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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JURNAL ILMIAH MATEMATIKA DAN TERAPAN Jurnal SOLMA
Asfar
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Other

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Temperature Data Prediction in South Sulawesi Province Using Seasonal-Generalized Space Time Autoregressive (S-GSTAR) Model Rizal, Muhammad Edy; Fathan, Morina A.; Safitriani, Nur Rezky; Yahya, Muhammad Zarkawi; Asfar
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 2 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i2.17516

Abstract

Indonesia's distinct tropical climate is influenced by its geographic location near the equator and its complex topography, resulting in pronounced seasonal temperature patterns. This study examines the application of the Seasonal Generalized Space-Time Autoregressive (SGSTAR) model to forecast the average air temperature in four regions of South Sulawesi Province: North Luwu, Tana Toraja, Maros, and Makassar. The dataset comprises monthly average temperatures from January 2019 to October 2024, sourced from BMKG's online database. The analysis includes stationarity testing using the Augmented Dickey-Fuller (ADF) test, seasonal pattern identification with autocorrelation function (ACF), and formal seasonal tests such as QS, QS-R, and KW-R. Spatial weight matrices were constructed based on Euclidean distances between regions. The best model was selected based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), and adjusted R² criteria. The findings reveal that the seasonal GSTAR model with AR orders (p=3), (ps=4), and (s=12) is the optimal model. Evaluation indicates that the model achieves high accuracy, with forecast errors (MSE and RMSE) below 1°C. This model effectively captures seasonal and spatio-temporal patterns in climate data. The study is expected to serve as a foundation for further development of seasonal GSTAR models for other climate datasets, supporting improved environmental planning and resource management.
Optimalisasi Limbah Sekam Padi dan Bonggol Pisang Kombinasi Daun Gamal sebagai Biofertilizer Mutmainna, Mutmainna; Andi Trisnowali Ms; Asfar; Nurannisa; Rasmiati; Ikasari
Jurnal SOLMA Vol. 14 No. 1 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i1.16911

Abstract

Background: Permasalahan limbah sekam padi di Desa Tappale masih menjadi permasalahan yang krusial khususnya bagi Mitra Kelompok Karang Taruna Sipabokori di Desa Tappale. Tujuan: Tujuan dari kegiatan pengabdian kepada masyarakat akan memberikan solusi kepada Mitra Kelompok Karang Taruna Sipabokori Desa Tappale untuk mereduksi limbah sekam padi dan bonggol pisang (duo bio) kombinasi daun gamal menjadi alternatif pupuk hayati (biofertilizer) dan meningkatkan pengelolaan kebun yang ramah lingkungan di Desa Tappale. Metode: Adapun metode yang digunakan dalam kegiatan pengabdian kepada masyarakat yaitu metode society participatory melalui pendekatan learning by doing. Tahapan pelaksanaan kegiatan pengabdian kepada masyarakat dilakukan dalam 3 tahap, yaitu tahap penyuluhan, tahap pelatihan dan tahap pendampingan. Hasil: Hasil kegiatan menunjukkan peningkatan signifikan pada berbagai aspek. Pengetahuan mitra meningkat dari 20% menjadi 80%, keterampilan produksi biofertilizer meningkat dari 10% menjadi 90% serta pemahaman terkait pengemasan dan pelabelan produk meningkat hingga 90%. Selain itu, mitra berhasil memahami strategi pemasaran hingga 95%, termasuk pemasaran digital melalui marketplace. Kesimpulan: Kegiatan ini berdampak positif dalam membangun kapasitas mitra dalam pengelolaan lingkungan dan wirausaha berbasis biofertilizer. Program ini diharapkan menjadi langkah awal dalam menciptakan desa yang lebih mandiri dan ramah lingkungan, serta berkontribusi pada SDGs desa nomor 3 (Desa Sehat dan Sejahtera).
Model Penyebaran Covid-19 di Provinsi Sulawesi Selatan Menggunakan Poisson Inverse Gaussian Regression (PIGR) Munawwarah; Adnan Sauddin; Nurfadilah, Khalilah; Asfar
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.56944

Abstract

Artikel ini membahas tentang model penyebaran Covid-19 di Provinsi Sulawesi Selatan menggunakan regresi Poisson Inverse Gaussian (PIG). Covid-19 merupakan penyakit menular yang berpotensi menimbulkan kedaruratan kesehatan masyarakat, hal ini disebabkan karena penyakit tersebut dapat menular melalui droplet yang keluar dari dari batuk, bersin, hingga napas orang yang terinfeksi Covid-19. Tujuan penelitian ini yaitu untuk mendapatkan model penyebaran Covid-19 di Provinsi Sulawesi Selatan menggunakan regresi Poisson Inverse Gaussian (PIG). Model rata-rata penyebaran Covid-19 di Provinsi Sulawesi Selatan menggunakan regresi Poisson Inverse Gaussian (PIG)
Exploring Diabetes Mellitus Risk Patterns with Multiple Correspondence Analysis at Torabelo Hospital, Central Sulawesi Asfar; Fadjryani; Ambarwati B. Sarabi , Valina; Jannah , Miftahul; Sartika, Dewi; Setiawan, Aldi; Nurfadilah, Khalilah
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.v13i2.59040

Abstract

This study aims to explore the pattern of diabetes mellitus risk factors among patients at Torabelo Regional Hospital in Sigi using Multiple Correspondence Analysis (MCA). A total of 465 patients were analyzed based on eight categorical variables, including age, gender, blood glucose levels, and lipid profiles. MCA was applied to identify inter-category relationships and visualize them in a low-dimensional space. The results show that most diabetes patients were female, aged 46 years and above, and had high fasting glucose, low HDL, and high LDL levels. The analysis identified two main patterns: a group with a low-risk metabolic profile who were not diagnosed with diabetes, and a group with a combination of high-risk metabolic categories who were more likely to already have diabetes. A distinct subgroup with extremely high triglyceride levels was also identified, indicating a rare but significant metabolic pattern. The first two dimensions of the MCA explained more than 40% of the data variation, providing sufficient support for meaningful visual interpretation. These findings demonstrate that MCA is effective in simplifying complex categorical data and supports risk-based segmentation strategies for early intervention planning in primary healthcare services, particularly in regions with high diabetes prevalence.
Co-Authors Adnan Sauddin Aldi Setiawan, Aldi Ambarwati B. Sarabi , Valina DEWI SARTIKA Fadjryani, Fadjryani Fathan, Morina A. Ikasari Jannah , Miftahul Muhammad Edy Rizal Munawwarah Mutmainna, Mutmainna Nurannisa Nurfadilah, Khalilah Rasmiati Safitriani, Nur Rezky Trisnowali MS, Andi Yahya, Muhammad Zarkawi