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JURNAL ILMIAH MATEMATIKA DAN TERAPAN
Published by Universitas Tadulako
ISSN : 18298133     EISSN : 2450766X     DOI : -
Core Subject : Education,
Mathematics
Jurnal Ilmiah Matematika dan Terapan adalah Jurnal yang diterbitkan oleh Program Studi Matematika FMIPA Universitas Tadulako. Jurnal ini menerbitkan artikel hasil penelitian atau telaah pustaka bersifat original meliputi semua konsentrasi bidang ilmu matematika dan terapannya, seperti analisis, aljabar, kombinatorika, matematika diskrit, statistika, dan semua aspek terapannya.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 307 Documents
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Biplot Analysis for Spatial Mapping of Dengue Hemorrhagic Fever (DHF) Incidence in Indonesia Gani, Fadjryani Abdul; Aisya, Cici; Ainanur; Afriza, Dini Aprilia
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.i2.17401

Abstract

Dengue Hemorrhagic Fever (DHF) is a serious threat to Indonesian public health, with the dengue virus spread by the Aedes aegypti mosquito continuing to claim victims in all provinces in Indonesia. The drastic variation of DHF incidence between provinces requires an in-depth understanding of its distribution pattern. Biplot analysis allows researchers to identify patterns based on factors that influence the incidence of DHF in different provinces. This study aims to identify the spatial distribution pattern of DHF in Indonesia using biplot analysis, an approach that allows complex visualization of factors affecting DHF incidence. Results showed that 62.48% of the data variation could be explained through biplot representation, revealing spatial distribution patterns, proximity between objects and diversity between variables. Key findings include the identification of provinces with the highest DHF cases (56,388 cases) in quadrant IV, the high incidence of DHF cases was associated with similar characteristics of average air humidity. In addition, there was significant variation in the number of DHF cases between provinces indicating disparities in the number of DHF cases in different parts of Indonesia, as well as relative uniformity in the percentage of households with proper sanitation (descriptive average of 86.62%). The results of this study are expected to assist policy makers in formulating more effective and targeted dengue prevention and control strategies, potentially reducing the incidence of dengue and improving the health of the Indonesian people.
Optimization of Overdispersion Modeling in Low Birth Weight Cases in Central Sulawesi Using Conway-Maxwell Poisson Regression Gamayanti, Nurul Fiskia; Nur'eni; Fadjryani; Astuti, Dewi Puji
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.i2.17429

Abstract

Low birth weight (LBW) is a condition of a baby weighing less than 2,500 grams where gestational age is not taken into account and the baby's weight is measured within 24 hours after birth. The level of infant development also plays an important role in determining the mortality rate and incidence rate of disease in infants with LBW. This study aims to find models and factors that influence LBW using Conway Maxwell Poisson Regression (CMPR). CMPR is an extension method of Poisson regression that has the advantage of overcoming violations of the equidispersion assumption, where data can experience overdispersion or underdispersion
Location Based Stunting Modeling Using Geographically Weighted Panel Regression in Blitar Regency Pramoedyo, Henny; Ngabu, Wigbertus; Iriany, Atiek
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.i2.17446

Abstract

Stunting remains a significant public health issue in Blitar Regency, Indonesia, particularly in rural areas where chronic malnutrition and inadequate access to healthcare services persist as major challenges. This study aims to explore the spatial and temporal factors influencing stunting using the Geographically Weighted Panel Regression (GWPR) method. By integrating cross-sectional and time-series data from 2021 to 2023, the study evaluates various factors, including the stunting prevalence rate and independent variables such as maternal education level, per capita income, the number of postpartum mothers receiving Vitamin A supplements, immunization coverage, and the availability of healthcare personnel. The findings reveal that stunting prevalence is significantly influenced by location-specific variables, with healthcare access and nutrition being dominant factors in rural areas, while economic conditions exert a greater influence in urban areas. The GWPR model provides deeper insights into spatial heterogeneity and offers valuable guidance for designing targeted and region-specific policies to reduce stunting rates in Blitar Regency. The results indicate that the R-Square value of the GWPR model is 0.9123, meaning that 91.23% of the stunting prevalence in Blitar Regency can be explained by the independent variables in this study
Optimization of Agricultural Land with the Hungarian Algorithm Method (Case Study: Agricultural Land in Tuatuka Village, Kupang Regency) Adoe, Vera Selviana; Senge, Yitran Detia; Metkono, Marci; Nae, Yohanes
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.i2.17466

Abstract

This research delves into how the Hungarian Algorithm's utilized to streamline workforce allocation, for activities in Tuatuka Village located in Kupang Regency in East Nusa Tenggara region of Indonesia. The primary obstacles encountered include shortage of labor and differences, in skill levels that affect productivity levels significantly. By employing the Hungarian Algorithm a mathematical method is utilized to reduce costs and time associated with assigning tasks effectively by matching workers to duties based on their skills and capabilities. This study includes gathering data by observing and conducting interviews that are later examined using POM QM, for Windows V5 software and manual computations. The results indicate that implementing this method can cut down total project expenses to 35 work hours through task allocation strategies. The adoption of the Hungarian Algorithm has been successful in improving workforce efficiency in agriculture areas leading to output. Decreased resource wastage. Therefore this study aids, in streamlining operations in the industry especially when it comes to managing resources in rural settings.
Comparison of Random Survival Forest and Fuzzy Random Survival Forest Models in Telecommunications Industry Customer Data Nurhaliza, Sitti; Harismahyanti, Andi; Najiha, Alimatun
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.i2.17498

Abstract

The telecommunications sector is facing increasing competition, and customer churn is still a majorchallenge despite the implementation of advanced promotions and high-quality services. Churn refers tothe discontinuation of services by customers, influenced by several factors that can be found through datamodeling. This study compares two predictive models, Random Survival Forest (RSF) and Fuzzy RandomSurvival Forest (FRSF), for predicting customer churn time in the telecommunications industry. Bothmodels are evaluated using the median C-index value obtained from 20 repetitions, ensuring moreconsistent and reliable results. RSF, a widely used survival analysis method, has shown strong predictivepower, with studies reporting up to 99% accuracy in churn prediction. However, FRSF, a modified versionthat incorporates fuzzy logic, has proved superior performance, particularly in handling imprecise oruncertain data. The results show that FRSF achieves a lower error rate of 0.1739, compared to RSF's errorrate of 0.1906. These findings suggest that FRSF outperforms RSF in churn prediction, making it a morereliable and righter model for finding at-risk customers. The study concludes that the FRSF model is thepreferred choice for predicting churn in the telecommunications industry, offering better predictive qualityand consistency in handling uncertain data.
Grouping of Regencies/Cities in Indonesia Based on National Health Insurance (JKN) Participants with the Ensemble ROCK Approach Azwarini, Rahmania; Fathan, Morina A.; Widiantoro, Tri
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.i2.17512

Abstract

Health is a fundamental human need, and the National Health Insurance (JKN) program was established in Indonesia to provide equitable access to healthcare services for all citizens. Despite its implementation, disparities remain across regencies/cities, necessitating a comprehensive mapping of JKN participant profiles. This study aims to group 34 regencies/cities in Indonesia based on the characteristics of JKN participants, utilizing numerical and categorical data clustering. The Ensemble Robust Clustering using links (ROCK) method was employed, combining hierarchical clustering for numerical data and the ROCK method for categorical data. The study analyzed data comprising eight numerical variables (age, household size, household total expend, expend healthcare, tobacco expend, ATP, WTP, and expend insurance) and six categorical variables (living area, sex, education, reasons for joining JKN, ATP, WTP). Numerical clustering through single linkage yielded four clusters, while categorical clustering with the ROCK method at a threshold value of 0.2 produced three groups. The final ROCK ensemble analysis integrated these results, forming three quality-based clusters: low, medium, and high. Key findings revealed distinct socio-economic and demographic patterns among the clusters. For instance, the low-quality group exhibited lower household expenditures and healthcare spending, while the high-quality group had higher averages across these variables. Insights from this study can guide policy-makers in prioritizing healthcare resources and addressing regional disparities in JKN implementation.
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.

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