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Adnan Sauddin
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Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA)
Published by Universitas Islam Negeri Alauddin Makassar
ISSN : 2355038X     EISSN : 25500767     DOI : https://doi.org/10.24252/msa
Core Subject : Economy, Health, Science, Education, Social,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology
The Jurnal MSA (Jurnal Matematika dan Statistika serta Aplikasinya) is a brand new on-line anonymously peer-reviewed journal interested in any aspect related to mathematics and statistics with their application. The Jurnal MSA is ready to receive manuscripts on all aspects concerning any aspect related to mathematics and statistics science with their application.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 295 Documents
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Perbandingan Analisis Cluster Kabupaten/Kota Di Provinsi Sulawesi Selatan Berdasarkan Luas Panen Dan Produksi Padi Menggunakan Average Linkage Method Dan Ward’s Method Nurul Jusmahilda Ismail; Try Azisah Nurman; Adiatma
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.v13i2.51488

Abstract

This study aims to further examine the two best methods, namely the Average Linkage Method and Ward's Method obtained from previous researchers by grouping 24 Regencies/Cities in South Sulawesi Province based on the variables of harvested area and rice production in 2023. In this study, Euclidean and Squared Euclidean distance measures were used. Furthermore, the minimum standard deviation value within the cluster (Sw), the maximum standard deviation between clusters (Sb), and the minimum ratio of Sw to Sb were observed to determine the most effective method. And also seen from the best Dunn Index value in determining the optimum number of clusters for each method. Based on the formation of the dendogram, the number of clusters tested was 2 to 6 clusters. The results of the study explain that of the two methods used, the method with the best performance is the Average Linkage method with the formation of an optimum cluster of 3 clusters, where cluster I has a low level of rice productivity. Cluster II has a high level of rice productivity. Cluster III has a fairly high (medium) level of rice productivity. And in the Ward’s method, the optimum number of clusters formed is two clusters, with Cluster I having a low level of rice productivity and Cluster II having a high level of rice productivity.
Bayesian Spatio-Temporal Conditional Autoregressive Modelling of Factors Affecting Pneumonia Cases in Indonesia Risma Mastory; Aswi, Aswi; Muhammad Fahmuddin; Lalu Ramzy Rahmanda
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.v13i2.56315

Abstract

The Bayesian Spatio-Temporal Conditional Autoregressive (BST CAR) method is a statistical approach used to analyze data with both spatial and temporal components. While the BST CAR model has been widely applied in various studies, no research has yet explored using the Localized BST CAR model for pneumonia cases in Indonesia. This study aims to identify and model the factors influencing pneumonia incidence in Indonesia using the Localized BST CAR framework. The data analyzed in this study consist of the number of pneumonia cases in Indonesia from 2018 to 2022, along with variables believed to affect the incidence. The findings indicate that the Localized BST CAR model with G=3 provides the best fit for modeling the relative risk of pneumonia cases in Indonesia. Key factors found to significantly influence pneumonia cases include the percentage of exclusively breastfed infants, the percentage of infants with complete basic immunization, and the percentage of the population living in poverty. Notably, the percentage of exclusively breastfed infants and the percentage of fully immunized infants were positively associated with pneumonia cases, while the percentage of the poor population had a negative effect
Autoregressive Distributed Lag (ARDL) Method for Estimating Poverty Levels in Polewali Mandar Regency Abeng, Andi Tenri; Alwi, Wahidah; Sauddin, Adnan; Anugrawati, Sri Dewi; Aeni, Nur
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.v13i2.60197

Abstract

Polewali Mandar Regency is the region with the highest poverty rate in West Sulawesi. According to a publication by the Central Bureau of Statistics in March 2022, the percentage of the poor population was 11.75%, an increase compared to March 2021. The forecasting method used in this study is the Autoregressive Distributed Lag (ARDL) method. This study aims to determine the Autoregressive Distributed Lag (ARDL) model, which is then used to forecast the number of poor people in Polewali Mandar Regency. The results of the study using the ARDL method yielded the best estimation model, namely ARDL (3, 3, 2, 2). The forecast results for the percentage of the poor population using the ARDL (3, 3, 2, 2) model for the following semesters are 21.79%, 10.15%, and 16.52%, respectively. The forecasting accuracy test using the Mean Absolute Percentage Error (MAPE) yielded a value of 12.18%, indicating that the ARDL model produced in this study is suitable for forecasting the percentage of the poor population in Polewali Mandar Regency.
Analysis of Catfish Production Growth Using Bernoulli’s Differential Equation Mey Gracia Sitanggang; Tio Arini Pasaribu; Rawiyah; Maria Adesuryani Purba; Lahnida Sitinjak; Nasution , Hamidah
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.v13i2.61668

Abstract

Catfish (Clarias sp.) is one of the most widely cultivated aquaculture commodities in Indonesia, playing an important role in food security and local economic development. This study applies Bernoulli’s differential equation, which reduces to a logistic model, to evaluate catfish production in Indonesia from 2019 to 2025. Secondary data were obtained from the Ministry of Marine Affairs and Fisheries. Model parameters, including carrying capacity, intrinsic growth rate, and initial production, were estimated to fit the observed data. The results show that the logistic model effectively represents production trends between 2020 and 2024, with an average annual growth rate of approximately 4%. However, the model fails to capture the sharp decline observed in 2025, when actual production dropped by nearly 47%. This discrepancy indicates the presence of external non-linear factors, such as disease, environmental stress, or distribution disruptions, that were not included in the mathematical framework. Therefore, Bernoulli’s differential equation provides a useful baseline for analyzing production growth under normal conditions, while highlighting the need to integrate ecological and managerial considerations for more accurate long-term predictions.
Application of ST-DBSCAN Algorithm in Clustering Earthquake Points in Sulawesi Region Sutamrin; Thaha, Irwan; Nur Insani Maiwa
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.v13i2.61832

Abstract

Clustering is a method in data mining that aims to group data based on certain similarities or characteristics. One of the clustering methods or algorithms is the Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. This algorithm was chosen because of its ability to analyse data based on spatial and temporal dimensions simultaneously, using the parameters of spatial distance (ε₁), temporal distance (ε₂), and minimum number of points (MinPts). This study aims to determine the results of the ST-DBSCAN algorithm in clustering earthquake points in the Sulawesi Region. The data analysed is secondary data obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) for the period 2019-2023, covering 12109 earthquake points with magnitude ≥ 3 on the Richter scale. The results show that earthquake points in Sulawesi are concentrated in subduction zones and active faults. The most earthquake-prone areas include North Sulawesi and Gorontalo, which are affected by the subduction of the Pacific and Eurasian Plates. In addition, Central Sulawesi, West Sulawesi, South Sulawesi and Southeast Sulawesi are also at high risk due to the activity of the Palu-Koro Fault. Earthquake intensity around the Flores Sea and Banda Sea increases in 2021-2022 due to subduction of the Indo-Australian Plate. The optimal parameters for clustering varied every year during the study. The optimal parameters for clustering varied every year during the study period. This study provides new insights into seismic activity patterns in Sulawesi that can be utilised to support disaster mitigation and earthquake risk reduction policies.

Page 30 of 30 | Total Record : 295

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