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91	Others	ABS-74
On constructions of topological BE-algebra Abdul Rouf Alghofari (a*), Corina Karim (a) (a) Dept. Mathematics Universitas Brawijaya Jln. Veteran 169 Malang, Indonesia * abdul_rouf[at]ub.ac.id Abstract In this research, a study will be conducted on topological concepts in an algebraic structures commonly referred to as BE-algebra. Research within the scope of algebra usually begin from existing studies by adding certain properties or axioms or by relaxation, namely loosening an existing properties or axioms. In this study, the properties of the algebraic structure combined with the topological concepts in it will be studied. The study of these properties includes an extension to the topological spaces of the direct product. Keywords: Topology, BE-algebra Share Link | Plain Format | Corresponding Author (Abdul Rouf Alghofari)

92	Others	ABS-81
A White Noise Approach to Stochastic Currents of Fractional Brownian Motion Herry Pribawanto Suryawan Department of Mathematics, Sanata Dharma University, Yogyakarta, Indonesia Abstract By using white noise calculus, we study the stochastic currents \(\xi(x)\), \(x\in \mathbb{R}^d\), of \(d\)-dimensional fractional Brownian motion with Hurst parameter \(H\in (0,1)\). We prove that for any non-zero \(x\in \mathbb{R}^d\) the stochastic currents are Hida distributions for any Hurst parameter \(H\) and for any dimension \(d\ge 1\). For \(x=0\) \(\xi(x)\) is a Hida distribution under the condition \(dH< 1\). To handle the remaining case, that is for \(x=0\) and \(dH\ge 1\), a renormalization procedure is needed. In this case, we prove that the renormalized stochastic current \(\xi^{(N)}(x)\), \(N\in \mathbb{N}\), exists as a Hida distribution if \(2N(H-1)+dH>1\). Keywords: Stochastic currents - fractional Brownian motion - white noise analysis Share Link | Plain Format | Corresponding Author (Herry Pribawanto Suryawan)

93	Others	ABS-93
Validation of DualSPHysics for 3D dam-break flow with different types of obstacles Rustan , M.Rizqie Arbie , Acep Purqon Department of Physics, Bandung Institute of Technology, Bandung, Indonesia Abstract DualSPHysics has been widely used in fluid flow modeling and its interaction with solids. In this study, 3D dam-break flow interacting with various types of obstacles are numerically simulated by the smoothed particle hydrodynamics (SPH) method. To validate the DualSPHysics, the first step involves simulating a 3D dam-break flow against a vertical wall. The results from the DualSPHysics simulation are then compared with experimental data and outcomes from other numerical method. Then, we apply DualSPHysics for 3D dam-break flows against a perpendicular obstacle, a inclined obstacle with the angle of inclination 37, 45, and 53, and a curve obstacle with the curvature angle 30, 60, and 90 are simulated. Pressure are analyzed at various positions within the simulation domain as a function of time. The results show that DualSPHysics successfully models the fluid from a dam-break interacting with obstacles accurately. The curvature obstacle with angle 60 can be considered one of the optimal designs for minimizing the collision force of the fluid following a dam-break. Keywords: DualSPHysics, fluid, dam-break, SPH Share Link | Plain Format | Corresponding Author (Rustan Rustan)

94	Others	ABS-95
Simple Evaluation Model of GeneXpert Utilization in Detecting Tuberculosis in West Java Annisa Aditsania, Fanny Kartika Sari, Arrofiatuz Zahra, Melly, Iko Sutrisko Prakasa Lay, Shionita Dwilani Nainggolan, Nuning Nuraini Institut Teknologi Bandung Abstract Tuberculosis (TB) is a preventable and generally curable disease. However, according to the WHO 2023 report, TB is the second deadliest disease after COVID-19 and has a mortality rate twice that of HIV/AIDS. Indonesia is the second highest contributor to TB cases after India in that year. Based on data from the West Java Provincial Health Office (2023 data plus February 1, 2024), West Java has the highest number of TB cases in Indonesia. To address this, West Java has 247 healthcare facilities providing 290 GeneXpert machines spread across 27 districts and cities in West Java. Using TB data from 2023 in cities/districts of West Java, this study built a model to estimate TB cases based on population density, testing rate, positivity rate, number of GeneXpert machines, and number of individuals tested using GeneXpert. Additionally, this study conducted clustering of cities/districts in West Java using K-Means based on the percentage of GeneXpert utilization. From the evaluation conducted, it was concluded that (1) positivity rates among regions in West Java are relatively similar, (2) reported TB cases result from varying levels of testing, (3) in 2023, there were 9 districts/cities with GeneXpert utilization over 1, averaging 1.243, and (4) the overutilization of GeneXpert in West Java in 2023 can be reduced by optimizing existing GeneXpert modules without acquiring new equipment. Keywords: tuberculosis, evaluation, clustering, modeling Share Link | Plain Format | Corresponding Author (Annisa Aditsania)

95	Others	ABS-97
Multiple seasonal time series forecast using decomposition, ARIMA model and discrete Fourier transform: a preliminary study. Kong Hoong Lem, Yi Xian Yap Universiti Tunku Abdul Rahman Abstract Multiple seasonalities often appear in time series observed at high frequency. For example, an hourly observed data may exhibit multiple seasonal patterns due to combination of daily, weekly, monthly or even yearly periodicity. In this study, we first decomposed the data into components using MSTL. For the seasonal components, we leveraged the properties of discrete Fourier transform to serve as a regressor, whereas the trend and the remainder components underwent an ARIMA model. Experiments were done on two datasets and compared with the TBATS approach. The proposed method yielded superior forecast performance. Keywords: multiple seasonal time series, discrete fourier transform, ARIMA, MSTL Share Link | Plain Format | Corresponding Author (Kong Hoong Lem)

96	Others	ABS-100
Stochastic Influence on Gradient Numerical Methods for Nonlinear Least Squares Problems Gerend Christopher (1), Janson Naiborhu (1) (1) Department of Mathematics, Institut Teknologi Bandung, Jl Ganesa 10 Bandung 40132, Indonesia Abstract The development of knowledge in the fields of mathematics, science and technology has brought humans into the information and digital era. This change leads to changes in the amount of data collected to be extracted and used for various purposes, one of them through mathematical models. There are many forms of mathematical models that are built to solve cases in certain domains. In this paper, the focus will be on solving nonlinear least squares problems. In building a model, the model that wanted to be built is an optimal model, that is, it has good accuracy and is efficient. In practice, models are built using numerical methods. The main purpose of this research is to investigate the influence of stochastic in numerical methods that utilize gradients, namely Levenberg-Marquardt, on accuracy and computational efficiency. Apart from that, several numerical results from several Stochastic Levenberg-Marquardt variants sampling and data taken in clusters will be compared. The result of this paper is Stochastic Levenberg-Marquardt with 100, 200, 300, and 400 are superior for having a very low relative error to Levenberg-Marquardt and faster than Levenberg-Marquardt in computational time Keywords: Nonlinear Least Squares, Stochastic Levenberg-Marquardt, Cluster, Accuracy, Computational Efficiency Share Link | Plain Format | Corresponding Author (Gerend Christopher)

97	Others	ABS-101
Energy Conservation of Linear Wave Equations on Equilateral Quantum Graphs Mohammad Januar Ismail Burhan (a*)- Yudi Soeharyadi (b) a) Mathematics Study Program, Department of Mathematics and Technology Information, Institut Teknologi Kalimantan, Karang Joang, Balikpapan, 76127, Kalimantan Timur, Indonesia b)Department of Mathematics, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Jl. Ganesha, 10, Bandung, 40132, Jawa Barat, Indonesia Abstract We study the conservation of energy of the linear wave equation on an equilateral quantum graph. We equipped the quantum graph with Dirichlet, Neumann-Kirchhoff, and Robin vertex conditions. We also show that the linear wave^s energy conservation remains relevant on the quantum graph. Moreover, our results are any properties of the conservation problem on the quantum graph and simulation that show the energy is conserved using the amplitude of the solution of linear wave equations on each edge. Keywords: Linear Wave Equations- Conservation of Energy- Quantum Graphs Share Link | Plain Format | Corresponding Author (Mohammad Januar Ismail Burhan)

98	Others	ABS-107
The Implementation of Principal Component Analysis (PCA) and K-Means Clustering for Heavy Metals Content in Well Water in Bandung Regency Devri Maulana, Udjianna Sekteria Pasaribu, Nurul Fahimah, Katharina Oginawati, Kurnia Novita Sari ITB Abstract Excessive levels of heavy metals in water sources can degrade water quality and harm humans. Nowadays, there has been little research on heavy metal content in well water in the Upper Citarum Watershed. Well water samples were taken from 160 locations, each containing ten heavy metals (arsenic, cadmium, chromium, cobalt, copper, iron, lead, manganese, mercury, and zinc). To determine the similarity between locations based on heavy metal content, cluster analysis was conducted using the K-Means method. However, the dimensions of the data are quite large (ten dimensions) so it is necessary to reduce the dimensions with principal component analysis (PCA) first. From the PCA results, the initial ten variables were reduced to eight new variables, capturing 87.7% of the information from the original data. Subsequently, clustering with K-Means was performed using these eight new variables, resulting in the formation of two clusters. Keywords: Analysis Multivariat, PCA, K-Means Clustering Share Link | Plain Format | Corresponding Author (Devri Maulana Maulana)

99	Others	ABS-109
On Generalization of k-space Dewi Kartika Sari, Zhao Dongsheng FMIPA UGM, MME-NIE NTY Abstract In this paper, we study a generalization of -k--space, a topological space that is closely related to locally compact spaces. A topological space -(X, \tau )- is called -S--defined if -A \substeq X- is open, whenever -A \cap K- is open in -K- for any -K &#8712- S(X)-, where -S(X)- is a family of subsets of -X-. By considering different families S(X), we obtain different types of spaces. In particular, we show that the Scott spaces of posets are -S--defined. Keywords: -k--space, locally compact, Scott space Share Link | Plain Format | Corresponding Author (Dewi Kartika Sari)

100	Others	ABS-114
On stably convergence index of a sequence of real-valued functions Atok Zulijanto Department of Mathematics, Universitas Gadjah Mada Abstract In this paper, we introduce an ordinal index of a sequence of real-valued functions on a metrizable space, called the stably convergence index. The stably convergence index has some similar properties with convergence index. We also prove that for any ordinal number &#120573-<&#120596-1, using stably convergence index, we obtain a finer gradation on the class of all real-valued functions with convergence index &#120573-. Keywords: stably convergence index, convergence index. Share Link | Plain Format | Corresponding Author (Atok Zulijanto)

101	Others	ABS-115
A Cryptography Algorithm Using Quantum Wavelet S-Transform Muhammad Sholehuddin Yusuf(a*), Mohammad Ilham Dwi Firmansyah(a), Subiono(a) a) Faculty of Mathematics, Sepuluh Nopember Institute of Technology Surabaya, Indonesia *msholehuddinyusuf[at]gmail.com Abstract Cryptography is instrumental in the advancement of information and communication technologies, particularly in maintaining information security. In general, cryptography is divided into classical and modern cryptography. Current cryptography is threatened to be solved by quantum cryptography, which is a cryptographic algorithm based on quantum phenomena such as superposition and entanglement. This paper discusses how to construct a cryptographic algorithm using quantum wavelet s-transforms. Encryption is based on analysis of the quantum wavelet s-transform, and decryption is based on the synthesis process of the quantum wavelet s-transform. The encryption key is based on many transformations and levels. The decryption key consists of three parts, namely the encryption key, binary code, and permutation matrix code. Based on the test results and analysis, it is found that this cryptographic algorithm is good based on the correlation between plaintext and ciphertext, the quality of encryption, and the size of the key space. It is also time efficient because it has a quantum complexity of O(n) or linear complexity. Keywords: Cryptography- Qubit- Quantum Wavelet S-Transform Share Link | Plain Format | Corresponding Author (Muhammad Sholehuddin Yusuf)

102	Others	ABS-117
Wave Optics in Gravitational Lensing Premana W. Premadi and Imamal Muttaqien Institut Teknologi Bandung Abstract Since its first detection in 2015, gravitational wave is studied more seriously and has opened many doors to relativistic astrophysics and cosmology. Most of gravitational wave sources are extragalactic at cosmological distances. As many of distant electromagnetic radiation sources are gravitational lensed, it is reasonable to suspect that the trajectory of gravitational wave could also be deflected by strong compact gravitational potential. This paper shows preliminary study of gravitational lens of gravitational wave. While most cosmological lens cases could be described in geometrical optics, lensing of gravitational wave requires wave optics approach. Here we illustrate the case by applying simple gravitational potential in the typical range of galaxy masses. Interference and diffraction patterns are features of this kind of lenses. Keywords: gravitational lensing- gravitational wave Share Link | Plain Format | Corresponding Author (Premana W. Premadi)

103	Others	ABS-120
IMPLEMENTATION OF DUAL RECIPROCITY BOUNDARY ELEMENT METHOD TO SOLVE THE MATHEMATICAL MODEL OF STEADY INFILTRATION IN HETEROGENEOUS SOILS VERTICALLY Armando Deminto Paa, Imam Solekhudin Department of Mathemathics, Universitas Gadjah Mada Abstract The Dual Reciprocity Boundary Element Method (DRBEM) is applied to the Richard^s equation for steady infiltration in unsaturated soil. The types of soil observed are Guelph Loam (GL) and Pima Clay Loam (PCL). This study observes infiltration in vertically heterogeneous soil. A mathematical model for infiltration in heterogeneous unsaturated soil is built using the Richard^s equation and the Gardner model, with soil parameters provided by GL and PCL. The observed heterogeneous soil types include GL-PCL and PCL-GL. The numerical simulation results provide the hydraulic conductivity values for each type of soil. Based on the numerical results of the hydraulic conductivity, the soil water potential values can be calculated using the Gardner model. Observations regarding the hydraulic conductivity and soil water potential values obtained from both homogeneous and heterogeneous soil are conducted in this study. Keywords: Richard^s equation, Gardner^s model, Hydraulic conductivity, Soil water potential Share Link | Plain Format | Corresponding Author (Armando Deminto Paa)

104	Others	ABS-123
A modified model of the relationship between inflation and unemployment using delay differential equations Steven Sergio (a*), Jalina Widjaja (a) a) Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung Jalan Ganesa 10, Bandung 40132, Indonesia *20822010[at]mahasiswa.itb.ac.id Abstract In this paper, a model of the relationship between inflation and unemployment is discussed. The model is based on Philips curve which is modified by involving delay differential equations. Using the data of inflation and unemployment in Indonesia from 2022 to 2024, we show that the model represents a more accurate relationship between inflation and unemployment. Keywords: inflation- unemployment- Philips curve- delay Share Link | Plain Format | Corresponding Author (Steven Sergio)

105	Others	ABS-124
Classification of Bandung Regency Well Water Based on Mineral Elements using Agglomerative Clustering Katharina Oginawati, Indah Rachmatiah Siti Salami, Nurul Fahimah, Yuli Sri Afrianti, Grace Angelia, Fadhil Hanif Sulaiman Bandung Institute of Technology Abstract Wells are sources of water used to meet daily needs, such as drinking water. However, the quality of well water cannot always be considered safe depending on the mineral elements in it. Therefore, a method is needed to classify well water quality safely. Cluster analysis is a statistical method for grouping data by maximizing the similarity of data in a cluster and minimizing the similarity of data with other clusters [1]. This research was conducted on 6 mineral elements (i.e., As, Cd, Fe, Mn, Pb, and Zn) in well water spread across 160 locations in Bandung Regency. The locations are clustered using Agglomerative Clustering with various linkage parameters (i.e., Single, Average, and Complete) as well as various distance metrics (i.e., Euclidean and Manhattan) [2]. The aim of this research is to identify the most optimal linkage cluster analysis method based on distance metrics and evaluate well water quality based on regional characteristics analysis. Each linkage is evaluated to determine the optimal number of clusters (k) using the Mojena method. Then, the optimal linkage is selected using the Silhouette Coefficient. The two best cluster methods were obtained- those are for metric Euclidean is Average Linkage and for metric Manhattan is Single Linkage. Next, each cluster member in both methods is mapped based on drinking water quality standards, and then the mean is calculated for each cluster. This is useful for determining the level of well water quality in each cluster. These clusters have a range of well water quality levels from 1 to 4, with the higher the level the better. Then, an analysis of regional characteristics was carried out to evaluate the map of the distribution of well water quality in each region. The results show that the Average linkage with Euclidean metrics is more representative of well water data than the Single linkage with Manhattan metrics. The results of this research can be an important basis for safe water resource management. Keywords: Well, Agglomerative, Bandung Regency Share Link | Plain Format | Corresponding Author (Grace Angelia)

106	Others	ABS-131
Please Just Try to Submit This Sample Abstract Please Just Try to Submit This Sample Abstract Please Just Try to Submit This Sample Abstract You Can Edit It Again Later Abstract Please Just Try to Submit This Sample Abstract You Can Edit It Again Later Keywords: Please Just Try to Submit This Sample Abstract Share Link | Plain Format | Corresponding Author (Novry Erwina)

107	Others	ABS-136
Stability Analysis of Mixing Problem with Cascading Tanks Robby, Jalina Widjaja Department of Mathematics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung, Indonesia. (E-mail: robbyrobbyby[at]gmail.com, jalina[at]itb.ac.id) Abstract Several cases of mixing problems which yield to a system of first order differential equations are investigated. When the perfectly mixed assumption is lifted, the model will be changed into a system of delay differential equation. The method of steps can be employed to find its solution. The behavior of the solutions is also compared to the behavior of the solution of the corresponding ordinary equations, especially its stability. The stability aspect can be analyzed by considering the solution of the characteristic equation corresponding to its system. Keywords: Key words and Phrases: mixing problem, mixing time, delay differential equation, stability analysis Share Link | Plain Format | Corresponding Author (Jalina Widjaja)

108	Probability and Stochastic Analysis	ABS-52
Enhancing Drought Prediction Accuracy with Integrated GSTARIMA-DNN Models Utilizing Penman-Monteith Method for Data Acquisition Devi Munandar , Budi Nurani Ruchjana , Atje Setiawan Abdullah , Hilman Ferdinandus Pardede 1) Doctoral Program of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia 2) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia 3) Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia 4) Research Center for Artificial Intelligence and Cybersecurity, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia Abstract Accurate drought prediction is essential for effective water resource management and agricultural planning. Traditional methods often fail to capture the complex spatiotemporal dynamics of drought patterns. This study introduces an integrated model combining Generalized Spatiotemporal Autoregressive Integrated Moving Average (GSTARIMA) with Deep Neural Networks (DNN) to enhance drought prediction accuracy. We collected monthly meteorological data, including temperature, solar radiation, humidity, wind speed, and atmospheric pressure, from NASA POWER for West Java. Evapotranspiration data were calculated using the Penman-Monteith method to provide a comprehensive input dataset. The GSTARIMA component captures spatial dependencies, while the DNN component models nonlinear temporal patterns. Data were partitioned into training and testing sets, and the model was trained and validated using these subsets. Performance evaluation was conducted using Mean Absolute Percentage Error (MAPE) as the primary metric. Results indicate that the integrated GSTARIMA-DNN model significantly outperforms traditional methods, demonstrating superior accuracy in predicting drought events. The use of Penman-Monteith evapotranspiration data played a crucial role in enhancing the model^s predictive capability. This study underscores the effectiveness of integrating GSTARIMA and DNN models for drought prediction, offering a robust tool for researchers and practitioners in climate science. Future research should explore further refinements and applications of this integrated modeling approach in various climatic and geographical contexts. Keywords: GSTARIMA, Deep Neural Network, Drought prediction, Spatiotemporal modeling, Penman-Monteith , Prediction accuracy Share Link | Plain Format | Corresponding Author (Devi Munandar)

109	Probability and Stochastic Analysis	ABS-54
Numerical Solution of Stochastic Volterra-Fredholm Integral Equations Using Improvement of Block Pulse Functions Ayyubi Ahmad Topografi Kodam XVIII/Kasuari, Manokwari Selatan, Indonesia Abstract A numerical method based on the improvement of block pulse functions (IBPFs) is used to solve the stochastic Volterra-Fredholm integral equations. We obtain a stochastic integration operational matrix for an improvement of block pulse functions on the interval [0,1). By using the improvement of block pulse functions and its stochastic integration operational matrix, the problem in this research can be simplified into a system of linear algebraic equations which is used to obtain an approximate solution of the stochastic Volterra-Fredholm integral equations. Therefore, the convergence and error analyzes of the methods used were investigated. Several examples are given to demonstrate the efficiency and accuracy of the method. Keywords: Brownian Motion, Improvement of Block Pulse Functions, Ito Integral, Stochastic Integration Operational Matrix, Stochastic Volterra-Fredholm Integral Equations. Share Link | Plain Format | Corresponding Author (Ayyubi Ahmad)

110	Probability and Stochastic Analysis	ABS-69
Evaluating the Effectiveness of Mapper Compared to PCA and Factor Analysis in High-Dimensional Survival Analysis Utih Amartiwi, Yaroslav A. Kholodov Innopolis University, Russia Abstract Survival analysis is a field of statistics used to analyze the time until an event occurs. In public health, this analysis is crucial for predicting the time until a patient recovers or dies, managing bed occupancy, and more. Currently, the development of technology in biology has produced the availability of vast amounts of data and raises the challenge in handling high dimensional data. This issue brings the need of approaches for dimensionality reduction and feature selection to provide a better prediction and knowledge discovery. Topological data analysis (TDA) is a new branch of data science that analyzes the structure of data. Mapper, a TDA approach, combines clustering and graph networks techniques to uncover insights from data that help us to select some important features in high dimensional survival analysis. To evaluate the effectiveness of this feature selection, we compare the C-Index of the survival model with those obtained from Principal Component Analysis (PCA) and Factor Analysis. The results show that Mapper not only improves the existing survival model but also provides valuable information about the features that affect survival time. Keywords: topological data analysis, mapper, survival analysis, high dimensional data Share Link | Plain Format | Corresponding Author (Utih Amartiwi)

111	Probability and Stochastic Analysis	ABS-70
Implementation of Persistent Homology in Survival Analysis for Correlated and Longitudinal Data : A case study of COVID-19 Spread in Indonesia Utih Amartiwi, Yaroslav A. Kholodov Innopolis University Abstract Most survival models assume no autocorrelation between objects and time-independence. However, in many cases of disease spread, an object can potentially affect its neighbors, and the feature may change over time. To address these issues, we implemented persistent homology, a topological data analysis (TDA) approach, to handle the problems of correlated and longitudinal data. We applied this to the case of the first infection time of COVID-19 in each province in Indonesia, using the human mobility index as a feature. The results show that survival models with persistent homology achieved a higher C-Index than those without persistent homology. Keywords: topological data analysis, persistent homology, survival analysis, correlated data, longitudinal data, Covid-19 Share Link | Plain Format | Corresponding Author (Utih Amartiwi)

112	Probability and Stochastic Analysis	ABS-82
Credit Portfolio Risk Analysis: Loss Modeling and Estimation of Credit Insurance Premiums Ellys Agustina (a), Sapto Wahyu Indratno (b*), Elisa Murti Dewi (a) a) Department of Actuarial Sciences, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesa No.10, Lb. Siliwangi, Bandung, 40132, Indonesia b) Statistics Research Division, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Jl. Ganesa No.10, Lb. Siliwangi, Bandung, 40132, Indonesia *saptowi[at]itb.ac.id Abstract Credit disbursement in Indonesia has surged significantly, reaching IDR 7,090 trillion according to OJK data. Despite this increase, the Non-Performing Loan (NPL) rate of 2-3% indicates a substantial risk of defaults. Consequently, credit insurance is vital to mitigate potential financial losses and maintain economic stability in Indonesia. This research models losses due to default within a credit portfolio and determines appropriate insurance premiums. Credit risk is modeled through default events using an indicator function, and the frequency of default events is modeled using a Probability Generating Function (PGF). A large credit portfolio is divided into several exposure groups through discretization. Losses are expressed in terms of PGF, with the loss probability function using the PGF outcomes of event frequencies. This approach simplifies the calculation of large loss probabilities with the Panjer algorithm. The research^s findings indicate that expected losses can be calculated as the first derivative of the PGF loss distribution, while unexpected losses are determined using Value at Risk (VaR). Capital reserves are calculated as the difference between expected and unexpected losses. The credit insurance premium model is developed based on expectation, standard deviation, and variance principles. A generic simulation was conducted to check the effectiveness of the proposed model. The experiments show that the approach can be implemented easily and provides reasonable results. Keywords: Credit Insurance Premium- Discretization- Losses- Panjer Algorithm- PGF Share Link | Plain Format | Corresponding Author (Ellys Agustina)

113	Probability and Stochastic Analysis	ABS-89
Estimation of IFRS 17 Risk Adjustment for Surrender Risk in Life Insurance Elisa Murti Dewi (a), Sapto Wahyu Indratno (b*), Ellys Agustina (a) (a) Departement of Actuarial Sciences, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung (b*) Statistics Research Division, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung *saptowi[at]itb.ac.id Abstract Surrender risk is a risk associated with policyholders terminating their life insurance policy before its maturity or before the insured event occurs. Surrender risk is a critical consideration for life insurance company because it leads to uncertainty in the expected future cash flows of the insurer. This risk affects the finansial stability of the insurer. This study proposes a model for calculating the risk adjustment under IFRS 17 for surrender risk in life insurance portfolios. The risk adjustment is one of the components of the liability measurement under IFRS 17 that reflects the compensation required by insurers to bear uncertainties arising from non finansial risks. IFRS 17 does not specify a particular method for risk adjustment calculation, making it one of the main challenges for insurance companies in implementing IFRS 17. The proposed model is based on statistical concepts, such as convex ordering and comonotonicity of random variables to provide a closed form quantile formula for the entire portfolio. Surrender rate is assumed to follow a data driven stochastic process and the present value of future individual cash flows is calculated. We demonstrate the practical application of these formulas in the calculation of risk adjustment. Using these formulas, insurance companies can calculate the risk adjustment for surrender risk without running time consuming simulations. Keywords: Risk adjustment, Surrender risk, IFRS 17, Convex ordering, Quantile approach Share Link | Plain Format | Corresponding Author (Elisa Murti Dewi)

114	Probability and Stochastic Analysis	ABS-90
Effect of Various Propagation Types of Madden-Julian Oscillation (MJO) on 95th Extreme Probability Change in Indonesia Dendi Setiabudi1*, Juni Tika Simanjuntak1,2, Banu Wijaya Yonas1,2, Dr. Rusmawan Suwarman, S.Si., M.T.1, Dr. Muhammad Rais Abdillah, S.Si., M.Sc.1 1 Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, Indonesia *whitemailnew[at]gmail.com 2 Badan Meteorologi Klimatologi dan Geofisika (BMKG), Indonesia Abstract The Madden-Julian Oscillation (MJO) is the eastward propagation of cloud clusters and precipitation from the Indian Ocean to the Pacific Ocean [2]. Intraseasonal variability of rainfall in Indonesia region is dominated by the Madden-Julian Oscillation (MJO) [4]. Each MJO phase has different characteristics and influences from one another in Indonesia [1]. The impact of the MJO on extreme daily rainfall (x_MJO greater than equals x_c), namely the 95th extreme, is known by quantifying the difference in the probability of extreme daily rainfall during the MJO phases against the November-April (NDJFMA) (delta_P_MJO) in the equation from Xavier et al. [5]: delta_P_MJO = ((P_MJO(x_MJO greater than equals x_c) - P_NDJFMA(x_NDJFMA greater than equals x_c))/P_NDJFMA(x_NDJFMA greater than equals x_c)*100% (1) Recent research conducted by Wang et al. [3] regarding MJO propagation found that MJO has four propagation characteristics (stand, jump, fast, and slow). In this study, the difference in the 95th extreme probability is the impact of the characteristics of the four types of MJO propagation which differ from each other according to recent research by Wang et al. [3]. Quantification of the 95th extreme probability difference with seasonal time scales makes preparation in weather forecasting better. Increasing understanding of phenomena associated with extreme weather can improve the potential for extreme weather forecasting. MJO identification according to Wang et al. [3], namely by using a band-pass filter frequency of 20-70 days against the daily average Outgoing Longwave Radiation (OLR) and certain criteria. Fast Forier Transform is applied to daily average OLR. The MJOs were identified according to certain criteria and grouped based on the similarity of propagation types using the k-mean clustering method. The Indonesia region is divided into 3 to create a matrix of regional opportunities from the 95th extreme probability difference and has 3 categories, areas from the 95th extreme probability difference >15% (positive chance), <15% (negative chance), and between >- 15% to <15% (normal chance). Slow and fast during active phases 2-4 in the Indian Ocean and Indonesian region (inactive phases 6-8 in the Pacific Ocean) are followed by an increase (decrease) in daily rainfall of 6 mm (-6 mm) and the probability of 95th extremes is 67-100% (-67 to -100%). Jump and stand do not propagate through Indonesian territory, but their active phase (inactive phases) approaches Indonesian territory and causes an increase in daily rainfall of 4 mm (-4 mm) and a probability of the 95th extreme of 33-67% (-67 to -100%). The regional probability of the 95th extreme probability difference has the same pattern as the four MJO types according to the criteria of Wang et al. [3], namely following propagation during the active and inactive phases. References Hidayat, R. and Kizu, S. (2010). Influence of the Madden-Julian Oscillation on Indonesian Rainfall Variability in Austral Summer. International Journal of Climatology. Madden, R. A. and dan Julian, P. R. (1972a). Description of Global-Scale Circulation Cells in the Tropics with a 40-50 Day Period. Journal of the Atmospheric Sciences. 29(6). 1109-1123. and Julian, P. R. (1972b). Description of Global-Scale Circulation Cells in the Tropics with a 40-50 Day Period. Journal of the Atmospheric Sciences. 29(6). 1109-1123. Wang, B. G. Chen, and Liu, F. (2019). Diversity of the Madden-Julian Oscillation. Science Advance: Research Article. Wheeler, M. C. and Hendon, H. H. (2004). An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Monthly Weather Review. 132(8). 1917-1932. Xavier, P., Rahmat, R., Cheong, W. K., and Wallace, E. (2014). Influence of Madden-Julian Oscillation on Southeast Asia Rainfall Extremes: Observations and Predictability. Geophysical Research Letter. Keywords: Madden-Julian Oscillation, MJO phases, 95th extreme, 95th extreme probability difference, MJO types (stand, jump, fast, slow), band-pass filter, fast forier transform, k-mean clustering, probability matrix. Share Link | Plain Format | Corresponding Author (Dendi Setiabudi)

115	Probability and Stochastic Analysis	ABS-91
DETECTION OF JKSE STOCK PRICE CHANGES DURING THE COVID-19 PANDEMIC USING THE GAUSSIAN PROCESS METHOD Naqisya Arifani, Sapto Wahyu Indratno, Dian Anggraini, Enrico Antonius, Kahfi Rizky Kosasih Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jl. Ganesa No. 10, Lb. Siliwangi, Bandung, 40132, Indonesia Abstract The COVID-19 Pandemic has had a significant impact on the Indonesian stock market, particularly on the Jakarta Composite Index (JKSE). This study aims to detect change points in the JKSE stock prices during the COVID-19 pandemic in Indonesia using the Gaussian Process method. The motivation of the research is to understand and analyze stock market fluctuations during crisis situations like the pandemic, which can affect investment decisions. The JKSE was chosen as it reflects the condition of the Indonesian stock market. This study utilizes weekly JKSE stock price data from 2019 to 2023, analyzed using the Gaussian Process method with Radial Basis Function (RBF) and Matern kernels. The analysis involves calculating the Generalized Likelihood Ratio Test (GLRT) values to detect change points with varying threshold of 10, 20, 30, 40, and 45 to assess detection sensitivity. The results indicate the several significant events during the COVID-19 pandemic in Indonesia caused sharp increases in the GLRT values. Low and stable GLRT values indicate normal market conditions without significant changes or high volatility. In conclusion, the Gaussian Process method with RBF and Matern kernels is effective in detecting significant changes in JKSE stock prices during the COVID-19 pandemic. This methods aids investors in monitoring market volatility and planning adaptive investment strategies to navigate stock market fluctuations in crisis situations such as the pandemic. Keywords: Gaussian Process, Change Point Detection, Pandemic, COVID-19, Market Volatility Share Link | Plain Format | Corresponding Author (Naqisya Arifani)

116	Probability and Stochastic Analysis	ABS-96
Spatio-Temporal Model and Big Data Approach: GSTARIMA-ARCH Model for Rainfall Forecasting Putri Monika (a*), Budi Nurani Ruchjana (b) , Atje Setiawan Abdullah (c) , Rahmat Budiarto (d) (a) Doctoral Program of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia *Correspondence: putri17001[at]mail.unpad.ac.id (b) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia (c) Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia (d) College of Computer Science and Information Technology, Al-Baha University, Alaqiq 65779-7738, Saudi Arabia Abstract Spatio-Temporal (ST) model is an advanced statistical model in stochastic modeling that is widely used for space-time data forecasting applications. This research integrates the ST model known as the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model with the Autoregressive Conditional Heteroskedasticity (ARCH) model. The GSTARIMA-ARCH model is part of complex statistical and mathematical analysis. This model has the ability to capture time and space patterns, and can handle non-constant (heteroscedastic) error variance in the data. The integration of the GSTARIMA-ARCH model in this study is used for rainfall forecasting with a big data approach. Rainfall observation data in this study is taken from NASA POWER (Prediction of Worldwide Energy Resources) which is big data and contains global climatology information. GSTARIMA-ARCH analysis and modeling requires a sophisticated data analytics life cycle methodology for more efficient data processing and more accurate forecasting. The analysis on real data in this study involves rainfall data from districts and cities in Java Island, Indonesia. Rainfall data was selected from 1982 to 2024 with a daily time span. The results of rainfall forecasting with the GSTARIMA-ARCH model provide accurate results shown by statistical evaluation of the calculation of the Mean Absolute Percentage Error (MAPE) value. The development of the GSTARIMA-ARCH model makes a significant contribution in the application of statistical and mathematical analysis, especially in the field of stochastic modeling. The results of rainfall forecasting can be utilized by agencies in the fields of meteorology, agriculture, and natural resource processing. Keywords: GSTARIMA- ARCH- Stochastic Modeling- Big Data- Heteroskedastic- Rainfall Share Link | Plain Format | Corresponding Author (Putri Monika)

117	Probability and Stochastic Analysis	ABS-102
Beef Price Forecasting based on Temporal, Spatial and Space-Time Parameter Indices Syifa Nurul Fatimah1 , Ahmad Fuad Zainuddin2,4 , Novi Mardiana2,5 , Utriweni Mukhaiyar3 1)Master Program of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia, syifanf34[at]gmail.com 2)Doctoral Program of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia 3)Statistics Research Group, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia, utriweni[at]itb.ac.id16 4)Business Mathematics Department, School of STEM, Universitas Prasetiya Mulya, Jalan BSD Raya No. 1, Tangerang 15339, Indonesia, ahmadfuadzain[at]gmail.com 5)Industrial Engineering Department, Engineering Faculty, Universitas Sangga Buana, Jalan PHH Mustofa No. 68, Bandung 40124, Indonesia, novi.mardiana[at]usbypkp.ac.id Abstract Beef is among the most sought-after commodities in Indonesia, resulting in significant price fluctuations, particularly during periods such as religious holidays. These price variations affect inflation and necessitate adjustments in government policies concerning beef distribution and imports. Therefore, it is essential to analyze and predict beef prices using empirical data from regions with the highest levels of beef production and consumption. This study aims to examine beef price data through the lenses of temporal, spatial, and space-time dependencies within Java. The methodologies employed in this research include ARIMA, Semivariogram, Kriging, and GSTAR models applied to weekly beef price data from Java. The findings of this study reveal that beef price fluctuations in Java are primarily influenced by temporal factors, particularly major religious holidays, rather than by location or a combination of location and time. However, there are spatial variations in beef prices across different observation locations. The best predictive model for forecasting beef prices is the ARIMA model. These results provide valuable insights into the patterns of beef prices based on temporal, spatial, and space-time parameters, offering a robust framework for understanding and anticipating price dynamics in the region. Keywords: beef price, ARIMA, kriging, GSTAR, forecasting Share Link | Plain Format | Corresponding Author (Syifa Nurul Fatimah)

118	Probability and Stochastic Analysis	ABS-111
Dynamical System Modeling in Asset and Liability Management Marvin Tandy, Prof. Marcus Wono Setya Budhi, Ph.D. Department of Mathematics, Institut Teknologi Bandung Abstract Insurance companies must ensure a balance between the profits generated from their investment and the liabilities they owe to policyholders. This is widely known as asset and liability management (ALM). One important element of ALM involves forecasting the long-term financial status of the company. Therefore, a discrete time dynamical system is developed to illustrate the fluctuations in the financial components of an insurance company. Subsequently, simulations are conducted based on the constructed model for cases both with and without mortality. The model follows the approach of Gerstner (2008), taking into account varying premium amounts and durations, a stochastic capital market, a dynamic management strategy, as well as mechanisms for establishing reserves. The financial components arising in the model are calculated recursively, allowing for easier and more efficient simulations. Keywords: asset and liability management, dynamical systems, stochastic model Share Link | Plain Format | Corresponding Author (Marvin Tandy)

119	Probability and Stochastic Analysis	ABS-113
Sensitivity Analysis of Thiele^s Differential Equation Ferdinand Laurencius Yonathan, Marcus Wono Setya Budhi, Dila Puspita Faculty of Mathematics and Natural Science, Bandung Institute of Technology Abstract In this paper, we will conduct sensitivity analysis on dual-state Thiele^s differential equation, which specifies the continuous evolution of insurance policy values over time. Analysis is focused on the impacts of expenses, sum assured and interest rate amounts. Force of mortality is determined using an estimation to the Indonesian Mortality Table IV, which is based on Gompertz^s Law. Annual rate of premium will be calculated using the equivalence premium principle. Force of interest is based on the stochastic CIR^s short-rate model, of which the market price of risk parameter is varied. Keywords: Thiele^s differential equation, policy value, CIR, premium, sum assured Share Link | Plain Format | Corresponding Author (Ferdinand Laurencius Yonathan)

120	Probability and Stochastic Analysis	ABS-119
Utilizing Trajectory Matrices and Singular Value Decomposition (SVD) for Multivariate Transformation in Time Series Analysis Dina Prariesa1, Udjianna Sekteria Pasaribu2, Utriweni Mukhaiyar2 1Doctoral Program in Mathematics, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Jalan Ganesha no 10, Bandung 40132, Indonesia 2Statistics Research Division, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Jalan Ganesha no 10, Bandung, 40132, Indonesia Abstract The trajectory matrix provides a mechanism for converting univariate time series data into multivariate form. This study utilizes the structural properties of the Hankel matrix (H), which supports weakly stationary processes with a lag of 1. Research on data matrices in Time Series Analysis (TSA) remains limited. Consequently, this study examines the Box-Jenkins TSA technique to investigate autocorrelation behavior in the Autoregressive (AR) model using the Singular Value Decomposition (SVD) approach on the H matrix. SVD, a multivariate analysis technique, aims to reduce dimensionality while preserving critical information from the observed data. The role of SVD in TSA is expanded by considering parameter properties, stationarity, and matrix embedding in time series models. An alternative approach with SVD explores the construction and significance of the trajectory matrix in AR modeling. This research focuses on the properties and requirements of the H and matrix dimension considerations in SVD. The initial research phase involved numerical simulations of time series models, particularly the AR(1) and AR(2) models. The results of PACF and scree plots from SVD generally exhibit similar patterns. This leads to the hypothesis that SVD on the H matrix could serve as an alternative method for order identification in the AR model, in addition to PACF. The findings provide an initial indication of future research potential. The methodology involves exploring, adapting, and generalizing previous research results. By examining these aspects, this research aims to offer new insights and methodological advancements in the field of TSA. Keywords: Time Series, Singular Value Decomposition, Hankel Matrix Share Link | Plain Format | Corresponding Author (Dina Prariesa)

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