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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 95 Documents
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Implementation of Cluster Analysis on Districts/Cities in Banten Province Based on Factors Causing Stunting in Toddlers Mahuda, Isnaini; Rofiroh, Rofiroh; Fitriani, Meida
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp329-338

Abstract

Indonesia is a country in Southeast Asia that has the fifth highest prevalence of stunting in world and as a country that has a relatively high stunting rate. Banten Province, as one of provinces in Indonesia, is in the bottom five provinces with the worst stunting cases according to results of Indonesian Toddler Nutrition Status Survey (SSGBI) 2021. Many efforts have been made to resolve this stunting problem. The condition of stunting is usually characterized by the toddler's length or height being less than the normal toddlers of the same age. Several factors causing stunting can be identified so that the government can take appropriate steps to reduce stunting rates in Indonesia, especially in Banten province. Banten province consists of several districts/cities which have their own characteristics, so it is necessary to analyze the grouping of factors causing stunting based on districts/cities in Banten province using hierarchy cluster analysis. The cluster analysis method used in this research was Single Linkage method with Euclidian distance. The results of this research showed that two clusters were obtained based on the grouping results, where first cluster had a higher average percentage of babies born alive with Low Birth Weight (LBW) and percentage of average per capita monthly expenditure for food compared to second cluster. In other hand, percentage of households with access to adequate sanitation and drinking water in first cluster had smaller average value than second cluster.
The Influence of Region on Reading Habits in Indonesia: RM-MANOVA Analysis of Population Aged 5+ (2018) Febyanti, Iin; Safira Devi, Arsita; Nugraheni, Setiawati; Wardah, Salsabila; Nasrudin, Muhammad; Trimono, Trimono
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp275-286

Abstract

This study explores the reading habits of the Indonesian population aged 5 and above, focusing on differences between urban and rural areas. Using data from the 2018 BPS survey, the research examines the proportion of individuals who engaged in reading various materials in printed and electronic formats over the past seven days. A Repeated Measures Multivariate Analysis of Variance (RM MANOVA) was employed to assess the influence of regional factors on reading behavior. The results indicated significant disparities: urban populations tend to read a broader range of materials such as newspapers, magazines, and scientific texts, while rural populations focused more on textbooks and basic materials. These findings highlight the need for regionally tailored literacy strategies to ensure equitable access to reading resources across Indonesia.
Modeling Poverty Rates in Indonesia Using Spline Nonparametric Regression Hendayanti, Ni Putu N.; Nurhidayati, Maulida; Jayanthi, Luh Putu E. I.; Widyanti, Luh Made I. A.; Cahyadinata, I Kadek D.; Parwata, I Made Y.
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp311-328

Abstract

Poverty is a complex and multidimensional issue that remains a major focus in Indonesia as a developing country. Data show that the poverty rate in March 2024 reached 9.03%, exceeding the government’s target of 6.5–7.5%. This study aims to analyze the factors that influence poverty levels in Indonesia by using secondary data obtained from the Central Statistics Agency (BPS) in 2023. The analytical method employed is Spline Nonparametric Regression, which is considered appropriate for processing social and economic data that tend to be sparse and non-stationary, such as Indonesia’s poverty data. The results of this study indicate that among the four variables analyzed, only the Mean Years of Schooling has a significant effect on the percentage of the poor population in Indonesia. Other variables, namely GDP growth at constant prices, the open unemployment rate, and life expectancy, were not proven to have a significant effect on provincial poverty levels. The best model obtained uses Spline Nonparametric Regression with two knot points, as it has the smallest GCV value compared to the one-knot and three-knot models. Therefore, the findings of this study are expected to provide input for the government in formulating more targeted poverty alleviation policies, particularly through improvements in education.
Optimization of Holt's Double Exponential Smoothing Model with Levenberg-Marquardt Algorithm for Forecasting Farmer Exchange Rate Dama Yanti, Lisa; S. J. Saputra, Wahyu; Terza Damaliana, Aviolla
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp297-310

Abstract

The Farmer Exchange Rate (NTP) is an indicator of farmer welfare calculated from the ratio of prices received by farmers to costs incurred in farming. East Java is one of the provinces with the agricultural sector as the main pillar of the regional economy. However, the NTP in this region shows a fluctuating pattern with a certain trend that reflects the economic instability of the agricultural sector. This instability may lower farmers' purchasing power and threaten production sustainability. Therefore, accurate forecasting models are needed to support data-driven policy making. Holt's Double Exponential Smoothing (DES) is an effective method for analyzing trend-patterned data, as it captures both level and trend components through exponential smoothing. However, the model's accuracy heavily relies on selecting smoothing parameters, typically determined through a time-consuming trial-and-error process that may yield suboptimal results. This study proposes using the Levenberg-Marquardt algorithm to optimize parameter smoothing. The algorithm effectively combines the Gauss-Newton and Gradient Descent methods to minimize prediction error. The data included monthly NTP values in East Java from 2014 to 2024, sourced from BPS. The results showed that the model with optimized parameters has higher accuracy, with MAPE decreasing from 1.28% to 1.06%.
Categorical Boosting and Bayesian Optimization in Natural Disaster Tweet Classification Christina, Enzelica Vica; Saputra, Wahyu S. J.; Hindrayani, Kartika Maulida
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp339-352

Abstract

Multi-label classification is an important challenge in natural language processing, especially when a single text data point can have more than one label. This study applies a multi-label classification approach to group information in Twitter comments related to natural disasters in Indonesia. The data is categorized into six labels: disaster, location, damage, victims, aid, and others. To address the complexity of text data, the Categorical Boosting (CatBoost) algorithm is used, which is a decision tree-based boosting method that excels at handling categorical features and reducing overfitting. The model is built using the MultiOutputClassifier approach to handle multiple labels simultaneously. Additionally, Bayesian optimization is performed, which is a parameter search method that uses a probabilistic approach to select the best parameter combination based on previous evaluations. Optimization focused on four main parameters: number of iterations, learning rate, tree depth, and L2 regularization. The results showed that the model achieved an accuracy of 75.41% and a Hamming loss of 0.0520, demonstrating the effectiveness of this approach in handling multi-label classification on Twitter data.
Comparison of LASSO, Ridge, and Elastic Net Regularization with Balanced Bagging Classifier Nisrina Az-Zahra, Putri; Sadik, Kusman; Suhaeni, Cici; Mohamad Soleh, Agus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp287-296

Abstract

Predicting Drug-Induced Autoimmunity (DIA) is crucial in pharmaceutical safety assessment, as early identification of compounds with autoimmune risk can prevent adverse drug reactions and improve patient outcomes. Classification analysis often faces challenges when the number of predictor variables exceeds the number of observations or when high correlations among predictors lead to multicollinearity and overfitting. Regularization methods, such as Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic-Net, help stabilize parameter estimation and improve model interpretability. This study focuses on building a binary classification model to predict the risk of DIA using 196 molecular descriptors derived from chemical compound structures. To address class imbalance in the response variable, the Balanced Bagging Classifier (BBC) is combined with regularized logistic regression models. Elastic Net + BBC outperforms other models with the highest accuracy (0.825), followed closely by LASSO + BBC and Ridge + BBC (both 0.816). This integration not only improves classification accuracy but also enhances generalization and the reliable detection of minority class instances, supporting the early identification of autoimmune risks in drug discovery.
Indonesian Students Reading Literacy Score in Framework Hierarchical Data Structure Using Multilevel Regression Maya Santi, Vera; Rahayuningsih, Yuliana; Sumargo, Bagus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp353-368

Abstract

Education is essential for improving the quality of Indonesian society. Indonesia participated in the Programme International Students Assessment (PISA) survey to improve the quality of education. Based on the 2018 PISA survey data, Indonesia's reading literacy score has a hierarchical data structure, which means students at level 1 are nested by schools at level 2. The multilevel model is an appropriate approach to analyze such hierarchical structures. However, quantitative analysis of PISA data is still rarely carried out. This study aims to analyze the explanatory variables that significantly affect Indonesian students' reading literacy from the PISA survey using multilevel regression. This study examined student-level and school-level explanatory variables obtained from the Organization for Economic Cooperation and Development (OECD). Significant parameter tests revealed that, at the student level, factors such as socioeconomic status, teacher support in language learning, teacher-directed instruction, enjoyment of reading, perceived difficulty, competitiveness, mastery goal orientation, disciplined classroom climate in reading, general fear of failure, attitudes toward school, and perceived feedback significantly influence reading literacy. At the school level, school size was found to be a significant factor affecting reading literacy scores. Furthermore, the Intraclass Correlation Coefficient (ICC) indicated that schools accounted for 49% of the total variance.
Rainfall Characteristic and Prediction in Central Maluku Using Markov Chain Kais, Siti Djasmin; Tentua, Gilldo; Waas, Valencia; Potimau, Grace A.; Soulisa, Jihan A. S.; Wattimanela, Henry Junus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): 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/parameterv4i2pp369-380

Abstract

This study analyzes rainfall characteristics in Central Maluku Regency using Markov Chain methodology to model stochastic rainfall patterns. Monthly rainfall data from 2015-2024 were categorized into dry (<100 mm), normal (100-200 mm), and wet (>200 mm) conditions and processed using Microsoft Excel and R software. Results show rainfall conditions stabilize with long-term probabilities of 44.25% for wet, 32.89% for normal, and 22.87% for dry conditions. The system reaches steady state in 19 months, with 2025 predictions following this distribution. Findings support agricultural planning, disaster mitigation, and sustainable resource management in this climate-vulnerable archipelagic region.
Analysis of Premium Reserves in Whole Life and Term Life Insurance Using the New Jersey Prospective Method Husuna, Cabelita; Achmad, Novianita; Nuha, Agusyarif Rezka; Yahya, Nisky Imansyah; Ayyasy, Muhammad Yahya
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): 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/parameterv4i3pp509-520

Abstract

Human life is constantly exposed to risks such as illness, accidents, and death, which create financial uncertainties for individuals and families. Life insurance serves as an essential financial instrument to mitigate these risks by transferring potential liabilities to insurance companies. This study analyzes premium reserves for whole life and term life insurance using the New Jersey Prospective Method, applying a 6% interest rate and the 2023 Indonesian Mortality Table (TMPI) as the basis of calculation. Actuarial commutation functions are employed to compute annuity values, single net premiums, annual net premiums, and reserve allocations across different ages. The results indicate that reserve values increase with age, reflecting higher mortality risks, with whole life insurance showing a sharper escalation compared to term life insurance. The New Jersey Prospective Method demonstrates accuracy and consistency in reserve estimation, particularly by setting zero reserves in the first policy year, thereby supporting initial liquidity. These findings highlight the method’s effectiveness in maintaining financial stability and readiness of insurance companies to meet future claims and long-term obligations to policyholders.
Evaluating the Performance of Ordinal Logistic Regression and XGBoost on Ordinal Classification Datasets Hanifa, Jasmin Nur; Mingka, Rizka Annisa; Indahwati, Indahwati; Silvianti, Pika
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): 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/parameterv4i3pp459-470

Abstract

Choosing the appropriate classification model is crucial, especially when dealing with data featuring an ordinal dependent variable. This study explores and compares the performance of Ordinal Logistic Regression (OLR) and Ordinal XGBoost in classifying ordinal data using ten datasets obtained from the UCI Machine Learning Repository and Kaggle, which vary in the number of observations and features. Each dataset undergoes multicollinearity detection, an 80% training and 20% testing data split, and class balancing using SMOTE. Model performance is evaluated using metrics such as accuracy, F1-score, AUC, MSE, precision, and recall. The results show that ordinal XGBoost outperforms on datasets with complex structures and a higher number of features, achieving a maximum accuracy of 0.953. In contrast, Ordinal Logistic Regression demonstrates more stable performance on datasets with fewer features or balanced class distributions.

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