cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Yahya Matdoan
Contact Email
keepyahya@gmail.com
Phone
+6282193229395
Journal Mail Official
jurnalparameter@gmail.com
Editorial Address
Jl. Ir. M. Putuhena, Poka-Ambon, 97233, Maluku, Indonesia
Location
Kota ambon,
Maluku
INDONESIA
Parameter: Jurnal Matematika, Statistika dan Terapannya
Published by Universitas Pattimura
ISSN : 28296303     EISSN : 28296303     DOI : https://doi.org/10.30598/parameterv3i01y2024
Core Subject : Education,
Mathematics
Parameter: Jurnal Matematika, Statistika dan Terapannya is an open access journal (e-journal) published since April 2022. Parameteris published by Department of Mathematics, Faculty of Science and Mathematics, Pattimura. Parameterpublished scientific articles on various aspects related to mathematics and statistics and its application. Articles can be in the form of research results, case studies, or literature reviews.
Arjuna Subject : Matematika - Matematika Terapan
Articles 110 Documents
 7   8   9   10   11 
Swarm-Genetics: A Hybrid PSO-GA Regeneration Model for Global Optimization Benchmark Problems Aprizal Resky; Zaitun Zaitun; Ryo Hartawan Sasolo; Andi Isna Yunita
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp45-58

Abstract

Particle Swarm Optimization (PSO) is widely used in global optimization due to its simple structure and fast convergence, but it may suffer from premature convergence in multimodal search spaces. This study proposes Swarm-Genetics, an iteration-wise hybrid PSO-GA regeneration framework that combines PSO-based particle movement with Genetic Algorithm operators. In each iteration, particles are first updated using PSO velocity and position equations, then regenerated through crossover and mutation, followed by the selection of the best particles for the next iteration. The proposed method was evaluated on fourteen benchmark functions and compared with standard PSO and GA using mean fitness values. The results show that Swarm-Genetics achieved the lowest mean fitness values across the tested benchmark functions, with several cases producing mean errors close to zero, such as and It also obtained a lower mean value on the Schwefel function than both baseline methods, indicating better exploration in a complex multimodal landscape. These findings provide descriptive numerical evidence that genetic regeneration can improve PSO search performance by enhancing exploration while maintaining exploitation-oriented swarm guidance.
Comparison of SARIMA Method, Holt-Winters Exponential Smoothing Method and Prophet Method in Inflation Data Forecasting Atika Ratna Dewi; Desty Mayang Pratiwi; Aina Latifa Riyana Putri
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp165-180

Abstract

This study discusses inflation forecasting in Indonesia using three time series methods, namely SARIMA, Holt-Winters Exponential Smoothing and Prophet, with monthly inflation data from January 2014 to October 2024. Inflation forecasting is important to maintain economic stability and support decision making in the monetary, fiscal, and investment sectors. The SARIMA method was chosen because of its ability to handle complex seasonal data, Holt-Winters Exponential Smoothing is used to accommodate seasonal patterns through alpha, beta, and gamma smoothing parameters, while Prophet was chosen because of its flexibility in handling nonlinear trends, seasonality, and special events such as holidays. The research steps include literature study, data collection, exploratory analysis, preprocessing, modeling with the three methods, and accuracy evaluation using Mean Absolute Percent Error (MAPE). The evaluation results show that the SARIMA(2,1,2)(1,0,1)^6model has a MAPE of 8.11%, better than Holt-Winters Exponential Smoothing of 11.75% and Prophet of 52.85%. Thus, SARIMA was chosen as the best model to forecast Indonesian inflation from November 2024 to April 2025. The prediction results were 6.19%, 5.40%, 4.96%, 4.94%, 4.57%, and 4.58%, respectively. This model is expected to be a reference in formulating strategic policies to maintain economic stability and improve public welfare.
Bayesian Hierarchical Lognormal Modeling of Dengue Incidence with Area-Specific Temporal Effects Erwan Setiawan; Anang Kurnia; Kusman Sadik
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp139-152

Abstract

This study presents the development and validation of a Bayesian hierarchical model to estimate the incidence rate of dengue fever (DF) in West Java, Indonesia. Bayesian hierarchical models offer powerful tools for handling uncertainty and regional heterogeneity, yet their implementation remains challenging—especially in complex datasets with multilevel structures. The proposed model incorporates both random intercepts (for regencies/cities) and random slopes (for year), with various prior distribution scenarios tested to ensure robustness. Among the tested predictors, population density was found to significantly influence DF incidence. Model performance evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) yielded values of 31.26 and 48.77, respectively, indicating good predictive accuracy. This research highlights the effectiveness of hierarchical Bayesian modeling for epidemiological analysis and contributes to more targeted public health strategies in dengue-endemic regions
Analysis of Passenger Flight Distance as an Indicator of Economic Activity Ihsan Fathoni Amri; Suci Izzati; Rendi Andika Putra; Iva Aurellia Khalif; Febryana Dilla Setyaningrum; Isnaini Maulida; M. Al Haris
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp111-124

Abstract

Understanding macroeconomic dynamics in the United States requires advanced forecasting techniques capable of capturing both seasonal structures and external shocks. This study investigates the relationship between passenger flight distance and the unemployment rate through the implementation of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model—an enhancement of the SARIMA framework. While SARIMA accounts for autoregressive, differencing, and moving average components with seasonal integration, SARIMAX further augments this structure by incorporating exogenous predictors, enhancing explanatory and predictive power. Monthly time series data from 2015 to 2024 were utilized, with flight distance as the endogenous variable and the unemployment rate as the exogenous regressor. The modeling procedure involved rigorous stationarity testing via the Augmented Dickey-Fuller (ADF) test, model selection using the Akaike Information Criterion (AIC), and residual diagnostics employing the Box–Ljung and Shapiro–Wilk tests. SARIMAX(0,1,0)(0,1,1)[12] + X emerged as the optimal specification, with all parameters statistically significant and a MAPE of 3.68%, denoting excellent forecast accuracy. Empirical findings reveal a significant and negative association between unemployment and air travel activity, emphasizing the role of labor market dynamics in shaping mobility trends. These results reinforce the utility of SARIMAX as a robust tool in macroeconomic forecasting and evidence-based policy formulation.
ARIMA and ARIMAX Modeling for Forecasting the Value of Non-Oil and Gas Exports Alfisyahrina Hapsery; Artanti Indrasetianingsih; Saputri Dyah Pratiwi
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp195-206

Abstract

Non-oil and gas exports are the activity of selling commodities other than oil and gas abroad. Non-oil and gas exports are able to provide foreign exchange for the country, which can create jobs, increase competitiveness, and improve economic growth. The country that can create jobs, increase competitiveness and overall economic growth. So that this sector becomes the main focus of a country's economic policy. West Java is the province with the highest non-oil and gas exports value. In every year, the value of non-oil and gas exports has decreased. This is suspected to be due to the Eid al-Fitr holiday, where the decline is always there every year but in different months. So it is necessary to do statistical analysis to predict the value of non-oil and gas exports in West Java using the ARIMA and ARIMAX methods. West Java using ARIMA and ARIMAX methods with the effect of calendar variation as a dummy variable. The best model criteria are based on the smallest RMSE and MAPE values. The ARIMA model provides a better level of accuracy to predict the value of non-oil and gas exports with an RMSE value of 175176.59, MAE value of 130034.6, and MAPE of 4.09%.
Clinical Factor Analysis and Comparison of Heart Failure Patient Prediction Models Using Logistic Regression and XGBoost Novri Suhermi; Rahida R. Aisy; Auriga Wiradhiani; Edvina Kresnaningrum; Fathin Sahirah; Faza Inayatulloh; Grahsaro Y. Teduhati; Hollyviar R. P. Zalukhu; Muhammad R. Insan; Regytha P. Ayuningtyas; Yoel P. Simamora; Linda D. Rahmawati; Zelika A. Rachman; Syahwalia Asacha
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp87-110

Abstract

Heart failure is a serious chronic condition and a leading cause of death globally. Early detection of mortality risk among heart failure patients remains a challenge due to the complexity of clinical data. This study aims to identify the most influential clinical factors associated with patient mortality and to compare the performance of two classification models,Logistic Regression and Extreme Gradient Boosting,in predicting death risk. The dataset includes clinical and demographic variables of heart failure patients.Key predictors identified include serum creatinine, ejection fraction, time, and age, which are clinically associated with kidney function, cardiac output, and treatment progression. These features were selected based on their relevance and contribution to the model’s predictive performance. Model performance was evaluated using accuracy, precision,recall, F1-score, and AUC. Results indicate that XGBoost slightly outperformed Logistic Regression in terms of accuracy (85%) and recall (63%) compared to Logistic Regression (83% and 58%). However, Logistic Regression achieved a higher AUC (0.88) and showed more consistent results between training and testing data. Its interpretability also makes it more appropriate for clinical applications. This study underscores the potential of data-driven approaches in enhancing risk stratification and guiding early interventions in heart failure management.
Feasibility Regions and Critical-Path Uniqueness in Inverse Project Scheduling Using Multilayer Acyclic Digraph Models Robby Robby; Levina Michella; Yanuar Bhakti Wira Tama
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp125-138

Abstract

This paper examines an inverse project scheduling problem formulated using the Critical Path Method (CPM), in which one activity durations are unknown. The objective is to derive analytical conditions that ensure a given project duration is feasible and that a particular path becomes the unique critical path. The project workflow is represented as a multilayered acyclic digraph, which facilitates symbolic analysis of all critical path candidates. A numerical example is implemented in Python on a six-layer network with two nodes per inside layer and one unknown duration. From an initial set of 16 possible paths, only 4 remain after slack-based pruning, enabling symbolic characterization of the feasibility region. The findings contribute to a deeper understanding of structural conditions that guarantee critical path uniqueness in inverse project scheduling problems.
Factors Associated with Waste Management Behavior in Coastal Communities: Evidence from Binary Logistic Regression Amanda Adityaningrum; Muhammad Rezky Friesta Payu; Bela Silfana
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp153-164

Abstract

Many communities continue to struggle with waste management, with improper handling still common, including in Gorontalo. To support effective waste management planning and intervention, it is crucial to identify factors influencing waste management behavior. The purpose of this study was to examine factors associated with waste management behavior using a binary logistic regression model. The study included 347 respondents in the South Leato Coastal Area, Dumbo Raya District, Gorontalo City, Gorontalo Province. It focused on attitude and waste management facilities as independent variables, as well as waste management behavior as a binary outcome. According to the likelihood ratio test, the logistic regression model was statistically significant (p-value < 0.05). As a result of the study, attitudes and waste management facilities were significantly associated with waste management behavior in the South Leato Coastal Area. People with a positive attitude were more likely to exhibit good waste management behavior than those with a negative attitude, and those with adequate waste management facilities were more likely to do so. Based on the Hosmer-Lemeshow test, the model fits the data well (p-value > 0.05).
Application of Multivariate Singular Spectrum Analysis for Forecasting the Production of Plantation Commodities Sintia Agustina Siregar; Rina Filia Sari; Rina Widyasari
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp59-74

Abstract

This study aims to determine the production forecast of plantation commodities in North Sumatera Province, including palm oil, rubber, coffee, cocoa, and tobacco. Therefore, a forecasting method that can capture patterns and interrelationships between variables simultaneously is needed. This study applies the Multivariate Singular Spectrum Analysis (MSSA) method in forecasting the production of several major plantation commodities in North Sumatra Province. The results obtained from the MSSA analysis stage are a window length (l) of 2 and r-grouping of 2 with a forecasting period length of 2 periods in chronological order. The forecasting results based on the forecasting accuracy level using MAPE for the production of palm oil, rubber, coffee, cocoa, and tobacco in North Sumatra Province using the Multivariate Singular Spectrum Analysis method are 3.57223%, 3.95038%, 6.92317%, 3.03589%, and 3.03589%. Based on the MAPE accuracy category for each variable, the MAPE values are < 10% and fall into the accurate category for forecasting.
Parameter Estimation of Partial Spline Regression Using Weighted Least Squares with Moving Average Approach Jiran Julita; Jerry Dwi Trijoyo Purnomo; I Nyoman Budiantara
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp207-2018

Abstract

Regression analysis is a statistical method used to model the relationship between predictor variables and a response variable. In analyzing stunting prevalence, conventional parametric approaches are often limited in capturing complex and nonlinear patterns in the data. Therefore, a semiparametric regression approach is used, as it combines parametric and nonparametric components, providing greater flexibility in modeling relationships between variables. The truncated spline method is applied to accommodate nonlinear patterns, particularly in the variable of access to proper sanitation. This study aims to model the prevalence of stunting in Indonesia using a truncated spline semiparametric regression approach, with the percentage of poor population as the parametric component and access to proper sanitation as the nonparametric component. Parameter estimation is conducted using Ordinary Least Squares (OLS) and improved using Weighted Least Squares (WLS) with a moving average approach to address heteroscedasticity. The optimal model is selected based on the minimum Generalized Cross Validation (GCV) value. The results show that the best model is obtained with order 1 and one knot point at 93.884, producing the minimum GCV value of 25.19599. The WLS approach improves the model performance, increasing the coefficient of determination (R²) from 53.25% to 74.40%, and successfully overcomes heteroscedasticity issues. The analysis also indicates that the percentage of poor population has a positive effect on stunting prevalence, while access to proper sanitation has a negative and nonlinear effect, with a stronger impact after exceeding the knot point. These findings indicate that the truncated spline semiparametric regression model with WLS estimation provides a better and more reliable approach in modeling stunting prevalence.

Page 11 of 11 | Total Record : 110

 7   8   9   10   11 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 3 No 2 (2024): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 3 No 1 (2024): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 2 No 2 (2023): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 2 No 1 (2023): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 1 No 2 (2022): Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 1 No 1 (2022): Parameter: Jurnal Matematika, Statistika dan Terapannya