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Application of Simulated Annealing Method on Tabarru-Fund Valuation using Inflator by Vasicek Model Approach Based on Profit and Loss Sharing Scheme Selvi Faristasari; Adhitya Ronnie Effendie
Indonesian Journal of Mathematics and Applications Vol. 1 No. 1 (2023): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.01.4

Currently, the financial services industry is dominated by conventional banks and individuals that apply the system of interest or an excess of loans. In Islam, this excess is referred to as usury, which is prohibited by Islamic law because, in practice, usury makes borrowers poorer as they cannot pay such high-interest installments. Not to mention, late payments are subject to penalties that will continue to accumulate if the borrower is unable to pay the next installment. From these facts, this system is prohibited by Islamic Law because there are harmed parties. Therefore, this research discusses mathematical models in the form of Islamic investment business loans for micro-economic traders by implementing a profit and loss sharing system. Tabarru-fund is a set of funds derived from borrowers’ contributions used to overcome conditions when they experience losses in certain conditions. In this mathematical model, the tabarru-fund acts as the premium that must be paid if the borrower is still profitable after the principal installments have paid off. This sharia model with tabarru funds is obtained by calculating the premium which involves the problem of minimizing the remaining tabarru funds in a certain period. The future value of the trader's profit rate will be projected using the Vasicek Model approach which previously determined the parameter estimation using OLS regression and then the data is generated using Monte Carlo simulation so that the sharia inflator is obtained. This sharia inflator plays a role in the optimization process of minimizing the remaining tabarru-fund which will be solved by the Simulated Annealing (SA) algorithm.

Application of Simulated Annealing Method on Tabarru-Fund Valuation using Inflator by Vasicek Model Approach Based on Profit and Loss Sharing Scheme Selvi Faristasari; Adhitya Ronnie Effendie
Indonesian Journal of Mathematics and Applications Vol. 1 No. 1 (2023): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2023.001.01.4

Currently, the financial services industry is dominated by conventional banks and individuals that apply the system of interest or an excess of loans. In Islam, this excess is referred to as usury, which is prohibited by Islamic law because, in practice, usury makes borrowers poorer as they cannot pay such high-interest installments. Not to mention, late payments are subject to penalties that will continue to accumulate if the borrower is unable to pay the next installment. From these facts, this system is prohibited by Islamic Law because there are harmed parties. Therefore, this research discusses mathematical models in the form of Islamic investment business loans for micro-economic traders by implementing a profit and loss sharing system. Tabarru-fund is a set of funds derived from borrowers’ contributions used to overcome conditions when they experience losses in certain conditions. In this mathematical model, the tabarru-fund acts as the premium that must be paid if the borrower is still profitable after the principal installments have paid off. This sharia model with tabarru funds is obtained by calculating the premium which involves the problem of minimizing the remaining tabarru funds in a certain period. The future value of the trader's profit rate will be projected using the Vasicek Model approach which previously determined the parameter estimation using OLS regression and then the data is generated using Monte Carlo simulation so that the sharia inflator is obtained. This sharia inflator plays a role in the optimization process of minimizing the remaining tabarru-fund which will be solved by the Simulated Annealing (SA) algorithm.

THE GRADUATION OF TRANSITION INTENSITIES FROM SEMI-MARKOV PROCESSES TO PREMIUM PRICING Zuhairoh, Faihatuz; Rosadi, Dedi; Effendie, Adhitya Ronnie
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2337-2350

One of the important assumptions of the premium pricing of a health insurance product is the probability for someone suffers from a certain disease. In this paper, the disability income model is applied to a company using two covariates, namely age and sex. The purpose is to find out the magnitude of the probability for employees to experience disabilities due to work, a multi-state model can be used with semi-Markov assuming. There are several approaches to complete the multi-state model, one of which is the transition intensity approach. The intensity of the transition in this paper is estimated using the maximum likelihood approach, which will produce a crude estimate. Afterwards, the graduation process is performed on a crude estimate to obtain a finer shape of the transition intensity function with the Generalized Linear Model (GLM). The intensity of the transition from the graduation results is used to form transition probabilities which are eventually used as one of the assumptions in premium pricing.

Improving the Accuracy of Discrepancies in Farmers' Purchasing and Selling Index Prediction by Incorporating Weather Factors Yulianti, Silvina Rosita; Effendie, Adhitya Ronnie; Susyanto, Nanang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22584

One measure that can be used to see the level of farmer welfare is the farmer exchange rate (NTP), which is a comparative calculation between the price index received by farmers (IJ) and the price index paid by farmers (IB), expressed as a percentage. In reality, NTP cannot explain the actual welfare situation of farmers because the ratio value has the potential to produce biased values. Another alternative that can be used to look at farmer welfare with less potential bias is to look at the difference between the sales index and the farmer purchasing index (ID). ID data forecasting can be a reference for developing and optimizing things that need to be improved in the agricultural sector. Despite the fact that a number of external factors, such as variations in the weather throughout the year, had a significant impact on the ID value, previous research used the ARIMA model to forecast without taking exogenous factors into account. Therefore, the goal of this research is to identify the optimal ARIMAX regression model for achieving accurate forecasting results with minimal error values. This research was carried out with limitations using data from the Central Statistics Agency and the Meteorological, Climatological, and Geophysical Agency in Central Java from 2008 to 2023. The first method in this research is to prepare the data, which involved collecting secondary data such as IJ and IB along with climate data such as rainfall, duration of sunlight, air pressure, wind speed, and rice prices. Next, calculate the difference between IJ and IB to determine the ID value. Then, verify the ID data's stationarity and perform AR and MA calculations. After determining the AR and MA values, construct an ARIMAX model that incorporates external factors, search for the optimal model, and utilize the optimal model to make future predictions. The results show that the accuracy of the ARIMAX model (1,1,0) has a better value than the accuracy of the ARIMA model (1,1,0), and the results obtained in this study are better than previous studies. The authors hope that the findings of this research will serve as a benchmark for the forecasting analysis of time series data in the agricultural sector, providing the local government with a foundation for policy decisions.

ANALYSIS MULTILEVEL SURVIVAL DATA USING COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL Sandelvia, Krismona; Effendie, Adhitya Ronnie
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.73-82

Multilevel survival data is time-to-event data with a hierarchical or nested structure. This study aims to model the data using the Covariate-Adjusted Frailty Proportional Hazards method, which is an extension of the Cox proportional hazards model with the addition of random effects (frailty). Parameter estimation is performed using a Bayesian approach via Markov Chain Monte Carlo (MCMC). This method is applied to analyze repeated observations of Chronic Granulomatous Disease (CGD) infections, with frailty represented by the hospital and the patient. The results of the data analysis indicate that both hospital and patient frailty significantly influence the time to infection, with patient frailty having a greater effect. Additionally, the treatment variable rINF-g significantly in reducing the risk of serious infection for CGD patients by 64.44%.

Classification of Tumor and Normal Tissue Gene Expression in Lung Adenocarcinoma Using Support Vector Machine and Gaussian Process Classification Yotenka, Rahmadi; Effendie, Adhitya Ronnie; Fajriyah, Rohmatul
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11763

Lung adenocarcinoma (LUAD) is a major cause of cancer-related mortality worldwide. This study aims to identify potential LUAD biomarkers and develop robust classification models using the GSE151101 microarray dataset. Preprocessing included RMA normalization, ComBat batch-effect correction, and feature filtering based on annotation completeness, variability, and statistical significance. Support Vector Machine (SVM) and Gaussian Process Classification (GPC) models were constructed, with the polynomial GPC model achieving the best performance (accuracy 97.92%; F1-score 97.96%). Repeated 10-fold cross-validation confirmed its stability (mean accuracy 96.88%, SD ±1.97%, CV 2.03%), outperforming linear SVM, GPC-RBF, and Multiple Kernel Learning (MKL). Functional enrichment analysis showed that key discriminative genes; CDH13, CDKN2A, BCL2L11, MYL9, COL1A1, and AKT3; were enriched in pathways related to epithelial–mesenchymal transition, extracellular matrix remodelling, focal adhesion, PI3K/AKT signalling, and cell-cycle regulation, all of which are central to LUAD progression. In general, polynomial-kernel GPC is a stable and useful way to classify transcriptomes and rank biomarkers. Nevertheless, the translational potential of these signatures requires further validation in independent and clinically controlled cohorts.

Pelatihan Geogebra untuk Peningkatan Kompetensi Guru MGMP Matematika SMA/MA Kota Samarinda Syaripuddin, Syaripuddin; A'yun, Qonita Qurrota; Indarsih, Indarsih; Solikhatun, Solikhatun; Isnaini, Uha; Wahyuni, Sri; Amijaya, Fidia Deny Tisna; Sandariria, Hardina; Gunardi, Gunardi; Effendie, Adhitya Ronnie; Sifriyani, Sifriyani; Dani, Andrea Tri Rian; Wahyujati, Mohamad Fahruli; Mulyadi, Taqriri Kamal; Putra, Fachrian Bimantoro
Journal of Research Applications in Community Service Vol. 2 No. 4 (2023): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v2i4.2438

Teknologi dapat memudahkan guru dalam menyampaikan konsep matematika kepada siswa melalui media pembelajaran di kelas. Pembelajaran di bidang Matematika perlu memanfaatkan perkembangan teknologi untuk mengatasi tantangan di era digital. Permasalahan yang terjadi adalah bahwa mayoritas guru Matematika SMA/MA di Samarinda belum menggunakan perangkat lunak untuk mendukung mutu proses pembelajaran Matematika secara daring maupun luring. Oleh karena itu, dalam rangka Pengabdian kepada Masyarakat, maka diselenggarakan pelatihan Perangkat Lunak GeoGebra untuk peningkatan kompetensi pembelajaran Matematika bagi guru SMA/MA khususnya yang tergabung dalam Musyawarah Guru Mata Pelajaran (MGMP) Matematika SMA/MA di Kota Samarinda. Pelatihan menggunakan metode tatap muka dengan diisi ceramah, diskusi, dan latihan. Peserta pelatihan mengerjakan tes yang diberikan di awal dan akhir pelatihan dalam rangka mengukur pemahaman peserta terhadap materi pelatihan. Diperoleh hasil tes awal dengan nilai rata-rata 102,12 dan hasil tes akhir dengan nilai rata-rata 130. Selanjutnya, hasil tes tersebut dianalisis menggunakan statistik deskriptif dan uji beda rata-rata Wilcoxon. Hasil menunjukkan bahwa nilai asymp. sig. (2-tailed) 0,000 kurang dari nilai alpha (α) yaitu 0,05. Berdasarkan hasil analisis uji beda rata-rata, dapat disimpulkan bahwa terdapat perbedaan nilai rata-rata nilai tes awal dan tes akhir. Kenaikan yang signifikan antara nilai rata-rata tes awal dengan nilai rata-rata tes akhir pelatihan mengindikasikan peningkatan pemahaman peserta pelatihan terhadap Perangkat Lunak GeoGebra. Tim terus memberikan pendampingan yang berkelanjutan terkait pemanfaatan GeoGebra kepada kepada guru-guru Matematika SMA/MA Kota Samarinda melalui forum komunikasi grup WhatsApp.

Peningkatan Kompetensi Guru MGMP Matematika SMA/MA Kota Samarinda dalam Pembelajaran Statistika Menggunakan Aplikasi RStudio Cloud Sandariria, Hardina; Syaripuddin, Syaripuddin; Gunardi, Gunardi; Effendie, Adhitya Ronnie; Sifriyani, Sifriyani; Dani, Andrea Tri Rian; Wahyujati, Mohamad Fahruli; Indarsih, Indarsih; Solikhatun, Solikhatun; Isnaini, Uha; Wahyuni, Sri; Amijaya, Fidia Deny Tisna; A'yun, Qonita Qurrota; Putra, Fachrian Bimantoro; Mulyadi, Taqriri Kamal
Journal of Research Applications in Community Service Vol. 2 No. 4 (2023): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v2i4.2439

Peran teknologi dalam pengembangan proses pembelajaran Matematika maupun Statistika sangat krusial seiring dengan perkembangan teknologi khususnya di era Big Data. Teknologi memungkinkan guru untuk secara efektif dan efisien menjalankan proses pembelajaran yang berkaitan dengan pengolahan data dan analisis data Statistika. Kemampuan Statistika menjadi penting untuk diajarkan lebih dini di sekolah sehingga guru perlu meningkatkan kompetensinya dalam pembelajaran yang memanfaatkan teknologi di bidang Statistika. Oleh karena itu, dilaksanakan kegiatan Pengabdian kepada Masyarakat berupa pelatihan penggunaan perangkat lunak RStudio Cloud untuk meningkatkan kompetensi pembelajaran Statistika bagi guru SMA/MA khususnya yang tergabung dalam MGMP Matematika SMA/MA Kota Samarinda. Metode pelatihan yang digunakan adalah tatap muka, ceramah, latihan, dan diskusi. Peserta mengerjakan tes sebelum dan sesudah pelatihan. Diperoleh data tes awal dengan nilai rata-rata 119,1667 dan data tes akhir dengan rata-rata 140,8333 yang kemudian dianalisis menggunakan statistik deskriptif dan Uji-t. Hasil pengolahan data menunjukkan bahwa nilai signifikansinya 0,000 < α=0,05. Berdasarkan hasil tes, diperoleh bahwa terdapat perbedaan rata-rata hasil tes akhir peserta dibandingkan dengan rata-rata hasil tes awal. Dengan demikian, kegiatan pelatihan memberikan pengaruh positif bagi guru-guru Matematika SMA/MA di Kota Samarinda terkait pemahaman dan implementasi aplikasi RStudio Cloud dalam upaya meningkatkan kompetensi guru-guru dalam pembelajaran Statistika.

TARIFF ANALYSIS OF MOTOR INSURANCE USING GENERALIZED LINEAR MODEL (GLM) AND GRADIENT BOOSTING MACHINE (GBM) Alsitaningtyas, Yunike Jemis Fifnelavindy; Muhammad, Hubbi; Effendie, Adhitya Ronnie
Jurnal Matematika UNAND Vol. 15 No. 1 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.1.78-94.2026

he insurance sector operates by managing the transfer of risk from policyholders to insurance providers, where premiums are charged as compensation for the assumed risk. Traditionally, premium determination in motor vehicle insurance relies on the Generalized Linear Model (GLM), which requires the response variable to follow a distribution from the exponential family and may have limitations in capturing non-linear relationships and complex interactions among rating factors. To address these limitations, this study compares the performance of the Generalized Linear Model (GLM) and the Gradient Boosting Machine (GBM) in modeling claim frequency and claim severity for motor vehicle insurance premiums. The analysis is conducted using an insurance dataset obtained from a public data repository, and both models are evaluated using K-Fold Cross Validation. Model performance is assessed based on the Root Mean Square Error (RMSE), which measures the average magnitude of prediction errors and is commonly used to evaluate predictive accuracy. The results indicate that the GBM consistently produces lower RMSE values than the GLM for both claim frequency and claim severity modeling, indicating superior predictive performance. However, despite its higher accuracy, the GBM model lacks the interpretability inherent in the GLM framework, which remains crucial for transparency and regulatory considerations in insurance premium determination. These findings suggest that while GBM is effective for improving prediction accuracy, GLM remains valuable for interpretability, and a complementary use of both approaches may provide optimal results in actuarial pricing applications

Regression Analysis for Multistate Models Using Time Discretization with Applications to Patients’ Health Status Utami, Rianti Siswi; Effendie, Adhitya Ronnie; Danardono, Danardono
Journal of Fundamental Mathematics and Applications (JFMA) Vol 8, No 2 (2025)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v0i0.28439

This paper addresses the estimation of multistate models in discrete time, which are widely used to describe complex event histories involving transitions between multiple health states. Accurate estimation of transition intensities and probabilities is essential for understanding disease progression and evaluating the impact of covariates. However, conventional estimators such as the Nelson–Aalen estimator often produce rough estimates, especially in sparse data settings. To improve estimation, we apply kernel smoothing to Nelson–Aalen estimators of transition intensities. Transition probabilities are then derived via product-integrals of the smoothed intensities. Covariate effects on transition intensities are modeled using the Cox proportional hazards model. Rather than modeling covariate effects on transition probabilities indirectly through their influence on transition intensities, we model them directly using pseudo-values of state occupation probabilities obtained through a jackknife procedure. These pseudo-values are treated as outcome variables in a Generalized Estimating Equation (GEE) framework. The proposed methodology is applied to patient visit data from a clinic in West Java, Indonesia, where it successfully captures both the progression dynamics across health states and the influence of key covariates.

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