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All Journal Epsilon: Jurnal Matematika Murni dan Terapan CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Matematika Sains dan Teknologi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengabdian Sumber Daya Manusia Jurnal Pengabdian Pada Masyarakat OMNICODE Journal (Omnicompetence Community Developement Journal) RAGAM: Journal of Statistics and Its Application
Lestia, Aprida Siska
Program Studi Matematika FMIPA Universitas Lambung Mangkurat
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PENDEKATAN BAYESIAN MARKOV CHAIN MONTE CARLO (MCMC) METROPOLIS-HASTINGS PADA PEMODELAN KLAIM ASURANSI KESEHATAN Lestia, Aprida Siska; Idris, Mochammad
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN (EPSILON: JOURNAL OF PURE AND APPLIED MATHEMATICS) Vol 18, No 2 (2024)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v18i2.13872

Abstract

The distribution of health insurance claims that tend to be asymmetric and have heavy tails, requires a method that can offer more flexible and robust solutions than traditional frequentist approaches, such as the Bayesian Method. The advantage of this method lies in its ability to comprehensively account for uncertainty in parameter estimation, thereby producing a posterior distribution that can capture complex pattern of claim data. This study aims to apply the Bayesian approach with the Markov Chain Monte Carlo (MCMC) Metropolis-Hastings method in modeling health insurance claims. The claim data used are divided into outpatient and inpatient claims, with the lognormal distribution fitting proven to be the most appropriate for both types of claims. Risk estimation through Value at Risk (VaR) and Conditional Tail Expectation (CTE) using the Bayesian approach showed more moderate results compared to empirical estimates, indicating that this approach can reduce risk overestimation
LEVEL SOFT GROUP AND ITS PROPERTIES Abdurrahman, Saman; Idris, Mochammad; Faisal, Faisal; Hijriati, Na’imah; Thresye, Thresye; Lestia, Aprida Siska
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2263-2274

Abstract

In this paper, we present an application of fuzzy subset and fuzzy subgroup to a soft set and a soft group, thereby creating a soft set and a soft group within the same group. Furthermore, we refer to the soft and soft groups as level soft sets and level soft groups. We also found out the level of soft sets and the operations on soft sets, such as intersection, union, and subset. We also examine what conditions a fuzzy subgroup and a soft group must meet to form a level soft group. Moreover, we scrutinize the properties of operations on a soft set, specifically intersection, union, and AND, and apply them to the level soft group to ascertain if they consistently produce a level soft group over the same set. Furthermore, we investigate the formation of a level soft and level soft group resulting from the homomorphism of the group and soft group. The research findings can enrich studies on the relationships between structures in fuzzy subgroups and soft groups and the application of soft group levels in further research.
ESTIMASI PARAMETER RANDOM EFFECT MODEL PADA REGRESI PANEL MENGGUNAKAN METODE GENERALIZED LEAST SQUARE (STUDI KASUS: KEMISKINAN DI PROVINSI KALIMANTAN SELATAN) Ariandy Hermawan; Yuana Sukmawaty; Aprida Siska Lestia
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7419

Abstract

Poverty is a condition that concerns the inability to meet the most minimum demands of life, especially from the aspects of consumption, income, education, and health. The problem of poverty is very complex and multidimensional in nature, as it relates to social, economic, cultural and other aspects. This study focuses on observation areas in South Kalimantan Province, with the PPM value in 2021 reaching (4.83%) still above the target goal of the Regional Medium-Term Development Plan (RPJMD) of South Kalimantan Province (3.96- 4.01%), so that further interventions are still needed to be able to reduce PPM in poverty cases. This study aims to estimate the parameters of the panel regression model used to analyze factors that are suspected to affect poverty cases in South Kalimantan Province in 2016-2020. The Random Effect Model (REM) is the best model used in this study, assuming that there are differences in slopes and interceptions caused by residual due to differences between individual units and between time periods. The process of estimating parameters on REM is determined through the Generalized Least Squares (GLS) Estimator method . From the results of the data processing, it was obtained that the model is influenced by economic growth, life expectancy, open unemployment rate, and labor force participation rate. From the results of the analysis of 2 (two) models, it was tested significantly and affected poverty in South Kalimantan Province in 2016-2020.  Keywords:   Poverty, Data Regression Panel, Generalized Least Square Method (GLS).
PEMODELAN REGRESI DATA PANEL PADA TINGKAT PARTISIPASI ANGKATAN KERJA PEREMPUAN DI PROVINSI KALIMANTAN SELATAN Putri Norhikmah; Fuad Muhajirin Farid; Aprida Siska Lestia
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7383

Abstract

Women’s Labor Force Participation Rate (LFPR) is an indication that can shows how much participation of women in the process development. The purpose of this study is to provide an overview of LFPR women in the Province of South Kalimantan, explaining the expected variables influence on women’s LFPR in South Kalimantan Province and determine the best model. This research data is sourced from the Centra Statistics Agency of South Kalimantan Province with a time period of 2017-2020. Variable independen research, namely female workers, female residents who are still in school and taking care of the household, the average length of schooling for women, female population according to the highest education ever graduate from senior high school above, female household heads, status of married and unmarried women marriage, district/city minimum wage, human development index women and regional domestic income growth at constant prices while the dependent variable is female LFPR. The results of data analysis, can be conclude that the Fixed Effect Model as the best model of panel regression. Women’s LFPR in South Kalimantan Province by producing two recommendation with Fixed Effect Model an R-Squared in the first model of 99,40%.Keywords:  Women’s LFPR, Panel Data Regression, South Kalimantan
LOWER LEVEL SUBGRUPOID Abdurrahman, Saman; Hijriati, Na'imah; Thresye, Thresye; Idris, Moch; Lestia, Aprida Siska
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 19, No 2 (2025)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v19i2.15332

Abstract

This study investigates the structure of anti-fuzzy subgroupoids within the framework of groupoids, extending the theory of fuzzy subgroups beyond traditional group-based algebraic systems. While numerous fuzzy approaches have been applied to groups and semigroups, the exploration of groupoids algebraic structures without the necessity of identity or inverse elements remains limited, particularly in the context of anti-fuzzy theory. This research addresses that gap by developing a mathematical characterization of anti-fuzzy subgroupoids and systematically analyzing their relationship with lower-level subsets. A key result demonstrates that every subgroupoid can be represented as a lower-level subset of a suitably constructed anti-fuzzy subgroupoid. Furthermore, it is shown that equality of two lower-level subsets occurs if and only if no element exists with a membership value strictly between the corresponding thresholds. Employing a deductive and axiomatic approach, this work contributes to the theoretical advancement of fuzzy structures in non-classical algebra. It offers a foundation for future applications in uncertainty-based decision systems
Integrated Artificial Intelligence Mentoring to Enhance Teacher Competence at SMP Negeri 12 Banjarbaru Lissa, Hermei; Hijriati, Na'imah; Abdurrahman, Saman; Idris, Moch; Lestia, Aprida Siska; Shiddiq, Muhammad Mahfuzh; Sa’adh, Yalela; Oktaviani, Yeni Rahma
OMNICODE Journal (Omnicompetence Community Developement Journal) Vol. 5 No. 1 (2025)
Publisher : UrbanGreen Central Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55756/omnicode.v5i1.230

Abstract

The integration of Artificial Intelligence (AI) in school learning can enhance instructional quality and efficiency; however, its use by teachers remains fragmented. This community development activity, conducted by the Department of Mathematics at Universitas Lambung Mangkurat, aimed to improve teachers’ knowledge and skills through training and mentoring that emphasized the integrated use of ChatGPT, Gamma AI, and Presentations.AI. The program was implemented at SMP Negeri 12 Banjarbaru, Indonesia, involving teachers from various subject areas. Activities included AI-based training, hands-on mentoring, and pre- and post-activity evaluation. Data were analyzed using the Wilcoxon Signed-Rank Test. Results showed that improvements related to ChatGPT were not statistically significant (p > 0.05), whereas improvements with Gamma AI were statistically significant (p < 0.05). Improvements in Presentations.AI usage and integrated AI application skills were highly significant (p < 0.01). These findings indicate that integrated AI-based mentoring effectively enhances teacher competence at the junior high school level.
From Risk-Neutral to Risk-Sensitive Reinforcement Learning: Actor–Critic vs REINFORCE with Tail-Based Risk Measures Lestia, Aprida Siska; Effendie, Adhitya Ronnie; Tantrawan, Made; Azrarsyah, Muhammad Rafli
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40309

Abstract

his study investigates the application of \emph{risk-sensitive reinforcement learning} on heavy-tailed return series by comparing two primary algorithms: REINFORCE with baseline (REINFORCE-BL) and episodic batched actor--critic (A2C-B). Initial exploratory analysis reveals an asymmetric return distribution with numerous extreme \emph{outliers}, rendering variance-based risk measures inadequate and motivating the integration of tail-based risk measures—specifically Value at Risk (VaR), Conditional Value at Risk (CVaR), and Entropic Value at Risk (EVaR)—into the RL objective function. This study constructs a simple portfolio environment with discrete actions (market entry, market exit, and \emph{hold}) and trains both algorithms under four scenarios: risk-neutral, VaR, CVaR, and EVaR. Experimental results demonstrate that A2C-B consistently outperforms REINFORCE-BL across all scenarios, exhibiting higher average long-term rewards, faster convergence rates, and more stable \emph{learning curves}. While VaR and CVaR penalties significantly reduce rewards and increase learning volatility for REINFORCE-BL, A2C-B experiences only moderate reward reductions while maintaining stability. In the EVaR scenario, both algorithms yield high rewards, yet A2C-B retains a slight advantage in terms of stability. These findings indicate that in environments with heavy-tailed returns, employing coherent risk measures (particularly CVaR and EVaR) within an actor--critic framework offers a more compelling trade-off between tail risk control and average performance, serving as a viable \emph{baseline} for the development of risk-sensitive RL in finance and actuarial science.
Co-Authors Adhitya Ronnie Effendie, Adhitya Ronnie Ahmad Taufik Amalia, Zharfa Rizqi Andriani, Asfia Ariandy Hermawan Azkia Azkia Azkia Khairal Jamil Azrarsyah, Muhammad Rafli Balya, Muhammad Afief Dewi Anggraini Dewi Sri Susanti Faisal Faisal Fitriani Fitriani Fuad Muhajirin Farid Fuad Muhajirin Farid Gunawan, I Wayan Ari Hijriati, Naimah Idris, Moch Idris, Mochammad Kasim Alfarisi Lissa, Hermei Maria Ulfah Muhammad Firdaus Muhammad Meidy Maulana Nabila Septiani Nadya Pratiwi Noor Baitirahmah Noor Jannah, Noor Nur Salam Nur Salam Oktaviani, Yeni Rahma Pardi Affandi Putri Norhikmah Riska Fitria Rusidawati Rusidawati Saman Abdurrahman Sa’adh, Yalela Sekar Wahyuningtias Shiddiq, Muhammad Mahfuzh Siti Jubaidah Syifa Ajeria Tantrawan, Made Thaibatun Nissa Thresye Thresye Tri Puspa Lestari Winda Adinda Tanjung Wuri Setyana Sari Yogi Apriyanto Yuana Sukmawaty Yuni Yulida