMAT JAM : Jurnal Aplikasi Manajemen JOIV : International Journal on Informatics Visualization Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal PAJAR (Pendidikan dan Pengajaran) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Varian Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Panrannuangku Jurnal Pengabdian Masyarakat ARRUS Journal of Mathematics and Applied Science SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research Indonesian Journal of Fundamental Sciences Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Jurnal Pendidikan Matematika Parameter: Jurnal Matematika, Statistika dan Terapannya Abdi Kimia : Jurnal Pengabdian Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications International Journal of Accounting and Management Information Systems
Suwardi Annas
STKIP Pembangunan Indonesia Makassar Universitas Negeri Makassar
Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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Pendekatan Geographically Weighted Regression (GWR) untuk Menganalisis Hubungan PDRB Sektor Pertanian, Kehutanan, dan Perikanan dengan Faktor Pencemaran Lingkungan di Jawa Timur Bakri, Nurul Aulya; Annas, Suwardi; Aidid, Muhammad Kasim
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm194

Abstract

The Geographically Weighted Regression (GWR) method is a method used to analyze spatial heterogeneity, where the same independent variable gives unequal responses at different locations in a research area. The purpose of this study was to determine the environmental pollution factors that affect GRDP in the agricultural, forestry and fisheries sectors in East Java. The data used in this study are the GRDP of the Agriculture, Forestry and Fisheries sectors in East Java in 2020 along with the environmental pollution factors that are thought to influence it. The results of this study obtained a different model for each district/city. The GWR model shows better results than the multiple linear regression model, as seen from the smallest AIC value and the largest R2
TREND ANALYSIS OF EARLY MARRIAGE CASES IN SOUTH SULAWESI USING VECTOR AUTOREGRESSIVE FOR STUNTING SOLUTION Astuti, Astuti; Sanusi, Wahidah; Annas, Suwardi
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i1pp153-166

Abstract

The purpose of this study is to use the VARmodel to predict and project the number of early marriage cases in South Sulawesi for the upcoming year. The data used in this analysis comes from the Dinas Pemberdayaan, Perlindungan Perempuan dan Anak, and the Pengadilan Tinggi Agama Makassar, covering the period from January 2017 to September 2024. The results indicate that the VAR(2) model is the best choice according to the AIC for determining the optimal lag length. To examine the relationships between variables, a Granger causality test was conducted for each district and city. The findings reveal significant causal relationships in most districts, suggesting that changes in one district can influence early marriage trends in others. The MAE method was used to calculate the prediction error. Some regions, such as Sengkang, Pangkajene, and Pare-Pare, showed an increasing trend in the projected number of early marriage cases from October 2024 to September 2025. In contrast, Barru and Masamba experienced a decline in these cases. Reducing early marriages could help lower rates of stunting, as early marriage is often linked to maternal and child health issues as well as malnutrition. These findings are valuable for developing effective policies aimed at reducing early marriage and its associated consequences for the people of South Sulawesi.
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
Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters Annas, Suwardi; Saleh Ahmar, Ansari; Hidayat, Rahmat
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1685

Abstract

Unhealthy and unsafe sanitation will make it easier for various diseases to attack the body. In addition, unsafe sanitation will also affect a country's economy, including declining welfare, tourism losses, and environmental losses due to the loss of productive land. The research aimed to estimate the welfare cost of premature deaths based on unsafe sanitation risks using the SutteARIMA, Neural Network Time Series, and Holt-Winters. The study analyzed estimates and projections of the welfare cost of premature deaths based on the risks of unsafe sanitation of BRICS countries (Brazil, Russia, Indonesia, China, and South Africa). The data in this research used secondary data. Secondary time series data was taken from the Environment Database of the OECD. Stat. (Mortality and welfare cost from exposure to environmental risks). The data on the study was based on variables: welfare cost of premature deaths, % GDP equivalent, risk: unsafe sanitation, age: all, sex: both, unit: percentage, and data from 2005 to 2019. The three forecasting methods (SutteARIMA, Neural Network Time Series, and Holt-Winters) were juxtaposed in fitting data to see the forecasting methods' reliability and accuracy. The accuracy of forecasting results was compared based on MAPE and MSE values. The results of the research showed that the SutteARIMA and NNAR(1,1) methods were best used to predict the welfare cost of premature deaths in view of unsafe sanitation risks for BRICS countries.
Flipbook-based personal development information service model for enhancing self-awareness in late adolescents Heriyah, Heriyah; Bundu, Patta; Annas, Suwardi
International Journal of Accounting and Management Information Systems Vol. 2 No. 1 (2024): February
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijamis.v2i1.3208

Abstract

Purpose: This study aimed to develop a flipbook-based personal development information service model and examine the increase in self-awareness of late adolescents as a measure of its effectiveness Research/methodology: This research used development research methods with an ADDIE design model. The instruments used in this study were validation and practicality sheets, model needs analysis questionnaires, self-awareness questionnaires, interviews, and observations. The data processing used in this research was based on the model's validity and practicality, and N-gain test analysis and Paired Sample t-test using SPSS version 24 were also carried out to evaluate effectiveness. Results: The research results obtained are as follows: 1) assessment of the feasibility of the flipbook-based personal development information service model shows a high validity category with an average value of 91%, and its practicality is considered very practical to use with an average value of 87%; 2) assessment of effectiveness, there is a Sig.2 tailed value of 0.000 < 0.025, so Ho is rejected, and Ha is accepted, as well as an increase in the average (Gain) of pre- and post-test data of 0.60, which is classified as a medium criterion. Based on the research results, the developed product model is effective and contributes to increasing self-awareness among high school students in their late adolescence. Conclusions: The flipbook-based personal development information service model is proven to be valid, practical, and effective in enhancing self-awareness among late adolescents. The product development follows the ADDIE framework and meets the criteria of high validity (91%), high practicality (87%), and effectiveness (N-gain = 0.60; Sig. = 0.000), indicating a statistically significant improvement in students' self-awareness. Limitations: This study is limited to high school students in South Tangerang and does not generalize across broader adolescent populations. The implementation relies on digital literacy and access to stable internet, which may not be consistent across various educational contexts. Contribution: This study introduces a validated flipbook-based model that enhances self-awareness in adolescents through accessible and interactive guidance services.
Determinants of Maternal Mortality in Indonesia: A B-Spline Nonparametric Regression Approach to Identify Nonlinear Relationship Patterns Annas, Suwardi; Aswi, Aswi; Hidayat, Rahmat
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.
Evaluating Random Forest Regression for Air Quality Prediction Izabi, Muh. Basyar; Annas, Suwardi; Ahmar, Ansari Saleh
Jurnal Varian Vol. 9 No. 1 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i1.6046

Abstract

Air pollution is a growing environmental issue in Makassar due to rapid urban development and increasing transportation activity. This study aims to model and predict air pollutant concentrations using the Random Forest (RF) regression method. The data consist of daily PM2.5, PM10, CO, NO2, SO2, and O3 measurements from September 2024 to September 2025, totaling 395 observations. Missing values (14.05%) were addressed using a hybrid approach combining linear interpolation and multiple linear regression. The RF model was trained under two data-split scenarios (70:30 and 80:20) and evaluated using SMAPE, RMSE, MAE, and R2. The results show that the 80:20 configuration provides the best predictive accuracy. CO and O3 yield the most accurate predictions with SMAPE values of 9.75% and 10.87%, and R2 of 0.973 and 0.964, respectively. PM2.5 and PM10 also show strong performance, with R2 values above 0.84. These results indicate that the RF model effectively captures pollutant variability and provides reliable forecasts. Overall, Random Forest has been shown to be a robust and accurate method for predicting air quality in Makassar, supporting environmental monitoring and early warning systems. Despite its strong performance, this study is limited to two data-partition schemes and does not incorporate temporal deep-learning architectures. Future studies may investigate hybrid ensembles or deep learning approaches to determine whether incorporating sequential modeling further enhances predictive stability.
Penerapan Algoritma Naive Bayes untuk Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Nunung Marlika; Aswi; Annas, Suwardi
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%.
Binary Logistic Regression Model of Stroke Patients: A Case Study of Stroke Centre Hospital in Makassar Annas, Suwardi; Aswi, Aswi; Abdy, Muhammad; Poerwanto, Bobby
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.
Co-Authors Abdul Rahman Ahmar, Ansari Saleh Aidid, Muhammad Kasim Asfar Astuti - Aswi Aswi, Aswi Bakri, Nurul Aulya Bundu, Patta Devilla, Rego Fahmuddin S, Muhammad Fahmuddin, Muh. Hastuty Musa Heriyah, Heriyah Hisyam Ihsan Irwan Irwan Irwan Istiqfar, Istiqfar Ivan, Zhalsa Larasati Izabi, Muh. Basyar Jajang Jajang Kaito, Nurlaila Khairana, Nadiyah Mahadtir, Muhamad Maksar, Adie Mar’ah, Zakiyah Muh. Idris Muhammad Abdy Muhammad Arif Tiro, Muhammad Arif Muhammad Refaldy Muhammad Yahya Nunung Marlika nurismi, Nur ismi Nurrahmah Nurrahmah, Nurrahmah Nusrang, Muhammad Pandjajangi, Andi Muhammad Ridho Yusuf Sainon Andi Poerwanto, Bobby Putri, Siti Choiratun Aisyah Rahmat Hidayat Rahmat, Rahmat Wahyudi Rais, Zulkifli Refaldy, Muhammad Rudi Ruliana Ruliana Ruliana, Ruliana Sainon Andi Pandjajangi, Andi Muhammad Ridho Yusuf Saleh Ahmar, Ansari Sapinah Shanty, Meyrna Vidya Sriwahyuni, Andi Ayu Syafruddin Side Vivianti Wahidah Sanusi Yassar, La Ode Salman Zulkifli Rais