STATMAT: Jurnal Statistika dan Matematika
STATMAT: Jurnal Statistika dan Matematika Journal Name : STATMAT: Jurnal Statistika dan Matematika Publication Frequency : Three a year (April, August and December) DOI : 10.32493/sm P-ISSN : 2655-3724 E-ISSN : 2720-9881 Indexing : SINTA GOOGLE SCHOLAR GARUDA CROSSREF WORLDCAT Editor in Chief : Dr. Choirul Basir, S.Si., M.Si. Website : https://openjournal.unpam.ac.id/index.php/sm/index Email : sm@unpam.ac.id Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia STATMAT – Jurnal Statistika dan Matematika is a peer-reviewed journal published every four months (three a year), in April, August and December, by the Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia. The journal welcomes high-quality original research articles, review papers, and special topic contributions that address significant developments in the fields of statistics, pure and applied mathematics. Submitted manuscripts must demonstrate novelty, methodological rigor, and relevance to mathematical theory or practice. Topics of interest include, but are not limited to: Statistics, Applied Statistics, Data Science, Big Data, Computational Mathematics, Actuarial Mathematics, Applied Mathematics, Pure Mathematics, Discrete Mathematics, Fuzzy Systems, Geometry, Graph Theory, Real Analysis, Operations Research, Optimization, Dynamical Systems, Mathematical Modeling, Mathematics Education. The journal particularly encourages works that combine theoretical depth with computational, industrial, or interdisciplinary relevance.
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia
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Pengelompokan Kecamatan di Kabupaten Banyumas Berdasarkan Produktivitas Hasil Tanaman Pangan Menggunakan Metode K-Means Clustering
Significant differences in food crop productivity in each sub-district in Banyumas are a major concern that needs to be addressed. This handling is specifically given to sub-districts with low productivity categories. However, the sub-district groups according to their categories are not yet known. Therefore, this study aims to group sub-districts in Banyumas Regency based on food crop productivity. Grouping is done using the K-Means Clustering method. The data used are data on the harvest productivity of five food crop commodities according to sub-districts in Banyumas Regency in 2023. The results obtained that there are five clusters with three clusters having at least one type of food crop that is less productive and two clusters which all types of food crops are productive.
Copyright (c) 2026 Retno Dwi Astuti, Dian Puspita http://creativecommons.org/licenses/by-nc-sa/4.0
Penerapan Generalized Additive Model Spline Dalam Analisis Dinamika Tingkat Pengangguran Terbuka di Provinsi Sulawesi Selatan
Analisis regresi adalah metode yang sangat penting dalam memodelkan hubungan antara variabel, di mana variabel prediktor berpengaruh terhadap variabel respon. Namun, regresi linear konvensional memiliki beberapa keterbatasan, terutama ketika asumsi linearitas dan normalitas tidak terpenuhi. Dalam konteks ini, Generalized Additive Model (GAM) muncul sebagai alternatif yang lebih fleksibel. GAM mampu menangani hubungan non-linear antara variabel prediktor dan respon, serta tidak mengharuskan variabel respon untuk berdistribusi normal. GAM menggunakan pendekatan regresi nonparametrik, seperti penalized spline, untuk mengestimasi fungsi smoothing yang dapat menyesuaikan pola data secara otomatis. Pendekatan ini juga membantu mencegah overfitting, yang sering menjadi masalah dalam pemodelan data yang kompleks. Penelitian ini bertujuan untuk menerapkan GAM spline dalam memodelkan faktor-faktor yang memengaruhi Tingkat Pengangguran Terbuka (TPT) di Provinsi Sulawesi Selatan. Tahapan penelitian mencakup pengujian linearitas, penentuan titik knot, dan parameter penghalus optimal. Selanjutnya, pemodelan GAM dilakukan dengan menggunakan penalized spline pada variabel non-linear. Hasil pemodelan menunjukkan bahwa variabel Rata-Rata Lama Sekolah (RLS) dan Produk Domestik Regional Bruto (PDRB) memiliki pengaruh signifikan terhadap TPT, sementara variabel lainnya tidak menunjukkan signifikansi. Dengan demikian, model GAM penalized spline berhasil memodelkan hubungan antara faktor-faktor yang memengaruhi TPT secara efektif. Penelitian ini memberikan pemahaman yang lebih dalam bahwa peningkatan RLS dan PDRB dapat berkontribusi dalam menurunkan TPT di Sulawesi Selatan, yang merupakan informasi penting bagi pengambil kebijakan dalam merumuskan strategi pengurangan pengangguran.
Copyright (c) 2026 Jelita Zalzabila, Anna Islamiyati http://creativecommons.org/licenses/by-nc-sa/4.0
Comparison of Moving Average and Double Exponential Smoothing Methods in Rice Production Forecasting Based on NTB Satu Data
The rapid development of technology in the industry 4.0 era has led to the emergence of the data literacy era, where data is no longer merely supporting information but has become a strategic asset in decision-making. Recognizing the importance of data utilization, the West Nusa Tenggara/Nusa Tenggara Barat (NTB) Provincial Government launched the NTB Satu Data program through Governor Regulation No. 45 of 2021. This program provides an open, standardized, and integrated sectoral data platform managed by the NTB Communication, Information, and Statistics Agency. Through this platform, data is collected, validated, analyzed, and visualized so that it can be utilized for evidence-based policy planning. This study utilizes annual rice production data from 2001 to 2024 from the NTB Satu Data portal to forecast rice production in 2025. Two commonly used time series forecasting methods, Moving Average (MA) and Double Exponential Smoothing (DES), are applied and compared in terms of accuracy. Evaluation was conducted using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) metrics. The analysis results show that the DES (Holt) method produced a MAPE of 9.455% and a MAD of 158.533, outperforming the MA order 2 method with a MAPE of 9.746% and a MAD of 161.700. These findings indicate that DES is more adaptive in capturing historical trend patterns and more effective in modeling production changes over time. The results of this study are expected to provide input for local governments in designing food security policies that are responsive to production dynamics. Utilizing these forecasts will enable more appropriate allocation of resources, increased preparedness for potential food supply disruptions, and strengthening of sustainable food security in NTB Province.
Copyright (c) 2026 Asri Mustika Arbyati, Andika Ellena Saufika Hakim Maharani http://creativecommons.org/licenses/by-nc-sa/4.0
Pemodelan Tren Pencarian Google Penyakit Penyebab Kematian Tertinggi di Indonesia Dengan Metode Loop Prophet
The Loop Prophet method is a time series forecasting approach that utilizes the Facebook Prophet model iteratively (looping) to automatically process multiple variables or keywords. This study aims to predict the weekly search trend on Google for the leading causes of death in Indonesia, taking into account trend, yearly seasonality, and weekly seasonality components, as well as to evaluate model performance using error metrics such as NMAE, NRMSE, and MAPE. The dataset consists of weekly Google Trends search data for selected diseases during the period from September 2020 to July 2025. The results indicate that the Loop Prophet model successfully captures both trend and seasonal patterns. Based on the evaluation criteria, models categorized as “very good” were obtained for the keywords “Stroke”, “Diabetes”, and “Diare”. The “good” category was obtained for “Serangan Jantung”, “Sirosis Hati”, “Penyakit Paru”, “COPD”, dan “Neonatal”. The keyword of “Tuberkulosis” was categorized as “good enough”. Meanwhile, the “poor” category was found for “Kanker Paru-Paru” dan “Pneumonia”, which tend to have fluctuating patterns influenced by incidental events. These findings demonstrate that the Loop Prophet method is effective for time series analysis with complex seasonal patterns, although its performance decreases for diseases with highly irregular search trends.
Copyright (c) 2026 Anggun Qurani, Chandra Sari Widyaningrum http://creativecommons.org/licenses/by-nc-sa/4.0
Generalized LASSO Regression Menggunakan K-Nearest Neighbors Pada Data Persentase Penduduk Miskin Indonesia
Generalized LASSO Regression merupakan pengembangan dari metode LASSO dengan memberikan penalti tidak hanya pada setiap parameter secara terpisah, tetapi juga pada kelompok parameter yang saling terkait. Pendekatan K-Nearest Neighbors (KNN) menentukan besaran penalti berdasarkan kedekatan data dalam ruang fitur sehingga model dapat memilih variabel yang berelasi secara lokal, meningkatkan kemampuan mengenali pola berurutan atau kelompok pada data berdimensi tinggi tanpa mengurangi kemudahan interpretasi. Penelitian ini bertujuan untuk memperoleh parameter tuning optimal pada model Generalized LASSO Regression dengan KNN dan mendapatkan variabel yang berpengaruh terhadap persentase penduduk miskin. Metode penelitian ini terdiri dari dua tahap umum, yakni penentuan tetangga menggunakan KNN dan pemodelan Generalized LASSO Regression dengan pendekatan KNN untuk pendugaan persentase penduduk miskin. Hasil analisis menunjukkan bawa model terbaik diperoleh pada KNN K=3dengan nilai parameter tuning sebesar 0,028 menghasilkan koefisien determinasi 91,5% dan RMSE sebesar 0,130. Model Generalized LASSO Regression dengan pendekatan KNN terbukti dapat menangani masalah multikolinearitas dan efek wilayah untuk mengetahui variabel yang berpengaruh terhadap persentase penduduk miskin. Model ini dapat menjadi alat bantu dalam perencanaan kebijakan di Indonesia dengan fokus wilayah.
Copyright (c) 2026 Sheilla Amanda Sulistyo, Anna Islamiyati http://creativecommons.org/licenses/by-nc-sa/4.0
Model Struktural Faktor Sosio-Ekonomi Yang Memengaruhi Tingkat Kesehatan di Indonesia: Pendekatan Partial Least Squares
Pembangunan manusia merupakan tolok ukur fundamental dalam menilai keberhasilan pembangunan suatu wilayah, dengan kesehatan sebagai variabel hasil yang krusial. Penelitian ini bertujuan untuk menganalisis model struktural pengaruh variabel laten Pendidikan dan Ekonomi terhadap Kesehatan, dengan Kemiskinan sebagai variabel mediasi. Penelitian ini menggunakan pendekatan kuantitatif dengan metode Partial Least Squares Structural Equation Modeling (PLS-SEM). Data yang digunakan merupakan data sekunder dari Badan Pusat Statistik untuk 38 provinsi di Indonesia pada tahun 2024. Evaluasi model awal menunjukkan perlunya revisi akibat adanya masalah multikolinearitas, loading factor yang rendah, dan jalur yang tidak signifikan. Model akhir yang direvisi menunjukkan tingkat kecocokan yang baik dengan nilai SRMR sebesar 0,041. Hasil penelitian menunjukkan bahwa variabel laten Ekonomi berpengaruh negatif signifikan terhadap Kemiskinan (koefisien jalur = -0,851) dan berpengaruh positif signifikan terhadap Kesehatan (koefisien jalur = 0,765). Variabel Ekonomi mampu menjelaskan 71,7% varians Kemiskinan dan 57,4% varians Kesehatan. Dalam model akhir, variabel Pendidikan serta peran mediasi Kemiskinan tidak terbukti signifikan. Studi ini menyimpulkan bahwa faktor ekonomi merupakan pendorong utama yang secara langsung memengaruhi penurunan angka kemiskinan dan peningkatan status kesehatan di Indonesia.
Copyright (c) 2026 Cinta Priscillia Maharani, Risky Gunawan http://creativecommons.org/licenses/by-nc-sa/4.0
Spatial Modeling in Public Health: A Review of Geographically Weighted Poisson Regression (GWPR) Applications
Geographically Weighted Poisson Regression (GWPR) is an important approach in analyzing spatial count data, especially in the field of public health. However, its application in Indonesia still has various methodological weaknesses. This study aims to critically review the suitability and application of the GWPR model, as well as analyze aspects of statistical assumptions, weighting function selection, model evaluation, and methodological innovation. This study uses an article review approach of 10 research articles that apply GWPR in public health. The review results show that most studies have not explicitly articulated research gaps and novelty, and have ignored crucial offset variables in count data. Spatial heterogeneity testing is often performed incorrectly using the BP test instead of visual exploration through thematic maps. The selection of weighting functions and bandwidths is often not based on objective evaluation. Additionally, many studies have not conducted multicollinearity checks and tests of the assumption of equidispersion, which directly impact model validity. Descriptive analysis and visualization of local parameters through maps remain limited, hindering contextual interpretation. Finally, some studies fail to include model goodness-of-fit evaluations such as AIC or pseudo-R², making it impossible to demonstrate the superiority of GWPR over global models objectively. These findings underscore the importance of upholding statistical validation principles and methodological transparency in GWPR modeling to produce accurate and relevant spatial analyses for regional policy-making.
Copyright (c) 2026 Syfriza Davies Raihannabil, Karini Halma, Reghita Maria Hutagalung http://creativecommons.org/licenses/by-nc-sa/4.0
Analysis of Factors Influencing Poverty in South Sumatra Using the Poisson Regression Model
Poverty remains a key development issue for the government, including in South Sumatra Province. This study aims to analyze the factors influencing poverty levels at the district/city level using a Poisson regression model. The independent variables used include the open unemployment rate, education level, Gross Regional Domestic Product (GRDP) per capita, and the percentage of the population working in the informal sector. The data used are secondary data obtained from the Central Statistics Agency (BPS) for the most recent available year. The analysis shows that several variables significantly influence the number of poor people in South Sumatra, where increasing open unemployment and a high proportion of informal workers tend to increase the number of poor people. Conversely, increasing education and GRDP per capita contribute to reducing poverty levels. The Poisson regression model proved appropriate for modeling the number of incidents (count) data in this study. These findings are expected to provide input for local governments in formulating more targeted poverty alleviation policies.
Copyright (c) 2026 Jolius Saut Mangaraja Manik, Fitri Maya Puspita, Sisca Octarina http://creativecommons.org/licenses/by-nc-sa/4.0
Classification of Pension Benefit Adequacy for Civil Servants in Pontianak City Using the C4.5 Algorithm
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.
Copyright (c) 2026 Dwi Indah Maharani, Ray Tamtama http://creativecommons.org/licenses/by-nc-sa/4.0
Clinic of Mathematics with the PPLAM Approach: Efforts to Improve Students’ Mathematics Learning in Manokwari
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.
Copyright (c) 2026 Loria Amisah Lubis, Trigarcia Maleachi Randa, Esther Ria Matulessy, Chrisaria Palungan, Dahlia Gladiola Rurina Menufandu http://creativecommons.org/licenses/by-nc-sa/4.0
Forecasting The Number of Rainy Days in Sorong City Using The Autoregressive Moving Average Method
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.
Copyright (c) 2026 Juan Kailola http://creativecommons.org/licenses/by-nc-sa/4.0
Forecasting Food Industry Enterprises in Ciamis Using Holt Winter Linear Trend Method
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.
Copyright (c) 2026 Irfan Permana, Alam Rahmatuloh http://creativecommons.org/licenses/by-nc-sa/4.0
Klasterisasi Pendapatan Nasional dan Pola Konsumsi Negara-Negara G20 Tahun 2023 Menggunakan Metode K-Means
The G20 countries are often treated as a relatively homogeneous group in macroeconomic analysis, despite substantial 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 consumption indicators in order to identify structural differences in their economic characteristics. The analysis employs the K-Means clustering method using standardized data to ensure comparability across variables with different measurement scales. Prior to clustering, data standardization is applied using the Z-score method. The optimal number of clusters is determined using a cluster validity measure, and the clustering process is performed using Euclidean distance. The results indicate that the optimal clustering structure is achieved with three clusters. The K-Means algorithm successfully groups G20 countries into three distinct clusters with clearly differentiated economic profiles. The centroid analysis reveals that each cluster exhibits unique characteristics in terms of income level, growth dynamics, and consumption patterns, allowing for objective and data-driven cluster categorization. The findings also show that higher income levels are not always associated with higher consumption patterns, and that developing economies tend to form a separate cluster characterized by relatively higher economic growth. The evaluation of cluster quality indicates good cohesion within clusters and 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 heterogeneity of economic structures among G20 countries. The findings contribute to a more nuanced understanding of global economic diversity and may serve as a basis for comparative economic analysis and policy oriented discussions.
Copyright (c) 2026 M Fatta Arya Irwanda Fatta, Ridho Saputra, M. Allif Khair, Fadhillah Fitri http://creativecommons.org/licenses/by-nc-sa/4.0
Critical Path Identification and Schedule Rationalization in a Plantation Road Improvement Project: Case Study at PTPN IV, Batanghari
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.
Copyright (c) 2026 Putri Kurniawati, Niken Rarasati http://creativecommons.org/licenses/by-nc-sa/4.0
Modeling Term Life Insurance Premiums Using Monte Carlo Simulations with Stochastic Interest Rates Based on The Indonesia Mortality Table IV
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.
Copyright (c) 2026 Mery Christyn Lubis, Bungaria Tampubolon, Febry Vista Kristen Tarigan http://creativecommons.org/licenses/by-nc-sa/4.0
Analysis of Life Insurance Underwriting Risk Classification Using Ordinal Logistic Regression and XGboost
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.
Copyright (c) 2026 Wenny Susanty, Dewi Fortuna Silaban http://creativecommons.org/licenses/by-nc-sa/4.0
Forecast of Farmers' Exchange Rate in Jambi Province Using the Arima Method (Autoregressive Integrated Moving Average)
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.
Copyright (c) 2026 Tiah Agustina, Gusmanely. Z http://creativecommons.org/licenses/by-nc-sa/4.0
Comparative Analysis of Mortality Rates and Whole Life Insurance Premiums Based on the Indonesian Mortality Table IV (TMI IV)
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.
Copyright (c) 2026 riski banjarnahor, Imelda Octavia Lumbantobing, Michael Dolly Sianturi http://creativecommons.org/licenses/by-nc-sa/4.0