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INDONESIA
Jurnal Statistika dan Matematika (Statmat)
Published by Universitas Pamulang
ISSN : 26553724     EISSN : 27209881     DOI : 10.32493
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
P-ISSN : 2655-3724 E-ISSN : 2720-9881 Jurnal Statmat UNPAM: Jurnal Statistika dan Matematika Universitas Pamulang is a means of publication of scientific articles and research with concentrations of Statistics, Pure Mathematics, Applied Mathematics, Computational Mathematics, Educational Mathematics, and other research articles related to Statistics and Mathematics. Mathematics Department, Faculty of Mathematics and Natural Sciences, University of Pamulang publishes this journal, since 2019, which scheduled periodically every six months (twice a year).
Arjuna Subject : Matematika - Matematika Terapan
Articles 150 Documents
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Classification of Pension Benefit Adequacy for Civil Servants in Pontianak City Using the C4.5 Algorithm Indah Maharani, Dwi; Tamtama, Ray
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.54960

Abstract

The development of information technology has encouraged the use of data-driven analysis to support decision-making in the governmental sector, including within the civil service pension system. This study aims to classify the adequacy of pension benefits received by retired Civil Servants in Pontianak City using the C4.5 algorithm. Pension benefit adequacy is determined by comparing the amount of basic pension received with household expenditures, which are calculated using the average monthly per capita expenditure in Pontianak City. The dataset consists of 322 retirees with variables including retirement age, years of service, last education, rank, and number of dependents. The model was developed using several training–testing data proportions, namely 70:30, 75:25, and 80:20. Model evaluation was conducted using accuracy, sensitivity, and specificity based on the confusion matrix. The results show that the 80:20 proportion produces the best model, achieving 100% accuracy, 100% sensitivity, and 100% specificity. The generated decision tree indicates that the most influential variable is the number of dependents, followed by rank and years of service. These findings suggest that household expenditure burdens and employment characteristics play a crucial role in determining pension adequacy. The resulting model is expected to assist pension management institutions in formulating data-driven policies to improve the welfare of retirees.
Clinic of Mathematics with the PPLAM Approach: Efforts to Improve Students’ Mathematics Learning in Manokwari Lubis, Loria Amisah; Trigarcia Maleachi Randa; Esther Ria Matulessy; Chrisaria Palungan; Dahlia Gladiola Rurina Menufandu
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.54979

Abstract

This study aims to analyze the effectiveness of the Mathematics Clinic in overcoming the difficulties of seventh-grade junior high school students in integer operations. The research focused on improving students’ learning outcomes through a short learning intervention consisting of a pretest, structured practice, and a posttest conducted within a single day. The method employed was a quasi-experimental design with a one-group pretest–posttest dengan Embedded Learning Analytics (PPLAM). The subjects were 25 seventh-grade students purposively selected based on their identified difficulties in learning mathematics. Data were collected through achievement tests (pretest and posttest) and students’ practice records. The data analysis included a paired t-test to measure the difference between pretest and posttest scores, the calculation of Normalized Gain (N-Gain) to assess the effectiveness of improvement, and effect size to determine the magnitude of the intervention’s impact. In addition, a simple learning analytics approach was applied to examine the relationship between practice quality and posttest results. The findings revealed a significant improvement in students’ learning outcomes with . The mean N-Gain score of 0.578 indicated a medium level of improvement, while the effect size (Cohen’s d = 2.164) was categorized as very large. Furthermore, learning analytics analysis showed an R2 = 0,70 suggesting that practice quality positively contributed to students’ posttest performance. It can be concluded that the Mathematics Clinic is effective as an alternative learning strategy to address difficulties in integer operations, even when implemented within a short time frame.
Forecasting The Number of Rainy Days in Sorong City Using The Autoregressive Moving Average Method Kailola, Juan
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.55122

Abstract

A high number of rainy days can lead to disasters and economic losses, especially in sectors such as agriculture, transportation, and infrastructure. Therefore, forecasting the number of rainy days is essential as a preventive measure against potential adverse impacts. Sorong City is one of the regions in Indonesia that experiences significant rainfall throughout the year. This study aims to forecast the monthly number of rainy days in Sorong City using the Autoregressive Moving Average (ARMA) method. The data used in this study consist of the monthly number of rainy days recorded at the Meteorological Station of Sorong City from July 2017 to December 2024. The dataset from July 2017 to August 2024 was used to build the ARMA model, while the data from September to December 2024 served as the testing data to evaluate forecasting accuracy. The results show that the best model is ARMA (1,0) with MAPE of 22.56%. The forecast indicates that the number of rainy days from January 2025 to June 2026 remains stable at approximately 20 days per month.
Forecasting Food Industry Enterprises in Ciamis Using Holt Winter Linear Trend Method Permana, Irfan; Rahmatuloh, Alam
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.55211

Abstract

The food industry sector in Ciamis Regency plays an important role in driving regional economic growth and employment. Understanding its future trend is essential for supporting policy formulation and industrial development strategies. To predict the number of food sector businesses in Ciamis Regency, this study uses the Holt-Winters Linear Trend method. The number of food industry enterprises has shown fluctuating and declining patterns in recent years, raising the question of how accurately the Holt-Winters Linear Trend method can predict future non-seasonal trends. This study provides a new application of the Holt-Winters Linear Trend method for forecasting non-seasonal industrial data at a regional level, an area rarely explored in previous research on small-scale industries. The method was implemented in Python, and performance was evaluated using MAE, MSE, RMSE, MAPE, and R². The forecast shows that the number of food industry enterprises will reach 92 units in 2026, 77 in 2027, 89 in 2028, 75 in 2029, and 86 in 2030, with an accuracy of 93.40%. For future studies, ensemble forecasting is recommended, with related variables such as labor numbers and production value added to enhance method performance.
Klasterisasi Pendapatan Nasional dan Pola Konsumsi Negara-Negara G20 Tahun 2023 Menggunakan Metode K-Means Fatta, M Fatta Arya Irwanda; Ridho Saputra; M. Allif Khair; Fadhillah Fitri
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.55980

Abstract

The G20 countries are often treated as a relatively homogeneous group in macroeconomic analysis, despitesubstantial differences in income levels, economic growth, and consumption patterns among member countries.This study aims to classify G20 countries based on national income, economic growth, and consumptionindicators in order to identify structural differences in their economic characteristics. The analysis employsthe K-Means clustering method using standardized data to ensure comparability across variables with differentmeasurement scales. Prior to clustering, data standardization is applied using the Z-score method. The optimalnumber of clusters is determined using a cluster validity measure, and the clustering process is performedusing Euclidean distance. The results indicate that the optimal clustering structure is achieved with threeclusters. The K-Means algorithm successfully groups G20 countries into three distinct clusters with clearlydifferentiated economic profiles. The centroid analysis reveals that each cluster exhibits unique characteristicsin terms of income level, growth dynamics, and consumption patterns, allowing for objective and data-drivencluster categorization. The findings also show that higher income levels are not always associated with higherconsumption patterns, and that developing economies tend to form a separate cluster characterized byrelatively higher economic growth. The evaluation of cluster quality indicates good cohesion within clustersand clear separation between clusters, suggesting that the clustering results are valid and reliable. Overall,this study demonstrates that cluster analysis provides an effective framework for capturing the heterogeneityof economic structures among G20 countries. The findings contribute to a more nuanced understanding ofglobal economic diversity and may serve as a basis for comparative economic analysis and policy orienteddiscussions.
Critical Path Identification and Schedule Rationalization in a Plantation Road Improvement Project: Case Study at PTPN IV, Batanghari Kurniawati, Putri; Rarasati, Niken
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.58258

Abstract

Keterlambatan dalam proyek infrastruktur, khususnya pembangunan jalan perkebunan, seringkali disebabkan oleh penjadwalan dan alokasi sumber daya yang kurang optimal. Studi ini bertujuan untuk mengoptimalkan waktu dan biaya dalam proyek perbaikan jalan sepanjang 2.600meter di PTPN IV Regional 4, Perkebunan Batanghari, menggunakan Metode Jalur Kritis (Critical Path Method/CPM). Data primer dikumpulkan melalui wawancara semi-terstruktur dengan staf proyek, sedangkan data sekunder diperoleh dari dokumen proyek resmi, yang mencakup durasi aktivitas, biaya, dan hubungan prioritas. Diagram jaringan dibangun menggunakan pendekatan Activity on Arrow (AOA), diikuti oleh analisis maju dan mundur untuk menghitung waktu mulai/selesai paling awal/paling lambat dan total float. Hasil menunjukkan bahwa jadwal dasar yang dimulai adalah 120 hari dengan total biaya Rp 1.075.197.832, namun analisis CPM mengungkapkan durasi minimum yang layak yaitu 117 hari, mengurangi total biaya proyek menjadi Rp 1.069.692.832 atau penghematan sebesar Rp 5.505.000. Pengurangan ini terutama disebabkan oleh penghapusan jadwal kelonggaran selama 3 hari, yang menurunkan biaya tenaga kerja (Rp 1.835.000/hari × 3 hari). Jalur kritis terdiri dari aktivitas A, B, D, E, F, G, H (total kelonggaran = 0), sedangkan aktivitas C (mobilisasi/demobilisasi) menunjukkan kelonggaran 2 hari dan karena itu tidak kritis. Studi ini menegaskan bahwa CPM tidak hanya menentukan aktivitas yang penting untuk waktu tetapi juga memungkinkan efisiensi biaya waktu yang terukur, mendukung pengendalian proyek yang lebih responsif dalam pengembangan infrastruktur perkebunan.  
Modeling Term Life Insurance Premiums Using Monte Carlo Simulations with Stochastic Interest Rates Based on The Indonesia Mortality Table IV Lubis, Mery Christyn; Tampubolon, Bungaria; Tarigan, Febry Vista Kristen
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.59229

Abstract

This study addresses the limitation of deterministic interest rate assumptions in actuarial premium calculations, which may lead to inaccurate pricing, particularly for long-term life insurance products. In practice, interest rates are influenced by economic conditions and exhibit stochastic behavior, making fixed-rate assumptions less realistic. Therefore, incorporating interest rate uncertainty is essential to obtain more accurate and reliable premium estimates. The objective of this study is to determine the net premium of term life insurance by integrating stochastic interest rates using the Vasicek model and Monte Carlo simulation. A quantitative approach is employed using actuarial modeling based on the Indonesian Mortality Table IV (TMI IV) and the equivalence principle. Interest rates are modeled as a mean-reverting stochastic process, and Monte Carlo simulation with 10,000 iterations is applied to estimate the distribution of premiums under uncertainty. The results show that premiums increase significantly with higher entry age and longer coverage periods, reflecting increased mortality risk and longer exposure to uncertainty. Male premiums are consistently higher than female premiums due to higher mortality probabilities. The premium distribution is approximately normal, with increasing variability observed for longer-term policies. Sensitivity analysis indicates that the long-term mean interest rate parameter has the strongest influence on premium values, while volatility mainly affects the dispersion of premiums rather than their expected value. Overall, the stochastic approach provides a more realistic and comprehensive framework compared to deterministic methods, as it captures both expected values and uncertainty. The findings of this study can support more accurate pricing, improved risk management, and better decision-making in the life insurance industry, particularly in the Indonesian context.
Analysis of Life Insurance Underwriting Risk Classification Using Ordinal Logistic Regression and XGboost Susanty, Wenny; Dewi Fortuna Silaban
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.59268

Abstract

The underwriting process in life insurance is a critical step in determining the risk classification of prospective policyholders, which impacts premium setting and the company’s sustainability. This study aims to analyze underwriting risk classification using the Ordinal Logistic Regression and XGBoost methods. The data used is the Prudential Life Insurance Assessment dataset, consisting of 59,381 training data points and 19,765 test data points with over 120 variables. The research methodology includes data preprocessing, variable selection using XGBoost, and modeling using Ordinal Logistic Regression and XGBoost. Model evaluation was conducted using the accuracy metric and Quadratic Weighted Kappa (QWK). The results indicate that variables related to health conditions and medical history, such as Medical_History, Medical_Keyword, and BMI, have a significant influence on risk classification. The Ordinal Logistic Regression model offers an advantage in interpreting relationships between variables, while XGBoost demonstrates fairly good classification performance with an accuracy of 0.568 and a QWK of 0.540. Overall, this study demonstrates that a combination of statistical and machine learning approaches can support a more effective underwriting process in the life insurance industry.
Forecast of Farmers' Exchange Rate in Jambi Province Using the Arima Method (Autoregressive Integrated Moving Average) Agustina, Tiah; Gusmanely. Z
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.59309

Abstract

Changes in the NTP over time indicate the variations in agricultural product prices and the costs of production that farmers deal with. This research intends to predict the Farmer Exchange Rate for Jambi Province using the ARIMA technique. The data utilized consists of time series information on the NTP of Jambi Province stretching from January 2021 to May 2025. The analysis process involves checking for data stationarity via the ADF test, performing Box-Cox transformation, differencing, identifying models using ACF and PACF charts, estimating parameters, and conducting model diagnostics. The optimal model was determined based on the MAPE evaluation. The findings indicated that the ARIMA (0,2,1) model was the most effective, exhibiting a lower MAPE value than other models, indicating strong forecasting accuracy. This model was subsequently employed to predict the NTP for Jambi Province for the period from June to October 2025. The results from these predictions reveal a rising trend in NTP values, suggesting a possibility for enhanced farmer welfare in Jambi Province. It is anticipated that this study will provide useful insights for the government and relevant stakeholders.
Comparative Analysis of Mortality Rates and Whole Life Insurance Premiums Based on the Indonesian Mortality Table IV (TMI IV) Banjarnahor, Riski; Lumbantobing, Imelda Octavia; Sianturi, Michael Dolly
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.59311

Abstract

This study aims to analyze the comparative mortality rates and whole life insurance premiums between males and females based on the Indonesian Mortality Table IV (TMI IV), as well as to examine the phenomenon of risk convergence at older ages and the implications of interest rates on premium sensitivity. This study employs a quantitative approach using actuarial simulation methods. The data used are secondary data from TMI IV published by the Indonesian Life Insurance Association (AAJI) for the study period of 2013–2017. The net annual premium is calculated using the commutation function (Pₓ = Mₓ / Nₓ), assuming a sum assured of IDR 100,000,000 and a technical interest rate of 6%. The results indicate that male mortality rates are higher than those of females across productive ages to early elderly stages, as reflected in the female survival curve being consistently above that of males. At age 80, out of an initial cohort of 100,000 individuals, 62,171 males (62.2%) remain, compared to 70,207 females (70.2%). The annual premiums for whole life insurance are higher for males than for females across all age groups, with premium differences ranging from 19.3% to 27.2%. The phenomenon of mortality convergence is observed through the narrowing premium gap between genders as age increases. Sensitivity analysis shows that premiums have an inverse relationship with interest rates, where a 1% decrease in the technical interest rate can increase premiums by approximately 8–12%. This study concludes that TMI IV effectively represents current mortality dynamics in Indonesia and serves as a crucial foundation for actuarial liability calculations, while age and interest rates significantly influence the determination of whole life insurance premiums.

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