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Journal of Mathematics, Computation and Statistics (JMATHCOS)
Published by Universitas Negeri Makassar
ISSN : 24769487     EISSN : 27210863     DOI : https://doi.org/10.35580/jmathcos
Core Subject : Education,
Mathematics
Fokus yang didasarkan tidak hanya untuk penelitian dan juga teori-teori pengetahuan yang tidak menerbitkan plagiarism. Ruang lingkup jurnal ini adalah teori matematika, matematika terapan, program perhitungan, perhitungan matematika, statistik, dan statistik matematika.
Arjuna Subject : Matematika - Matematika Terapan
Articles 210 Documents
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Comparative Analysis: Multiple Regression and Random Forest Regression in Predicting Food Security Index in Indonesia Handayani, Vitri Aprilla; Rahmiati, Sari; Varischa, Bintang; Arrafi, Adamsyam
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8593

Abstract

Food security was an important issue influenced by production, access, prices, and socio-economic conditions. In Indonesia, the Food Security Index (IKP) was used as the main indicator. However, prediction methods such as multiple linear regression often failed to capture the complex relationships between variables. Machine learning methods, such as random forest regression, offered a more suitable alternative for non-linear and large-scale data. Nevertheless, few studies in Indonesia compared the effectiveness of these two methods. Therefore, this study aimed to compare the performance of linear regression and random forest in predicting the IKP, in order to support more accurate and sustainable food security planning. The analysis results showed that the forecasting method with better performance in predicting the IKP in Indonesia was Random Forest Regression. This study made a significant contribution by empirically comparing multiple regression and Random Forest in predicting the Food Security Index (IKP) using big data. The results showed that Random Forest performed better in terms of MSE (5.5431) and RMSE (57.7242), indicating higher overall accuracy, while multiple regression had lower MAE (6.0805) and slightly higher R² (68.21%), suggesting more stable predictions and better explanatory power. Random Forest also identified key influencing variables, such as poverty rate and health worker ratio, and provided clearer insights through decision tree visualization. Overall, the findings demonstrated that while no model was entirely dominant, Random Forest offered greater flexibility and predictive strength for complex, large-scale data, supporting its potential use in formulating data-driven food security policies in Indonesia
Flood Risk Clustering Based on SARIMA Rainfall Prediction and Regional Mapping in Central Java Maulidiyah, Wildatul; Rahmasari, Hazelita Dwi; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul 
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8632

Abstract

High and spatially uneven rainfall is a major contributing factor to flooding in tropical regions such as Indonesia, including Central Java Province. This study aims to classify regions based on rainfall patterns using the Dynamic Time Warping (DTW) method and hierarchical clustering, followed by rainfall forecasting for each cluster using the SARIMA model. The dataset comprises monthly rainfall records from 2017 to 2023 across 35 regencies and cities in Central Java. The clustering process identified three distinct groups with low, medium, and high rainfall intensity. Evaluation results indicated that the single linkage models for each cluster were SARIMA(0,0,2)(0,1,0)[12] with a MAPE of 27% (Cluster 1), SARIMA(0,1,2)(0,1,1)[12] with a MAPE of 9.4% (Cluster 2), and SARIMA(1,0,0)(1,1,0)[12] with a MAPE of 9.97% (Cluster 3). These findings provide a robust spatio-temporal basis for supporting flood risk mitigation strategies based on rainfall prediction in Central Java.
Generalized Space-Time Autoregressive Moving Average Model with Rainfall as Exogenous Variable for Inflation Data in Sulawesi Island Rahman, Muhammad Fatur; Ihsan, Hisyam; Sanusi, Wahidah
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8798

Abstract

The Generalized Space-Time Autoregressive Moving Average with Exogenous Variables (GSTARMAX) model is an extension of the Generalized Space-Time Autoregressive Moving Average (GSTARMA) model, incorporating an exogenous variable (X) to enhance model accuracy while accounting for external factors. The advantage of the GSTARMAX model is its ability to accommodate location heterogeneity and generate a picture of an event for several future periods while considering other factors outside the scope of observation. This study applies the GSTARMAX model approach to analyze inflation data in Sulawesi Island, considering rainfall as an exogenous variable. Given the extreme and unpredictable climate changes, particularly rainfall in the Sulawesi region, which have become an annual phenomenon in recent years. This not only impacts community activities but also triggers uncertainty in future inflation. Uncontrolled inflation affects the decline in purchasing power, increases production costs, and disrupts goods distribution. Therefore, the objective of this study is to develop a model that can describe inflation in Sulawesi Island based on historical inflation and rainfall data. This study discusses the application of the Generalized Space-Time Autoregressive Moving Average with Exogenous Variables (GSTARMAX) model to analyze inflation in Sulawesi Island during the period 2020-2024. The data collected are from six provinces in Sulawesi Island: South Sulawesi, Southeast Sulawesi, West Sulawesi, Central Sulawesi, North Sulawesi, and Gorontalo. This study uses inverse distance weighting and cross-correlation normalization to build the model. The results indicate that the GSTARMAX (11;0;0) (1;2;0) or GSTARX (11) (1;2;0) model using cross-correlation normalization weights is the best model for inflation data in Sulawesi Island, with residuals that meet the white noise assumption. This means the model can be used to forecast future inflation.
Reformulation and Generalization Of Van Aubel's Theorem Using Complex Variables Laiya, Gusti Hari Fajar; Asriadi, Asriadi; Achmad, Novianita; Ismail, Sumarno; Nurwan, Nurwan
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9051

Abstract

This research aims to reformulate and generalize Van Aubel’s Theorem using a complex number approach. Classically, Van Aubel’s Theorem states that if squares are constructed externally on the sides of an arbitrary quadrilateral, then the line segments connecting the centers of opposite squares are congruent and perpendicular to each other. Through a complex algebra approach, this research systematically reconstructs the proof of the theorem and extends its application to more complex geometric forms. The representation of points, rotations, and lines in the complex plane is employed to reprove and develop generalizations of the theorem. The results demonstrate that the use of complex numbers facilitates geometric analysis and reveals new patterns in geometric relationships. This research contributes to the advancement of modern geometry through algebraic methods and opens opportunities for further exploration of complex numbers in solving other geometric problems.
Application of Association Rule Method Using the ECLAT Algorithm on Over-the-Counter Drug Transaction Data at Pharmacy “X” Dini, Sekti Kartika; Fuadah, Sekar Ridho
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9187

Abstract

Health is a fundamental need for every human being that plays an important role in improving the quality of life and productivity of society. As one of the means of pharmaceutical services, pharmacies not only serve as places for drug distribution but also as abundant sources of information regarding community purchasing patterns for health products. In the current digital era, every transaction that occurs at a pharmacy generates high-value data that can be utilised for data-driven decision making. One of the relevant analytical approaches in this context is Market Basket Analysis (MBA). Association rule is a commonly used method in MBA. This method generates rules in the form of implications "if X, then Y" based on the frequency of item occurrences in the data. The algorithm that can be used to perform association rule mining is ECLAT (Equivalence Class Clustering and bottom-up Lattice Traversal). Based on the results of the descriptive analysis of non-prescription drug transaction data from Pharmacy "X," it is known that the drugs frequently purchased by consumers are those containing paracetamol. Next, the association rule with the ECLAT algorithm with a minimum support of 0,0004 and a minimum confidence of 0,5 produces three rules that reflect that these drugs are often purchased together by consumers of Pharmacy "X".
Spatial Dynamics of Digital Society Index in Indonesia: A Spatial Autoregressive Approach Zakiyah Mar'ah; H. S., Rahmat; Hidayat, Rahmat
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9217

Abstract

The equitable distribution of digital transformation in Indonesia is a strategic issue, given the importance of technology in supporting national development. Indeks Masyarakat Digital Indonesia (IDSI) or Indonesian Digital Society Index (IDSI) was developed to measure the level of digital literacy, inclusion, and digital infrastructure readiness at the provincial level. However, the distribution of IDSI values shows striking spatial disparities. This research aims to identify spatial dependency patterns and significant factors influencing IDSI using a Spatial Autoregressive (SAR) model. The data used is the 2024 IDSI from 34 provinces in Indonesia, with five independent variables: access and adoption of digital technology, learning ecosystem, ICT introduction, empowerment, and employment. A spatial autocorrelation test using Moran's I revealed a significant positive spatial dependency, indicating a clustered pattern in IDSI distribution. The Lagrange Multiplier test showed spatial dependency in the lag or response variable, making the SAR model suitable. Estimation results demonstrate that all five independent variables significantly impact IDSI, with a coefficient of determination (R²) of 0.98. These findings indicate that geographically proximate regions tend to have similar IDSI values. Therefore, spatial approaches like SAR are crucial for formulating national digital equity policies.
Bayesian Spatial BYM CAR Model for Estimating the Relative Risk of Dengue Hemmorhagic Fever in Bandung Ananda Shafira; Asep Saefuddin; Kusman Sadik
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9272

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease whose transmission is influenced by spatial and environmental factors, including population density, altitude, household sanitation, and clean and healthy living behaviors. In 2022, the city of Bandung reported a high incidence of DHF cases, highlighting the need for spatial modeling to capture interdependencies among geographic regions. This study aims to examine the impact of different parameter settings in hyperprior distributions on the Besag-York-Mollie conditional autoregressive (BYM CAR) model, estimate the relative risk (RR) of DHF, and map district-level risk to support the identification of priority areas for targeted prevention. The BYM CAR model was employed within a Bayesian framework, and the posterior distributions were obtained using Markov Chain Monte Carlo (MCMC) with the Gibbs sampling algorithm. Five hyperprior scenarios based on the Inverse-Gamma distribution were compared to evaluate their influence on model performance. The results show that hyperprior selection substantially affects model outcomes, with the best model obtained when the prior for the structured spatial component was specified as Inverse-Gamma(0.1, 0.1), and the unstructured spatial component as Inverse-Gamma(1, 0.01). Gedebage, Arcamanik, and Rancasari districts were identifies as high-risk areas, while Babakan Ciparay and Bandung Kulon exhibited the lowest RR estimates. This spatial risk mapping offers insights for policymakers in formulating more targeted and efficient DHF prevention strategies.
Application of Complex Numbers to Prove Several Theorems in Plane Geometry Lamuda, Zulfatra; Asriadi, Asriadi; Panigoro, Hasan S.
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9273

Abstract

Geometry is a branch of mathematics that focuses on studying the characteristics of flat and spatial structures. One of the most well-known standard flat structures is the circle. Additionally, there are triangles formed by the intersection of three line segments. This study discusses the proofs of two theorems related to circles and triangles: the radius of the circumcircle of a triangle and Heron’s theorem. Heron’s theorem is used to determine the area of an arbitrary triangle. Unlike the conventional synthetic geometry approach, this article employs an alternative method, namely complex numbers, to prove these two central theorems.
The Impact of Malnutrition on Infant Mortality Rate in Indonesia: A Spline Regression Approach Muthahharah, Isma; Hidayat, Rahmat
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9351

Abstract

The Infant Mortality Rate (IMR) in Indonesia is still an important index in assessing the quality of public health. One aspect that is thought to influence the high IMR is malnutrition. One of the objectives of this study is to analyze the relationship between malnutrition and IMR through a nonparametric spline regression approach. The data used in this study are secondary data obtained from the Central Statistics Agency in 2022 with the IMR variable as the dependent variable and the percentage of malnutrition as the independent variable. The spline regression model was chosen because it is able to capture the nonlinear relationship between the variables analyzed. Based on the research results that have been obtained, we can see that the best model is spline regression, namely by selecting three knot points, the coefficient of determination (R^2) value is 23,27%. However, this model still has limitations, such as violations of residual assumptions. Therefore, it is hoped that further research will add or select other variables that may be more relevant in order to improve the quality of the model.
Modeling Grade Point Average (GPA) of Students in IAIN Sultan Amai Gorontalousing Binary Logistic Regression Approach Syilfi; Indriani; Muhammad Obie
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9377

Abstract

The objective of this study is to construct a model for the Grade Point Average (GPA) of the 2021 cohort of students enrolled in the Faculty of Education and Teacher Training (FITK) IAIN Sultan Amai Gorontalo, and analyze the factors that influence it. GPA modeling uses the binary logistic regression method. Binary logistic regression is a data analysis technique used to test whether or not there is a correlation between the response variable (y) which has two categories or is binary and the predictor variable (x) which has several categories or is polychotomous. This study uses primary data obtained through a questionnaire with a sample size of 190 students from six majors. The response variable in this study is student GPA and there are 8 independent variables studied. Based on the data obtained, the average GPA of 2021 students is 3.13. The results of data processing show that the variables of region of origin (x1), the number of organizations joined (x3), the average length of study per day (x4), and the average length of internet usage per day (x5) significantly affect the GPA of FITK students. The logit model obtained is . The probability function for obtaining a GPA ≥ 3.13 is .

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