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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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Journal : Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA)

 1 
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