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271	MATHEMATICS AND STATISTICS	ABS-134
Survival Analysis of Package Delivery Time from Shopee to Users on Java Island Adella Puspitasari, Muhammad Bayu Nirwana, Isnandar Slamet Statistics Departement, Faculty of Math and Science, Sebelas Maret University Jalan Ir. Sutami No.36, Kentingan, Kec. Jebres, Kota Surakarta, Jawa Tengah 57126, Indonesia *humas[at]mail.uns.ac.id Abstract Online shopping is the choice of many people to satisfy their needs because of high mobility every day. Shopee is a mobile marketplace that is currently used by all. Shopee has collaborated with many shipping service companies to support the convenience of consumers in buying and selling. Based on a survey by Ipsos about consumer considerations when shopping on e-commerce platforms in Indonesia, delivery time is one of the most important things. Besides that, Shopee has quality delivery services in second place after Tokopedia. Java Island is called the central economy facilitated by adequate digital infrastructure. Therefore, it is necessary to analyze factors that affect package delivery time from Shopee. Survival analysis is a statistical method used to analyze the time an event occurs over a certain period. One method of survival analysis that can be used in this study is the Cox Proportional Hazard regression must satisfy the proportional hazard assumption, where the ratio of the two hazard values must be constant with time. Therefore, the factors that significantly affect package delivery time using the Cox Proportional Hazard regression at significance level 5% are domicile, type of stuff, time of order, types of the expedition, distance, and cost. Keywords: Shopee- delivery time- survival analysis- Cox Proportional Hazard- proportional hazard assumption Share Link | Plain Format | Corresponding Author (Adella Puspitasari)

272	MATHEMATICS AND STATISTICS	ABS-135
Parameter Estimation of Spatial Ensemble Model Output Statistics Fajar Dwi Cahyoko(a*), Sutikno (a) and Purhadi (a) a) Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya, Indonesia *fajardwicahyoko[at]gmail.com Abstract Numerical Weather Prediction is a weather forecasting method that is translated into a system of mathematical equations that are solved by numerical methods. The transformation of the basic theory of NWP into computer code still produces errors. Several statistical methods have improved accuracy and reduced bias in NWP forecasts. One of them is statistical postprocessing using Ensemble Model Output Statistics (EMOS). EMOS is a variant of multiple linear regression traditionally used for deterministic forecasting. In performance, EMOS can provide a predictive probabilistic density function and cumulative distribution function from a continuous weather variable ensemble forecast at a single observation site, without considering spatial correlation. Unlike EMOS, Geostatistical Output Perturbation (GOP) considers spatial correlations between multiple locations simultaneously. However, the GOP only applies to a single deterministic forecast. Spatial Ensemble Model Output Statistics (SEMOS) is a method that combines EMOS and GOP. SEMOS is expected to be able to make up for the shortcomings of the EMOS and GOP methods. The parameter estimator method for SEMOS is divided into several stages, namely the estimation of EMOS parameters using Maximum Likelihood Estimation with newton Raphson^s numerical iteration, followed by the estimation of spatial correlation parameters using the Weighted Least Square approach with Limited Memory BFGS (L-BFGS) iterations and finally the estimation of SEMOS model regression parameters with the same stages as EMOS parameter estimation. Keywords: EMOS, GOP, SEMOS, MLE, L-BFGS Share Link | Plain Format | Corresponding Author (Fajar Dwi Cahyoko)

273	MATHEMATICS AND STATISTICS	ABS-136
THE SIGNIFICANCE INFLUENCE OF DIMENSIONS ON THE READING LITERACY ACTIVITY INDEX IN INDONESIA IN 2019 WITH GENERALIZED LINEAR MODEL (GLM) Grace Primayanti (a*), Rebecca Ester M. Sihotang (b*) a,b) Faculty of Mathematics and Sciences, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia Abstract Issues that related to literacy are one of the focuses highlighted by government in Indonesia currently. This is motivated by the low literacy skills of students in Indonesia based on Indonesia PISA^s score in 2018. Based on the research of the Indonesia^s Ministry of Education and Culture which describes the index of reading literacy activity for each province in Indonesia 2019. That research examines the literacy index based on four dimensions, namely the dimension of access, proficiency, alternatives, and culture. Based on that research, this research aimed to analyze the dimensions that provide the strongest significance of the four dimensions to the reading literacy activity index. The mathematical model that is used in this research is multiple linear regression with Generalized Linear Model (GLM). In this research, the response variable is the reading literacy activities index, while the predictor variables are dimension of access, proficiency, alternatives, and culture. Based on this research, it was found that the access dimension had the most significant effect on the reading literacy activity index with coefficient &#946-=0.36018. Therefore, the access dimension which is the availability of literacy resources needs to be improved. Keywords: Reading Literacy Index- Literacy Support Dimensions- Generalized Linear Model Share Link | Plain Format | Corresponding Author (Grace Primayanti)

274	MATHEMATICS AND STATISTICS	ABS-139
Grouping of Objects in Preclinical Trial Post COVID 19 Vaccination with the Kmeans Algorithm Nor Laela Ramadhaniyah(a*,c), Heri Kuswanto (a), Chairul Anwar Nidom(b,c) a) Departement of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember Kampus ITS, Sukolilo, Surabaya 60111, Indonesia *norlaelaramadhaniyah35[at]gmail.com b) Faculty of Veterinary Medicine, Airlangga University Mulyorejo, Kec. Mulyorejo, Kota SBY, Jawa Timur 60115 Indonesia c) Professor Nidom Foundation Jl. Wisma Permai Tengah I Blok AA No.2, Mulyorejo, Kec. Mulyorejo, Kota SBY, Jawa Timur 60115 Indonesia Abstract The COVID 19 virus is a virus that causes respiratory problems. Since it was discovered at the end of 2019, the origin of this virus has not been known with certainty. The COVID 19 virus spread quickly throughout the world, so that the World Health Organization (WHO) declared it a pandemic. In addition to taking control measures, many countries in the world have started making vaccines to prevent the spread of the COVID 19 virus. One of the series of vaccine trials, the COVID 19 vaccine also requires preclinical trials on animals to determine the safety and efficacy of the COVID 19 vaccine before clinical trials on humans. Preclinical trials of the COVID 19 vaccine used treatment time, namely the time after vaccination (h28) and after the booster (h56) with eleven parameters divided into two measurements, namely hematology and body condition. Preclinical testing was carried out on thirty six experimental animals, namely Macaca fascicularis (long tailed monkey). In this study, grouping of research objects was carried out using the K Means algorithm using the Silhouette and Gap Statistics methods to determine the value of k, then the differences in the groups after vaccination (h28) and after booster (h56) were identified. The results of grouping each measurement using the K Means method yielded 2 groups, with cluster 2 having an average of each factor greater than group 1. Keywords: Praclinical trial vaksin COVID 19- K Means algorithm- Silhouette- Gap Statistics Share Link | Plain Format | Corresponding Author (Nor Laela Ramadhaniyah)

275	MATHEMATICS AND STATISTICS	ABS-140
Exploration and Evaluation of Fuzzy C-Means Clustering for Classification of Health Workers in West Java, Indonesia Atsila Nurtsabita, Achmad Fauzan Statistics Department, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia, Indonesia Abstract Cluster analysis places a set of objects into several groups based on their similarity in nature or characteristics. Fuzzy C-Means (FCM) clustering divides data into members of all clusters based on their degree of membership. This study aims to obtain profiling of data on the number of health workers at community health centers (Puskesmas) in the province of West Java, Indonesia, in 2020 based on their similarity. Because the data contains multicollinearity, dimension reduction is performed on the data using Principal Component Analysis (PCA). From the results of PCA followed by FCM clustering. The goodness of the FCM method model is based on three validation values, namely: (1) Partition Entropy (PE), (2) Partition Coefficient (PC), and (3) Modified Partition Coefficient (MPC). The PE validation value was 0.339, PC was 0.793, and MPC was 0.585. Based on these three indicators, the data on health workers are divided into two clusters. Cluster 1 consists of 5 Regencies/Cities, and Cluster 2 consists of 22 Regencies/ Cities. Keywords: Fuzzy C-Means, Health Workers, Modified Partition Coefficient, Partition Coefficient, Principal Component Analysis Share Link | Plain Format | Corresponding Author (Achmad Fauzan)

276	MATHEMATICS AND STATISTICS	ABS-150
Information insertion into vector matrices for steganography applications Natalis Ransi^1), Muh. Ikram Marhiansyah^2), Edi Cahyono^2,*), Arman^2) 1) Department of Computer Science FMIPA Universitas Halu Oleo 2) Department of Mathematics FMIPA Universitas Halu Oleo *) Email of correspondence: edi.cahyono[at]uho.ac.id Abstract Steganography is a technique of hiding secret data within an ordinary, non-secret, file or message in order to avoid detection. In this paper we introduce a concept of vector martices that is applied on steganography. A vector matrix is a matrix where the elements are vectors. Qualitatively, a vector matrix may be presented as a digital image. In this case the image size (in pixels) represents the dimension of the matrix. The elements of the matrix are 3-dimensional vectors of I^3, where I = {0, 1, 2, ..., 255}. We consider an information that will be hidden in a digital image. To do so, we convert the digital image into a vector matrix, transfering the information in ASCII, then inserting it into some elements of the matrix. We conduct a numerical experiment in python, and analyze the digital images before and after insertion. Keywords: Digital image, steganography, vector matrix Share Link | Plain Format | Corresponding Author (Edi Cahyono)

277	MATHEMATICS AND STATISTICS	ABS-151
Simulation of the Properties of Noncoprime Graph of Finite Group Verrel Rievaldo Wijaya (*), Abdul Gazir Syarifudin Department of Mathematics, Bandung Institute of Technology, Bandung 40132, Indonesia *20121005[at]mahasiswa.itb.ac.id Abstract In this paper, we consider some kind of finite groups such as dihedral and generalized quaternion group. The dihedral group of order \( 2n \) denoted by \(D_{2n} \) is the symmetry group of a regular n-polygon consisting of rotation and reflection elements and the composition of both elements. Another group that has a similar structure to dihedral group because of its highly related presentations is called generalized quaternion group \( Q_{4n} \). We then construct a noncoprime graph of these two groups, that is the graph where two elements are connected if the order of that elements is not coprime to each other. Many properties can be studied from this graph such as shape, spectrum, chromatic numbers, etc. By utilizing the Python programming language, we can draw this graph and simulate the properties concerning this graph to get more insight on it. Keywords: noncoprime graph, dihedral group, generalized quaternion group, python, networkx Share Link | Plain Format | Corresponding Author (Verrel Rievaldo Wijaya)

278	MATHEMATICS AND STATISTICS	ABS-155
Estimation of Health Insurance Ownership of Each District in Surabaya Using Small Area Estimation with Hierarchical Bayes Approach Rifda Zukhrufi Almas*, Agnes Tuti Rumiati, Heri Kuswanto Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia *rifdazukhrufia15[at]gmail.com Abstract The development goals of an area can be achieved if the welfare of the community occurs as a whole. Community welfare includes economic capacity, access to education, and health services. The program that is carried out to realize the SDGs in the health sector is the Program Indonesia Sehat with 3 pillars, namely the healthy paradigm, health services and national health insurance. Health insurance is gradually being applied to all Indonesian residents with the aim of ensuring that participants receive health care benefits and health protection. This study focuses on the level of ownership of health insurance in Surabaya. The data on the percentage of ownership of health insurance available so far can only be known at the city level. Therefore, the method used in this research is Small Area Estimation (SAE) using Hierarchical Bayes (HB) approach. SAE is used to estimate parameters by using auxiliary variable information, and produces a smaller bias than direct estimation. The data source for this research is secondary data obtained from the results of the 2020 Survey Sosial Ekonomi Nasional (Susenas) and publications by the Central Bureau of Statistics (BPS). The auxilary variables consist of characteristics that are thought to be related to the percentage of ownership of health insurance, namely, the percentage of the productive age group, population density, percentage of the working population, ratio of educational facilities, ratio of facilities to anticipate/mitigate natural disasters, ratio of economic facilities, ratio of BTS (Base Transceiver Station), and ratio of health facilities. Keywords: Health Insurance- Hierarchical Bayes- SAE- SDGs Share Link | Plain Format | Corresponding Author (Rifda Zukhrufi Almas)

279	MATHEMATICS AND STATISTICS	ABS-159
Stability Analysis of Limit Cycle on Single Nonlinear Oscillator Model Nahrul Mubarok, Rudy Kusdiantara, Nuning Nuraini Institut Teknologi Bandung Abstract An oscillator is a tool that is widely applied in everyday life because of its ability to produce oscillatory motion without the need for continuous external force. To get the desired motion, it is necessary to analyze the bifurcation of the external force parameters of the oscillator system. The oscillatory motion result can be viewed as a periodic solution based on the system of differential equations which is modelled by an oscillator system. In this paper, we propose a scaling method that can be used to find the periodic solution (limit cycle) of an oscillator equation. This method will be used to find a periodic solution of the oscillator model with a nonlinear force and its period. The stability of the periodic solution can be determined by using the Floquet theory. We also perform pseudo-arclength continuation to obtain the bifurcation diagram when linear damping, i.e., the bifurcation parameter varies. This method is quite effective in obtaining the bifurcation diagram when the solution branches have a turning point. By using this method, the behaviour of the oscillator system can be categorized into three cases with respect to changes in the linear damping parameter. Keywords: nonlinear oscillator, limit cycle, Floquet theory, numerical continuation, bifurcation diagram Share Link | Plain Format | Corresponding Author (Nahrul Mubarok)

280	MATHEMATICS AND STATISTICS	ABS-167
A note on some Endpoint Estimates of Commutators of Fractional Integral Operators Verrel Rievaldo Wijaya, Denny Ivanal Hakim (*), Marcus Wono Setya Budhi Department of Mathematics, Bandung Institute of Technology, Bandung 40132, Indonesia *dhakim[at]math.itb.ac.id Abstract It is known that fractional integral operators are not bounded from Lebesgue integrable functions to Lebesgue space with related exponents. Based on some recent results by Schikorra, Spector, and Van Schaftingen, we investigate commutators of fractional integral operators on Lebesgue integrable functions. We establish a weak type estimates for these commutators generated by essentially bounded functions. Under the same assumption, we also prove that the norm of these commutators are dominated by the norm of the Riesz transform. Keywords: fractional integral operators, commutators, Riesz transform, boundedness Share Link | Plain Format | Corresponding Author (Verrel Rievaldo Wijaya)

281	MATHEMATICS AND STATISTICS	ABS-168
Reduction of Data Dimensions Using PCA and SVD with Case Studies of ITB Tracer Study Data Dina Prariesa (a), Udjianna Sekteria Pasaribu (b*), Utriweni Mukhaiyar (b) a) Doctoral Program in Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia b) Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia *Corresponding Author: udjianna[at]math.itb.ac.id Abstract As big data develops, techniques to reduce dataset dimensions are becoming increasingly important in statistical analysis. Researchers and data analysts have been faced with the task of reducing a high-dimensional data set to a collection of lower-dimensional data sets without significant loss of information. The most common techniques of reducing dataset dimensions uses Principal Component Analysis (PCA) which retains as much variation as possible in the data set by transforming to a new set of variables, namely the principal components, which are uncorrelated, and ordered so that the first few variables retain most of the variation present in all original variable. In this paper, a theoretical study of the data dimension reduction technique is carried out using PCA and its derivative, namely the Singular Value Decomposition (SVD) technique. Furthermore, the relationship between these two data reduction techniques, PCA and SVD, will be applied to the tracer study data of ITB alumni, especially study programs in the Faculty of Mathematics and Natural Sciences (FMIPA), School of Pharmacy (SF), and the School of Life Sciences and Technology (SITH). Keywords: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Tracer Study Share Link | Plain Format | Corresponding Author (Dina Prariesa)

282	MATHEMATICS AND STATISTICS	ABS-171
Comparison of Optimization Project Scheduling with Term Cost-Balancing by CPM-Earliest, CPM-Latest, and CPM-Monte Carlo Methods (Case of Housing Project Type 65) Lilik Muzdalifah (a*), Kris Gular Pamitra (a), Eriska Fitri Kurniawati (a), Rita Yuliastuti (a), Jamaliatul Badriyah (b) a) Universitas PGRI Ronggolawe Jalan Manunggal 61, Tuban 62381, Indonesia *muzdalifahlilik[at]gmail.com b) Universitas Negeri Malang Jalan Semarang 5, Malang 65145, Indonesia Abstract The most effective and realistic project scheduling is the one which is able to integrate various aspects such as time, resources, and costs. Many scheduling methods to optimize time and cost have been introduced by researcher. The most widely used among those methods is Critical Path Method (CPM). In addition, the CPM method, namely CPM (Earliest) and CPM (Latest), can also be combined with other methods such as in previous studies using the CPM-Cuckoo Search (CS) and CPM-Monte Carlo (MC) methods in terms of optimize project scheduling with daily cost-balancing. Considering that the disbursement of project funds is actually carried out in a periodic manner, the researchers have to conduct research that is more realistic. The CPM method has been proven to optimize the project scheduling with term cost-balancing more effectively and efficiently than daily cost-balancing. This research will study how CPM-MC can optimize the term cost-balancing case and then compare the optimal value of the term cost-balancing of the type 65 housing project scheduling calculated by the CPM (Earliest), CPM (Latest), and CPM-MC methods. Keywords: Project Scheduling, Term Cost Balancing, CPM, Monte Carlo Share Link | Plain Format | Corresponding Author (Lilik Muzdalifah)

283	MATHEMATICS AND STATISTICS	ABS-172
Modeling infectious disease trend using Sobolev polynomials Rolly Czar Joseph Castillo (a*), Victoria May Mendoza (b,a), Jose Ernie Lope (a), and Renier Mendoza (a) a) Institute of Mathematics, University of the Philippines Diliman *rtcastillo1[at]up.edu.ph b) Natural Sciences Research Institute, University of the Philippines Diliman Abstract Trend analysis plays an important role in infectious disease control. An analysis of the underlying trend in the number of cases or the mortality of a particular disease allows one to characterize its growth. Trend analysis may also be used to evaluate the effectiveness of an intervention to control the spread of an infectious disease. However, trends are often not readily observable because of noise in data that is commonly caused by random factors, short-term repeated patterns, or measurement error. In this paper, a smoothing technique that generalizes the Whittaker-Henderson method (WHM) to infinite dimension and whose solution is represented by a polynomial is applied to extract the underlying trend in infectious disease data. The solution is obtained by projecting the problem to a finite-dimensional space using an orthonormal Sobolev polynomial basis obtained from Gram-Schmidt orthogonalization procedure and a smoothing parameter computed using the Philippine Eagle Optimization Algorithm, which is more efficient and consistent than a hybrid model used in earlier work. Because the trend is represented by the polynomial solution, extreme points, concavity, and periods when infectious disease cases are increasing or decreasing can be easily determined. Moreover, one can easily generate forecast of cases using the polynomial solution. This approach is applied in the analysis of trends, and in forecasting cases of COVID-19. Keywords: Whittaker-Henderson method, data smoothing, Sobolev space Share Link | Plain Format | Corresponding Author (Rolly Czar Joseph Castillo)

284	MATHEMATICS AND STATISTICS	ABS-173
An Empirical Studies on Online Gender-Based Violence: Classification Analysis Utilizing XGBoost Arum Handini Primandari (a*), Putri Ermayani (b) (a) Universitas Islam Indonesia (b) Orbit Future Academy Abstract Keywords: Classification- Cyberbullying- Cyber Harassment- XGBoost Share Link | Plain Format | Corresponding Author (Arum Handini Primandari)

285	MATHEMATICS AND STATISTICS	ABS-185
Analysis of Indonesian New Education Policy of Entrance Selection for Candidate of Undergraduate Students Leonardus Reinaldy Christianto (a), Rebecca Ester M Sihotang (b), Aditya Wisnugraha Sugiyarto (c) (a,b,c) Faculty of Mathematics and Science, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia Abstract The choice of college entrance selection system is an important aspect of predicting student learning success. The Indonesian Ministry of Education, Culture, Research, and Technology has decided to change the joint selection system for entering higher education in 2023 by eliminating the academic ability test (TKA) and only focusing on the potential scholastic test (TPS) which focuses more on the reasoning abilities of prospective students. By adopting the cross-industry standard process for data mining (CRISP-DM) method but giving some modifications to it, using data from the top 1000 schools participating in the Computer-Based Written Examination (UTBK) in 2021 and 2022 obtained from the Institution of University Entrance Exam page (LTMPT). This study aims to find an in-depth analysis of the reasons whether the policy of eliminating TKA was the wise decisions. The results of this study indicate that there is a correlation between TPS and TKA so the role of TPS can replace TKA because TPS scores can predict scores on TKA. In addition, through this research, it was also found that TPS scores can be used for further analysis and provide advice to prospective students to choose a major that focuses on science or social. Keywords: UTBK- Academic Ability- Scholastics Ability- CRISP-DM Share Link | Plain Format | Corresponding Author (Rebecca Sihotang)

286	MATHEMATICS AND STATISTICS	ABS-186
Non-Hierarchical Cluster Analysis to Group Tektite in Australian Strewnfield Based on Composition of Geochemical Data Using the K-Means Clustering Method Authors: Juli Mulyanto (a), Triyana Muliawati (a*) a) Department of Mathematics, Institut Teknologi Sumatera Jalan Terusan Ryacudu, Lampung 35365, Indonesia *triyana.muliawati[at]ma.itera.ac.id Abstract Cluster analysis aims to group objects based on the similarity level of characteristics among these objects. This study examined cluster analysis to classify tektites in the Australian Strewnfield based on the composition of geochemical data using the k-means clustering method. The data used were compiled from as much as 60 tektite samples, and the variables used were nine geochemical data components consisting of {SiO}_2,\ {\ TiO}_2,\ {\ Al}_2O_3,\ \ FeO,\ M\ nO,\ \ MgO,\ \ CaO,\ {\ Na}_2O,\ and K_2O. Before grouping using the k-means clustering method, principal component analysis is carried out to reduce dimensional data that can represent diversity and the Silhoutte method to obtain optimal k cluster values. Based on the results of the main component analysis, 2 main components were formed from the 9 variables analyzed. And based on the results of the grouping analysis using the k-means clustering method, the results show that the tektite in the Australian Strewnfield is divided into three clusters with a silhouette value of 0.6068 , that significanly depend on silica minerals abundances ({SiO}_2). Keywords: Tektite- Silhouette Method- Principal Component Analysis- Geochemistry- K-Means Clustering Share Link | Plain Format | Corresponding Author (Juli Mulyanto)

287	MATHEMATICS AND STATISTICS	ABS-188
Topological Indices of Relative g-noncommuting Graph of Dihedral Groups Nur Ain Supu (a) Intan Muchtadi Alamsyah ( a) Erma Suwastika (a*) Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, 40132 Bandung, Indonesia *ermasuwastika[at]itb.ac.id Abstract Let G be a finite group, H be a subgroup of G and g be a fixed element of G. The relative g-noncommuting graph \Gamma_{g,H,G} of G is defined as a graph with vertex set is G and two distinct vertices x and y are adjencent if [x,y]\neq g and [x,y]\neq g^{-1}, where at least x or y belong to H. In this paper, we will discuss the relative g-noncommuting graph of the dihedral groups D_{2n} in particular case when n is a prime number. We give several topological indices of the relative g-noncommuting graph of the dihedral groups D_{2n} including the first Zagreb index, Wiener index, Edge-Wiener index, Hyper-Wiener index, and Harary index. Keywords: Relative g-noncommuting graph, Dihedral group, Topological indices Share Link | Plain Format | Corresponding Author (Nur Ain Supu)

288	MATHEMATICS AND STATISTICS	ABS-190
Application of Max-Plus Algebra for Scheduling the Alethaskin Body Lotion Production System Winarni (a*)- Umul Kalsum Septiani (a)- Kartika Nugraheni (a) (a) Institut Teknologi Kalimantan Jalan Soekarno-Hatta KM 15 Karang Joang Balikpapan 76127, East Kalimantan, Indonesia *winarni[at]lecturer.itk.ac.id Abstract Companies must plan and determine a good production system to be able to meet market demand. CV Dermalis Group is a cosmetic manufacturing company. The main product is Alethaskin body lotion. There are two variants of Alethaskin body lotion, namely Infused Body Night Serum with AHA and Gluthation (night lotion) and Booster Perfect Brightening Day Lotion with Repairing and UV Filter (day lotion). The problem that often occurs in Alethashin^s body lotion production is irregular production because there is no proper production schedule. Therefore, in this study, the Alethaskin body lotion production system was scheduled using the max-plus algebraic model. The existing production system was arranged in the form of a production flow diagram and graph. Then, the max-plus algebraic model for the production schedule is constructed. The model that had been constructed was analyzed to determine the eigenvalues and eigenvectors using the power algorithm with Scilab and the max-plus algebra toolbox, to obtain a periodic production system, after that the production schedule is arranged. Based on the research results, the eigenvalue is 285, which means that the period between one production to the next is 285 minutes. Furthermore, a good initial condition was obtained to start production, in the P1 to P10 process, starting respectively at 1, 1, 17, 20, 53, 61, 91, 99, 188, and 224 minutes. Alethaskin body lotion production schedule is periodically carried out for 24 hours of work with a shift system. It can be produced in five batches for 24 hours. Keywords: cosmetics- graph- max-lus algebra- production system- scheduling Share Link | Plain Format | Corresponding Author (Winarni Winarni)

289	MATHEMATICS AND STATISTICS	ABS-192
Determination of Multi-Asset Black-Scholes Differential Equation Using Backward Stochastic Differential Equations (BSDEs) Aimmatul Ummah Alfajriyah (*), Endah RM Putri, Kistosil Fahim Department of Mathematics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Jl. Raya ITS, Sukolilo, Surabaya, 60111, Indonesia. (*)aimmatulummahff[at]gmail.com Abstract Multi-asset Black-Scholes differential equation is widely used in option pricing. One method of option pricing is backward stochastic differential equations (BSDEs). A BSDE has important applications in the field of mathematical finance, since the BSDE can be used in pricing financial products on incomplete markets. A primary objective of this study is to determine the multi-asset Black-Scholes differential equation using the BSDE. This research begins with constructing a multi-asset portfolio in the form of BSDE. Relationship between BSDE and partial differential equations (PDEs) is given by Feynman-Kac theory. Using the Feynman-Kac theory, we derive BSDE of multi-asset portfolio such that it has a unique solution. It is also a solution of multi-asset Black-Scholes differential equation. Then, by deriving BSDE, the multi-asset portfolio can be transformed into the multi-asset Black-Scholes differential equation. We also obtain the exact solution of the multi-asset option price on the basket option, by transforming multi-asset Black-Scholes differential equation into a diffusion equation. As an application, some simulations of multi-asset option prices are conducted. Keywords: Basket Option- Black-Scholes Equation- BSDEs- Feynman-Kac- Multi Asset Option- Option Pricing Share Link | Plain Format | Corresponding Author (Aimmatul Ummah Alfajriyah)

290	MATHEMATICS AND STATISTICS	ABS-194
Comparison of Support Vector Machine of Unbalanced Microarray Data Classification using The Resampling Method: Synthetic Minority Oversampling Technique (SMOTE) and Radial Based Oversampling (RBO) Diana Nurlaily(a*), Irhamah (b), Santi Wulan Purnami (b) a) Study Program of Statistics, Institut Teknologi Kalimantan, Kampus ITK-Karang Joang, Balikpapan, 76127, Indonesia. *diana.nurlaily[at]lecturer.itk.ac.id b) Department of Statistics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo Surabaya 60111, Indonesia. Abstract Microarray data contains hundreds to thousands of observable genes. Microarray data that is usually used for research is DNA Microarray. DNA Microarray is used to determine the level of gene expression and the gene sequence in the sample. This type of data is used to collect information from tissue and cell samples about differences in gene expression that can be useful for diagnosing disease or differentiating certain type of tumors. Some characteristics of microarray datasets are high dimensions and imbalance. These characteristics can lead to inappropriate classification predictions. This study aims to compare which resampling method is better between the Synthetic Minority Oversampling Technique (SMOTE) and Radial Based Oversampling (RBO) methods in microarray data classification using a Support Vector Machine (SVM). The data used in this study are breast cancer and lymphoma which have an imbalance ratio and a different number of variables. Based on the results of the analysis, it was found that the goodness of SVM model with SMOTE was better than using RBO. Keywords: Micrroarray, RBO, SMOTE, SVM Share Link | Plain Format | Corresponding Author (Diana Nurlaily)

291	MATHEMATICS AND STATISTICS	ABS-201
PREDICTING THE RANKING OF POSITION-BASED BEST PLAYERS IN FOOTBALL COMPETITIONS IN INDONESIA USING SUPPORT VECTOR REGRESSION Muhammad Noorridho Ilmansyah & Dedy Dwi Prastyo Institut Teknologi Sepuluh Nopember, Abstract The industrial era brings football into the modern industry, which happens globally, including in Indonesian football competitions. One component that must be improved in this business is league quality. If the quality of the league is good, then indirectly, the value of television broadcasting rights will increase, which indicates an increase in media exploration, and further, it can undoubtedly attract sponsors to become official partners in the league. The more and more significant sponsorship value indicates that the competition has bright prospects from the business side. The value of sponsorship can also make plots such as awarding. A good award is based on data and statistics relevant to the field^s situation. Therefore, an awarding assessment method is needed to look at the statistics of each player. Statistics can help us understand the game and how to judge the players^ performance properly. However, the use of statistics must also be contextual because the use of statistics without context will result in poor interpretation. Therefore, the awarding assessment that is carried out apart from statistics also uses a different assessment for players in each position because each player has a different task. This research employs Support Vector Regression, with benchmarks Ridge Regression method and Principal Component Regression, to predict which week the best players in each position will be known almost surely before the competition end. Keywords: Please Just Try to SuFootball Industry, Ranking of each Position, Support Vector Regression, Ridge Regression, Principal Component Regressionbmit This Sample Abstract Share Link | Plain Format | Corresponding Author (Muhammad Noorridho Ilmansyah)

292	MATHEMATICS AND STATISTICS	ABS-202
Coprime Degree of Some Groups and Its Relation to Coprime Graphs Abdul Gazir Syarifudin (a), Rian Kurnia (a), Pudji Astuti Waluyo (a), Ahmad Muchlis (a*) (a) Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, 40132 Bandung, Indonesia *muchlis[at]math.itb.ac.id Abstract Coprime degree of a group can be thought of as a generalization of commutativity degree of a group. Coprime degree of a group is defined to be the probability that two random elements in the group have coprime orders. In this paper, we derive explicit description of coprime degree for several finite groups with respect to the prime decompositions of their group orders, including cyclic groups, dihedral groups, and generalized quaternion groups. Furthermore, we establish the relation between coprime degree of a group and its coprime graph. Keywords: Coprime degree, Commutativity degree, Coprime graph, Cyclic groups, Dihedral groups, Generalized quaternion groups. Share Link | Plain Format | Corresponding Author (Abdul Gazir Syarifudin)

293	MATHEMATICS AND STATISTICS	ABS-207
Assessment of dependence between first and second vaccinations with active COVID-19 cases in Jakarta, Indonesia through C- and D-Vine copula based regression Falah Novayanda Adlin(a), Atina Ahdika(a*) (a) Department of Statistics, Universitas Islam Indonesia *atina.a[at]uii.ac.id Abstract Until now, Indonesia is still battling the COVID-19 virus, which has an increasing number of cases with an increasing varied type of virus. Jakarta is the province with the highest number of vaccinations 1 and 2. However, the number of active COVID-19 cases is still the highest in Indonesia. Based on these facts, there may be another dependency structure between the number of vaccinations and active cases of COVID-19 that ordinary linear correlations cannot capture. This study identified the dependence between the number of vaccinations 1 and 2 with the number of active cases of COVID-19 in Jakarta Province using the C- and D-vine copula models. The data used is time series data for the three variables from January 13, 2021, to September 24, 2021. Furthermore, an estimation of the number of active COVID-19 cases is carried out based on the number of vaccinations 1 and 2 with a vine copula-based regression model. The analysis results show that the dependence between vaccinations 1 and 2 is positive, between active cases of COVID-19 and vaccination 1 is positive, and between active cases of COVID-19 and vaccination 2 is negative. Finally, the best estimation results for active COVID-19 cases were obtained using a D-Vine copula-based regression model with a MAPE value of 0.597%. Keywords: COVID-19, C-Vine copula, dependency, D-Vine copula, vaccination Share Link | Plain Format | Corresponding Author (Atina Ahdika)

294	MATHEMATICS AND STATISTICS	ABS-209
Sentiment Analysis and Topic Modelling of Bjorka Using Support Vector Machine and Latent Dirichlet Allocation Muhammad Muhajir (1,3*), Dedi Rosadi(2) (1*) Department Mathematics, Gadjah Mada University, Sendowo, Sinduadi, Kec. Mlati, Kabupaten Sleman, Yogyakarta 55281, Indonesia. *muhammadmuhajir[at]mail.ugm.ac.id (2) Department Mathematics, Gadjah Mada University, Sendowo, Sinduadi, Kec. Mlati, Kabupaten Sleman, Yogyakarta 55281, Indonesia. (3)Department Statistics, Islamic University of Indonesia, Jl. Teknika, Krawitan, Umbulmartani, Kec. Ngemplak, Kabupaten Sleman,Yogyakarta 55584, Indonesia Abstract A wide range of data is now easily accessible via the microblogging service Twitter thanks to the rapid advancement of technology. The bjorka controversy, one of the most talked-about topics right now, has generated numerous comments from the general public and thus has risen to the top. The Bjorka phenomenon is an obvious example of cybercrime, with a sharp uptick in incidents occurring in Indonesia during the Covid-19 pandemic. Sentiment analysis employing the Support Vector Machine technique allows for the statistical analysis of public opinion about Bjorka as it appears on the Twitter social network. Latent Dirichlet Allocation (LDA) will be used to analyze the sentiment analysis with SVM results, which have been separated into positive and negative sentiments. In this study, using LDA for sentiment analysis resulted in an accuracy of 89.5%. Dismantling government data, including personal data and government crimes, was the most positively predicted topic, with 75.2% of all predictions leaning in that direction. It is hoped that the government will be able to use the information gleaned from this study to better understand the public^s perspective and the trust deficits that need to be addressed. Keywords: Bjorka, Cybercrime, SVM, LDA Share Link | Plain Format | Corresponding Author (Muhammad Muhajifr)

295	MATHEMATICS AND STATISTICS	ABS-212
Application of Markov Chain for Estimating Earthquake Magnitude Pattern Around Java Island Afif Amrullah Taufiq, Udjianna Sekteria Pasaribu*, RR Kurnia Novita Sari Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Indonesia *Corresponding Author: udjianna[at]math.itb.ac.id Abstract The purpose of this study is to apply a Markov chain model in analyzing the behaviour magnitude of earthquakes around Java Island. A 3x3 transition probability matrix is constructed by data from Indonesian Agency for Meteorology, Climatology, and Geophysics from January 1, 2015 to December 20, 2022. The data is divided into three states, namely minor earthquake (magnitude <5), moderate earthquake (5<= magnitude <6), and strong earthquake (magnitude >=6). The study shows that: 1) number of communication class is one since there are transitions between all pairs of states in the matrix and therefore it is possible to reach any state from any other state , 2) type of each state is recurrent since the Markov chain is irreducible and has at least one state is recurrent. Keywords: Markov Chain, Transition Probability Matrix, Classification of States, Earthquake Magnitude Share Link | Plain Format | Corresponding Author (Afif Amrullah Taufiq)

296	MATHEMATICS AND STATISTICS	ABS-215
A Space-Time Stochastics Model for Analysing Hydrological Feedback of Tropical Peatland Utriweni Mukhaiyar, Tarasinta Prastoro, Adilan W. Mahdiyasa* Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia *adilan[at]math.itb.ac.id Abstract Indonesian peatlands serve as one of the most efficient carbon sinks and are one of the largest near-surface reserves for terrestrial organic carbon. The total area of tropical peatlands in Indonesia is around 13.43 million ha or equivalent to 7% of Indonesia land area, and they store about 57 gigatonnes of carbon Gt C. Tropical peatlands are highly significant to global efforts to combat climate change, as well as broader sustainable development goals. The protection and restoration of tropical peatlands are vital in the transition toward a low-carbon and circular economy for Indonesia. However, tropical peatlands are complex systems with the potential to shift dramatically between equilibrium states in response to hydrological changes. One approach to understanding this complex behaviour is through the space-time stochastics model that provides a prediction on how the water table position evolves on a wide range of timeframes. We estimate the water table position of Indonesian peatlands using Generalized Space-Time Autoregressive (GSTAR) and analyse the implication of water table fluctuation on the peatland carbon stock and resilience. This approach would provide an early warning system that can be used as a reliable indicator of catastrophic events, for instance, peat fire that commonly occurs in Indonesia. Keywords: Generalized Space-Time Autoregressive, stochastics model, tropical peatland hydrology, carbon balance Share Link | Plain Format | Corresponding Author (Adilan Mahdiyasa)

297	MATHEMATICS AND STATISTICS	ABS-216
On The Existance of MDS Matrices over \(\mathbb{F}_{p}+v\mathbb{F}_{p}\) Defita, Intan Muchtadi-Alamsyah Mathematics, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia Abstract An \(n\times n\) matrix is called MDS (Maximum Distance Separable) matrix if and only if its submatrices are non-singular. In 2022, Adhiguna et al proved that over a field of characteristic \(p>2\) there is no orthogonal circulant MDS matrix of even order \(m\) and of order divisible by \(p\). In this research, we observe the existence of MDS matrices over ring \(\mathbb{F}_{p}+v\mathbb{F}_{p}\) where \(v^{2}=v\). Using the fact that for every \(a+vb\in \mathbb{F}_{p}+v\mathbb{F}_{p}\) can be written as \(a+vb=v(a+b)\oplus (1-v)a\), we prove that there is no orthogonal circulant MDS matrix of even order and of order divisible by \(p\) over \(\mathbb{F}_{p}+v\mathbb{F}_{p}\). Keywords: MDS matrix- orthogonal- circulant Share Link | Plain Format | Corresponding Author (Defita -)

298	MATHEMATICS AND STATISTICS	ABS-218
Modelling Indonesian Mortality Table Using Heavy-Tailed Distribution on Two Different Age Groups Marcellius K. Chandra, Udjianna S. Pasaribu*, Adilan W. Mahdiyasa Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia *udjianna[at]math.itb.ac.id Abstract In the field of life insurance, actuaries often have to determine a survival model that matches actual data before deciding a premium price based on a person age. One of the possible methods to determine insurance premiums is to use mortality tables. The mortality table is built based on a predetermined model or available data on the number of deaths. However, through these approaches, the mortality table does not model a person survival chances continuously, which produces considerable limitations in calculating the probability of survival. This study presents the possibility of modelling the Indonesian Mortality Table using heavy-tailed distribution for both genders. The results show that fitting two different distributions based on age groups produces a model that is more in line with the mortality table data than the model that relies only on one distribution. This study also provides some considerations in fitting a distribution model into mortality tables. Keywords: Survival model, mortality table, heavy-tailed distribution, life insurance Share Link | Plain Format | Corresponding Author (Adilan Mahdiyasa)

299	MATHEMATICS AND STATISTICS	ABS-221
Predicting Pfizer Stock Price using LSTM and Bi-LSTM Latifa Ega Nadhira (a), Dina Tri Utari (a*) a) Department of Statistics, Universitas Islam Indonesia, Jl. Kaliurang KM 14.5, Sleman, Yogyakarta, Indonesia * dina.t.utari[at]uii.ac.id Abstract The availability of enormous amounts of data and the rapid development of artificial intelligence and machine learning techniques make it possible to create sophisticated stock price prediction algorithms. Meanwhile, the stock market is now more challenging to understand and volatile than ever due to the ready availability of investing options. The globe is searching for a precise and trustworthy forecasting model that can capture the highly volatile and nonlinear market behavior in an all-encompassing framework. One of the stock prices that increased during the Covid-19 pandemic was Pfizer Inc., a healthcare sector company that produces the Covid-19 vaccine with claims of having a high level of effectiveness. This study aims to determine the application of LSTM and Bi-LSTM in predicting Pfizer Inc.^s stock price uses daily close prices from January 2018 to January 2022. By dividing training data by 80% and testing data by 20%, the best model for LSTM is obtained using neurons ten and epoch 1000, while Bi-LSTM uses neurons 20 and 500. The experimental findings demonstrate that, compared to Bi-LSTM models, the single-layer LSTM model offers a higher fit and excellent prediction accuracy. Keywords: Pfizer- stock price- prediction- LSTM- Bi-LSTM Share Link | Plain Format | Corresponding Author (Dina Tri Utari)

300	MATHEMATICS AND STATISTICS	ABS-228
A Mathematical Study of Effects of Delays Arising from The Interaction of Glucose and Insulin in The Body Mariani (a), Rudy Kusdiantara (b) (a) Institut Teknologi Bandung, (b) Institut Teknologi Bandung Abstract The interaction between glucose and insulin was modeled mathematically using a delay differential equation. The delay factor in the model states that there is a natural delay that occurs during the pancreas process of producing insulin and a natural delay that occurs during the process of glucose production in the blood to balance the body^s metabolism. The glucose-insulin interaction system with two delay factors will be investigated. By choosing \tau_1 dan \tau_2 as the bifurcation parameters, we can indicate that the Hopf bifurcation will appear when the time delay crosses some critical values. The linear stability of the steady-state point will be investigated and the Hopf bifurcation will be demonstrated by analyzing the characteristic equations of each case. Numerical simulations are given to describe the results of the analytical solution and changes in stability behavior will be observed. Keywords: Delay differential equation, Glucose, Hopf bifurcation, Insulin, Stability analysis Share Link | Plain Format | Corresponding Author (Mariani Mariani)

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