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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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Comparison of Random Forest and XGBoost Methods for Predicting Work Accident Claim Reserves Anugrah, Sri Ayu; Anugrawati, Sri Dewi; Sauddin, Adnan; Mariani, Andi
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/parameterv4i3pp497-508

Abstract

The potential high claim burden in the work accident insurance sector managed by BPJS Ketenagakerjaan have an impact on the company’s financial stability. This encourages insurance companies to provide additional funds to maintain the company’s operational sustainability. Thus, preparing future fund reserves is a crucial step in risk and financial management to minimize payment delays, up to the risk of default. This study aims to determine the best method for predicting work accident claim reserves by comparing the Random Forest and XGBoost methods. The result of the analysis shows that the XGBoost method has an outstanding ability to predict work accident claim reserves on BPJS Ketenagakerjaan in the period July 2016 to August 2023, with a MAPE of 5.14% and an accuracy rate of 94.86%.
Numerical Solution of Drug Addiction Mathematical Model Using Adams Moulton Method Maulida, Nurul Ismi; Side, Syafruddin; Syam, Rahmat
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/parameterv4i3pp521-536

Abstract

Rug abuse is one of the major social issues in Indonesia, particularly in South Sulawesi. This study aims to develop a mathematical model that represents the dynamics of drug addiction in the population and to solve the model numerically using the Adams-Moulton method. The model applied is theSIIHRcompartment model, which segments the population into five groups: susceptible (S), light addict (IR), heavy addict (IB), in rehabilitation (H), and recovered (R). The analysis results indicate that the basic reproduction number푅₀is less than 1, suggesting that drug addiction in the population can be managed. Numerical simulations are conducted using the fifth-orderRunge-Kuttamethod for initial values, followed by theAdams-Bashforth-Moultonmethod as the numerical solution technique. The model demonstrates stability at the drug-free equilibrium point and consistency between the outcomes of mathematical and numerical analysis.
Forecasting the Inflation Rate Using Long Short-Term Memory Model Based on Consumer Price Index Limba, Syella Zignora; Hapsari, Nimas Ayu; Anggraini, Yenni; Notodiputro, Khairil Anwar; Maulifah, Laily Nissa Atul
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/parameterv4i3pp537-550

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.
Optimization of Assignment Problems in Private Class Scheduling Using Graph Application Wati, Hanifah Felisia; Wahyuningsih, Sapti; Ramadhan, Muhammad Nur
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/parameterv4i3pp409-428

Abstract

The tutoring institution PT. Inspirasi Mandiri Nusantara (PINTARA) provides various types of learning services for students of various levels. The services offered include regular, intensive, exam preparation, and private classes. Private class services face scheduling problems due to the limited number of tutors and the mismatch between the availability of tutors and the subjects offered. This article discusses the optimization of tutor assignments using the maximum matching algorithm on bipartite graphs and the Hungarian algorithm. The study uses a mathematical approach and data is obtained through direct observation and modeled in the form of graphs, then solved with the Python program tool. The results show that optimal assignments can be achieved using the maximum matching algorithm, the Hungarian algorithm, and the Python program tool with the same and optimal values. This approach has proven effective and can be the basis for the development of an automatic scheduling system in the future.
Analysis and Prediction of Turbidity Level of Water Based on Ammonia Substance Using Random Forest and K-Nearest Neighbor Herlambang, Teguh; Azmi, Mohd Sanusi; Othman, Zuraini Binti; Arief, Mochammad Romli
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/parameterv4i3pp429-440

Abstract

Water is the primary need for human survival. The need for clean water is very important, both in a household scale and in an industrial scale. The clean water used and consumed by the community comes from river water processed and distributed by the Regional Drinking Water Company (PDAM). Water conditions before being treated and distributed contain various harmful substances if not purified, one of which is ammonia. Currently, with the development of information technology, especially in the field of machine learning and data analysis, the process of predicting the content of ammonia substances in water is becoming increasingly facilitated. Machine learning has provided a number of scientific prediction methods that can be used. In this research, the methods used were Random Forest Regression and K-Nearest Neighbor (KNN) methods are used to predict turbidity level of water in PDAM Surya Sembada Surabaya. This research aims to compare robbustness and accuracy of prediction models. The Random Forest method produced the best prediction error value of 0.0934, while the K-Nearest Neighbor (KNN) method produced the best prediction error value of 0.0918.
Time Series Clustering of Rice Productivity Using Trimming Gaussian Mixture Models Fadhlia, Sarah; Hendri, Eko Primadi
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/parameterv4i3pp381-394

Abstract

This study investigates the application of the Trimming Gaussian Mixture Model (TGMM) for clustering monthly rice productivity time series data in West Java from 2018 to 2023. TGMM is a robust clustering approach that reduces the influence of outliers by trimming a specified portion of the data prior to parameter estimation. The dataset, sourced from Open Data Jabar, was analyzed to identify the most representative number of clusters using the Silhouette Score. The optimal clustering solution was achieved with two main clusters (k = 2) and a trimming proportion of 15%. The results revealed three distinct regional groups: two dominant clusters characterized by moderate-stable and high-consistent productivity patterns, and a separate group of outliers marked by low and highly fluctuating productivity. Cluster stability was assessed using the Adjusted Rand Index (ARI), yielding values of 0.41 (bootstrap) and 0.545 (subsampling), which indicate a reasonably consistent clustering structure. These findings demonstrate the effectiveness of TGMM in capturing underlying productivity patterns while accounting for noise and outliers, suggesting its potential as a robust decision-support tool for data-driven agricultural planning and policy formulation.
Modeling Illiteracy Rate in Indonesia with Spatial Regression Balda, Alvizar Syamsul; Andriani, Parhaini; Astuti, Alfira Mulya
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/parameterv4i3pp575-590

Abstract

The illiteracy rate (ILR) serves as an indication of educational attainment, representing the percentage of individuals aged 15 and older who lack reading and writing skills. Despite a reduction in Indonesia's ILR to 3.33% in 2024, the objective of eliminating it entirely remains a priority to fulfill the fourth aim of The Sustainable Development Goals (SDGs) by 2030. This research seeks to estimate Indonesia's rate of illiteracy by examining relevant elements, including the number of individuals living in poverty, mean years of schooling, and gross enrollment ratio. The data is obtained from the BPS-Statistics Indonesia. This study employs spatial regression, utilizing an area-based methodology to capture spatial impacts among regions and analyze them with R software. The analysis results indicate that a) the chosen weighted matrix is k-Nearest Neighbor, b) the selected spatial model for the illiteracy rate in Indonesia is the Spatial Durbin Model (SDM), and c) mean years of schooling and gross enrollment ratio within a province significantly affect the illiteracy rate in that province, which may indirectly elevate the illiteracy rate in neighboring regions.
Modeling of Life Expectancy Index in West Nusa Tenggara Province Using Spatial Panel Regression Apriana, Baiq Nurul; Astuti, Alfira Mulya; Fahrudin, Fadrik Adi
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/parameterv4i3pp563-574

Abstract

One important indicator for evaluating the well-being and standard of living of people in a certain area is the life expectancy index. The goal of this study is to model the life expectancy in the Province of West Nusa Tenggara (NTB). The number of impoverished individuals, adjusted per capita spending, and the average number of years of education are the independent variables used. The data comprised a panel from 10 districts/cities in NTB for the year 2019–2023, obtained from BPS-Statistics NTB Province. The applied analytical method was spatial panel regression, utilizing queen contiguity and a customized weighted matrix based on transportation routes. According to the analysis, a) the spatial autoregressive panel model (SARFEM) was chosen as the model to study life expectancy in NTB; b) life expectancy in one region is directly impacted by life expectancy in adjacent regions; and c) a region's life expectancy is significantly impacted by its mean years of education and adjusted per capita expenditure, both of which can raise life expectancy in nearby regions. This link underscores the necessity of enacting policies that prioritize advancements in education and economic conditions in a particular location, while also taking into account the wider geographical context, to promote general well-being and life expectancy in adjacent regions.
Application of Hierarchical Cluster Analysis for Sub-District Grouping Based on Plantation Production Latar, Egis Natasantika; Pusung, Yulia Betania; Wael, Nurvia Imran; Maspaitella, Paskalina; Gaitian, Joni; Wattimanela, Henry Junus
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/parameterv4i3pp441-458

Abstract

Plantation production is the result of plantation cultivation which includes the of commodities such as coconut, cloves, nutmeg, cocoa, and coffee which have a high sales and economic value and are good for income, as well as being the main source of income for the community's life. Central Maluku Regency is one of the areas in Maluku Province that has great potential in the plantation sector, but the production level between sub-districts shows considerable variation due to differences in geographical conditions, infrastructure, and market access. This study aims to group sub-districts in Central Maluku Regency based on plantation production using a hierarchical cluster analysis approach. Distance measurement uses Single linkage, Average linkage, Complete linkage, and Ward linkage methods. In addition, descriptive analysis, standardization, KMO tests, and multicollinearity tests were carried out with the data used is in the form of plantation production data per sub-district in 2024 sourced from the Central Statistics Agency. Data processing using Microsoft Excel, SPSS, and R applications. The results of the study showed the formation of several sub-district clusters with similar production characteristics, where the Average linkage and Complete linkage methods with the number of clusters as many as three produced the best grouping with the highest Silhouette Score value of 0.477. Each cluster shows the basis for making policies for the development of the plantation sector. These results are expected to be the basis for local governments in formulating plantation development policies that are more targeted and effective.
Spatial Interpolation of the Probability of Mercury Threshold Exceedance Using Indicator Kriging Zhahirulhaq, Mufdhil Afta; Sirodj, Dwi Agustin Nuriani
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/parameterv4i3pp395-408

Abstract

Indicator Kriging (IK) is a spatial interpolation method used to estimate the probability that a variable exceeds a specified threshold. This study applies IK to assess the probability of mercury (Hg) concentrations exceeding environmental thresholds in river systems across DKI Jakarta. Given the skewed and non-normally distributed nature of mercury data, IK was selected due to its robustness in handling non-parametric data and its sensitivity to extreme values. Mercury concentration measurements were first transformed into binary indicator data based on a predefined threshold. An experimental semivariogram was then computed to analyze the spatial dependence of the indicator values, followed by the fitting of theoretical semivariogram models (Gaussian, Spherical, and Exponential). The best-fitting model was selected using the Leave-One-Out Cross-Validation (LOOCV) approach, with the Spherical model yielding the lowest root mean square error (RMSE). The final probability map generated through IK reveals five unsampled locations with a probability greater than 0.5 of mercury concentration exceeding the threshold: two located along the Ciliwung River and three along the Sunter River. These findings highlight critical zones requiring monitoring and support the use of IK as an effective geostatistical tool for environmental risk assessment of heavy metal contamination in urban river systems.

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