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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) Jurnal Varian Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Panrannuangku Jurnal Pengabdian Masyarakat ARRUS Journal of Mathematics and Applied Science 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) 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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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.
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.
Co-Authors Abdul Rahman Ahmar, Ansari Saleh Aidid, Muhammad Kasim Asfar 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 Arif Tiro, Muhammad Arif Muhammad Refaldy Muhammad Yahya nurismi, Nur ismi Nurrahmah Nurrahmah, Nurrahmah Nusrang, Muhammad Pandjajangi, Andi Muhammad Ridho Yusuf Sainon Andi 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