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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jambura Journal of Mathematics Inferensi Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Mathematics, Computation and Statistics (JMATHCOS)
Mujiati Dwi Kartikasari
Department Of Statistics, Universitas Islam Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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Premium Pricing of Liability Insurance Using Random Sum Model Mujiati Dwi Kartikasari
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 17, ISSUE 1, February 2017
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/eksakta.vol17.iss1.art5

Abstract

Premium pricing is one of important activities in insurance. Nonlife insurance premium is calculated from expected value of historical data claims. The historical data claims are collected so that it forms a sum of independent random number which is called random sum. In premium pricing using random sum, claim frequency distribution and claim severity distribution are combined. The combination of these distributions is called compound distribution. By using liability claim insurance data, we analyze premium pricing using random sum model based on compound distribution
Prediction of Outstanding Claims Liability in Non-Life Insurance: An Application of Adaptive Grey Model Mujiati Dwi Kartikasari; Hani’atul Maghfuroh
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 2, ISSUE 2, August 2021
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol2.iss2.art4

Abstract

In order to assess the solvency of non-life insurance companies, the prediction of outstanding claims liability is very important. Prediction of outstanding claims liability is usually done by using a run-off triangle data scheme. However, if data are not available to form the scheme, the prediction of outstanding claims liability cannot be made. Another alternative for predicting of outstanding claims liability is to use time series analysis. This research uses an adaptive grey model that has the advantage of being free of assumptions of data patterns and a minimum amount of data used to predict is small (at least 4 data). To determine the accuracy of the adaptive grey model, we compare the prediction of outstanding claims liability using a grey model classic. Based on the analysis results, the adaptive grey model is better than the classic gray model in predicting outstanding claims liability.
Self-Organizing Map Menggunakan Davies-Bouldin Index dalam Pengelompokan Wilayah Indonesia Berdasarkan Konsumsi Pangan Mujiati Dwi Kartikasari
Jambura Journal of Mathematics Vol 3, No 2: July 2021
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (800.87 KB) | DOI: 10.34312/jjom.v3i2.10942

Abstract

ABSTRAKKecukupan konsumsi pangan merupakan salah satu penunjang terbentuknya sumber daya manusia unggul yang menjadi fokus kebijakan pembangunan di Indonesia. Agar konsumsi pangan terpenuhi, salah satu cara yang dapat dilakukan adalah melakukan pengelompokan wilayah berdasarkan konsumsi pangan. Penelitian ini bertujuan untuk mengelompokkan wilayah Indonesia berdasarkan konsumsi pangan berdasarkan data konsumsi kalori per kapita sehari dari berbagai komoditas pangan. Pengelompokan wilayah dilakukan dengan metode self-organizing map (SOM) dengan terlebih dahulu ditentukan jumlah cluster optimum menggunakan nilai Davies-Bouldin Index (DBI) terkecil. Hasil penelitian menunjukkan bahwa hasil cluster optimum yang terbentuk sejumlah 4 cluster dengan jumlah anggota untuk cluster 1 sebanyak 22 provinsi, cluster 2 sebanyak 10 provinsi, cluster 3 sebanyak 1 provinsi, dan cluster 4 sebanyak 1 provinsi.ABSTRACTAdequate food consumption is one of the supports for forming superior human resources, which is the focus of development policies in Indonesia. To fulfill food consumption, one way to be done is to group regions based on food consumption. This study aims to classify regions of Indonesia based on food consumption based on average daily per capita calorie consumption data from various food commodities. Regional grouping is done using the self-organizing map (SOM) method by first determining the optimum number of clusters using the smallest Davies-Bouldin Index (DBI) value. The results showed that the optimum cluster results were 4 clusters with the number of members for cluster 1 as many as 22 provinces, cluster 2 as many as 10 provinces, cluster 3 as many as 1 province, and cluster 4 as many as 1 province.
Implementasi Metode Perhitungan Aktuaria Program Dana Pensiun Menggunakan Flask Muthia Dishanur Izzati; Mujiati Dwi Kartikasari
Jambura Journal of Mathematics Vol 4, No 2: July 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1656.682 KB) | DOI: 10.34312/jjom.v4i2.12954

Abstract

The pension fund program is a program that seeks future planning by providing pension benefits to participants. The vital thing that becomes a concern in the pension fund program is the actuarial cost method. There are two categories for actuarial cost methods, which are Accrued Benefit-Cost Method and Projected Benefit-Cost Method. The normal contribution characteristic of the Projected Benefit-Cost Method is more stable than the Accrued Benefit-Cost Method, so it is better to use it from the participants’ perspective. This study discusses the use of the Projected Benefit-Cost Method by calculating normal contributions and actuarial liabilities from the methods included in it, which are Attained Age Normal, Entry Age Normal, and Individual Level Premium. Based on the calculation results, the Entry Age Normal and Individual Level Premium methods have a smaller final value of normal contribution payments and have a larger actuarial liability than the Attained Age Normal. Thus, of the three methods included in the Projected Benefit-Cost Method, the Entry Age Normal and Individual Level Premium methods are better used from the perspective of participants. For the calculation of pension funding using the Attained Age Normal, Entry Age Normal, and Individual Level Premium methods to be widely implemented by the public, this study created an application website using flask, which can be accessed at https://perhitunganaktuariadapen.herokuapp.com/.
A Comparative Study of Fuzzy Chain Ladder and Fuzzy Bornhuetter-Ferguson Methods for Claims Reserving Mujiati Dwi Kartikasari
Inferensi Vol 9 No 1 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i1.9449

Abstract

Accurate estimation of claim reserves is critical for the solvency of non-life insurance companies. Classical deterministic methods, such as Chain Ladder (CL) and Bornhuetter-Ferguson (BF), often fail to capture the inherent vagueness in actuarial judgments regarding development patterns and prior information. Fuzzy Set Theory, particularly through Triangular Fuzzy Numbers (TFNs), offers a formal framework to model this imprecision. This study conducts an empirical comparative analysis of two fuzzy reserving methods: the Fuzzy Chain Ladder (FCL) and the Fuzzy Bornhuetter-Ferguson (FBF). The FCL method fuzzifies the development factors, while the FBF method extends fuzzification to both the development pattern and the prior estimate of ultimate losses. Both methods are applied to a real-world liability insurance claims dataset from an Indonesian company, structured into a run-off triangle. The performance is evaluated by comparing the central reserve estimates, the total model uncertainty, and the asymmetry of the fuzzy output intervals. The results indicate that the FCL method produces a more conservative and volatile central reserve, approximately 12.3% higher than the FBF estimate under a risk-neutral assumption. More importantly, the FBF method demonstrates superior stability, with a total uncertainty measure about 14.7% lower than FCL, and exhibits an asymmetric uncertainty structure in which the right spread is consistently narrower, making its defuzzified reserve less sensitive to the actuary's risk attitude. The study concludes that the FCL method is suitable as a transparent, data-driven benchmark, whereas the FBF method is recommended for generating more robust and risk-sensitive forecasts when credible prior information is available.
Pendekatan Metode Prophet dalam Peramalan Nilai Ekspor Jawa Tengah untuk Perencanaan Ekonomi Daerah Rio Wahyu Saputra; Mujiati Dwi Kartikasari
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.37781

Abstract

Export plays an important role in promoting regional economic growth. Central Java Province, as one of the regions with significant international trade activities, contributes to the national export value. However, the export value of Central Java has shown fluctuations influenced by various factors, such as global economic dynamics, the COVID-19 pandemic, and trade policies. Therefore, a forecasting method capable of producing accurate predictions is needed to support regional economic planning and policy decision-making. This study aims to forecast the export value of Central Java for the period 2019–2025 using the Prophet method based on monthly data obtained from the Central Java Provincial Statistics Agency (BPS). The dataset was divided into 70% training data and 30% testing data. The modelling process utilized the trend, seasonality, and holiday components available in the Prophet method. Model performance was evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) accuracy measures. The results show that the best-performing model yields an RMSE of 54.094 and an average MAPE of 16.2317%, indicating a good level of forecasting accuracy. The forecasting results also indicate that the export value of Central Java is expected to increase until the end of 2025, with the highest export value predicted to occur in December at approximately 1,028.44 million US dollars.
Comparative Analysis of Grey GM(1,1) and Grey Verhulst Models for Forecasting Electricity Consumption in Indonesia Indri Noer Khoeriyah; Mujiati Dwi Kartikasari
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37710

Abstract

Electricity energy demand in Indonesia continues to increase in line with economic development, technological advancement, and population growth. This condition necessitates appropriate energy planning to ensure a stable electricity supply. Electricity consumption forecasting is required to predict future demand and to serve as a basis for formulating sustainable energy policies. However, studies comparing different grey forecasting models for Indonesia’s electricity consumption remain limited, particularly in identifying the most suitable model for its growth characteristics. This study aims to compare the performance of two models within Grey System Theory, namely the Grey Model GM(1,1) and the Grey Verhulst model, in forecasting electricity consumption in Indonesia. The data used consist of annual electricity consumption data for the period 2000–2024, obtained from the official websites of Statistics Indonesia (BPS) and the Ministry of Energy and Mineral Resources (ESDM). The analysis stages include model construction, parameter estimation, forecasting, and accuracy evaluation using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results indicate that the Grey Verhulst model outperforms the GM(1,1) model, with an RMSE of 10,955.97, an MAE of 9,233.64, and a MAPE of 5%, whereas the GM(1,1) model yields an RMSE of 23,562.39, an MAE of 15,151.57, and a MAPE of 9%. These results suggest that the Grey Verhulst model provides a better fit for the observed data, which exhibits nonlinear growth behavior with a tendency toward saturation. The best-performing model, namely the Grey Verhulst model, produces a forecast of electricity consumption for the year 2025 of 388,025 GWh. This result is expected to serve as a reference for national electricity policy planning.
Peramalan Inflow dan Outflow Uang Kartal Menggunakan X-13 ARIMA-SEATS Rizka Fitria Herdiantini; Mujiati Dwi Kartikasari
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4331

Abstract

Currency is an important part of Indonesian society's economic transactions. In order to effectively manage the amount of currency in circulation, Bank Indonesia must carefully plan and estimate its currency needs. One way to estimate this need is by looking at Bank Indonesia's inflow and outflow. Therefore, forecasting currency inflow and outflow is crucial for future planning. Inflow and outflow data are included in the time series that is affected by calendar variations. Traditional forecasting methods, such as exponential smoothing and ARIMA, cannot handle these variations. Therefore, this study uses the X-13 ARIMA-SEATS method, which is able to forecast time series data with the effect of calendar variations. Based on monthly data on currency inflow and outflow from January 2015 to December 2022, the results show that the X-13 ARIMA-SEATS method is effective when used with the mean absolute percentage error (MAPE) criteria.
Child Stunting Classification using the LightGBM Method: A Case Study in the Rowosari District of Kendal, Central Java Galuh Syafa Azahra; Mujiati Dwi Kartikasari
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6865

Abstract

Stunting in children under five is a major health problem in Indonesia, significantly affecting physical, mental, and cognitive development, which can ultimately lower their quality of life in the future. Early identification of children at risk of stunting remains a challenge due to limited resources and the effectiveness of existing prediction methods. Machine learning techniques offer a promising approach to improving stunting classification and risk prediction. This study aims to develop an accurate and efficient classification model for identifying stunting status using the Light Gradient Boosting Machine (LightGBM) method. The study was conducted on children in Rowosari District, Kendal, Central Java, utilizing seven key variables: gender, age (months), birth weight, birth length, current weight, current height, and stunting status. The results indicate that the LightGBM model achieved 97% accuracy with an AUC value of 0.99, demonstrating a high ability to distinguish stunting status. Furthermore, the model successfully identified the key risk factors contributing to stunting. These results show that the LightGBM method has the potential to make very accurate predictions and also help people come up with more timely and targeted intervention strategies that are based on data. By leveraging machine learning, health practitioners and policymakers can improve stunting prevention efforts, optimize resource allocation, and implement more effective public health strategies to reduce stunting prevalence in Indonesia.
Unveiling the Connection: The Impact of Poverty Rate on Human Development Index in the Special Region of Yogyakarta Province Using the Almon Lag Model Kayyis Vatin; Mujiati Dwi Kartikasari
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7348

Abstract

The Human Development Index (HDI) is established as one of the main indicators included in the fundamental framework of regional development. Ideally, HDI, which serves as a benchmark for regional development, has a negative correlation with poverty conditions. Historical poverty can influence the current HDI based on elements of health, education, and a decent standard of living. The Special Region of Yogyakarta is the province with the second-highest HDI in Indonesia but has had the highest poverty rate in Java in recent years. The Almon Lag Model can analyze the impact of poverty on HDI by considering the distributed lag effect. This study aims to analyze the impact of the poverty rate on HDI over the past twenty-seven years. Based on the analysis, the best model utilizes a lag length of three years and a polynomial degree of two. The model has a Mean Absolute Percentage Error (MAPE) of 0.73%, indicating that the applied Almon Lag Model can make accurate predictions.
Co-Authors Amimah Shabrina Putri Prasmono Galuh Syafa Azahra Hani’atul Maghfuroh Indri Noer Khoeriyah Kayyis Vatin Muthia Dishanur Izzati Rio Wahyu Saputra Rizka Fitria Herdiantini Saffanah Nur Elvina Mulyawati