Proceedings of the 8th International Conference on the Applications of Science and Mathematics: SCIEMATHIC 2022; 17―19 Oct; Malaysia (Springer Proceedings in Physics, 294)

This book presents peer-reviewed articles and recent advances on the potential applications of Science and Mathematics for future technologies, from the 8th International Conference on the Applications of Science and Mathematics (SCIEMATHIC 2022), held in Malaysia. It provides an insight about the leading trends in sustainable Science and Technology. Topics included in this proceedings are in the areas of Mathematics and Statistics, including Natural Science, Engineering and Artificial Intelligence. Conference Organisation Editorial Board Preface Contents Editors and Contributors Part I Mathematics 1 Solving Lane-Emden Equation by Using Differential Transformation Method 1.1 Introduction 1.2 Methodology 1.3 Result and Discussion 1.4 Conclusion References 2 Optimization of Asset Liability Management on Textile and Garment Companies Using Goal Programming Model 2.1 Introduction 2.2 Research Methods 2.2.1 Data 2.2.2 Model Formulation 2.3 Results and Analysis 2.3.1 Optimization Results 2.3.2 Optimization Analysis 2.4 Conclusions References 3 A Two-Stage Fuzzy DEA Approach—Application to Study the Effectiveness of Indian Iron and Steel Industries 3.1 Introduction 3.1.1 Decision Making Units (DMU) 3.1.2 Multi Stage DEA 3.2 Methods 3.2.1 Collection of Data 3.2.2 Selecting Input or Output Variables 3.2.3 Mathematical Modeling 3.3 Results and Discussion 3.3.1 Stage: 1F-CCR Model 3.3.2 Stage: 2 (Marketability) 3.3.3 Tables 3.4 Conclusion References 4 An EPQ Model Under Time-Dependent Demand with Single Time Discount Sales and Partial Backlogging 4.1 Introduction 4.1.1 Assumptions and Notations 4.2 Methods 4.3 Numerical Illustration 4.4 Conclusions References 5 A Two-Stage Fuzzy DEA Using Triangular Fuzzy Numbers—An Application to Study the Efficiency of Indian Iron and Steel Industries 5.1 Introduction 5.1.1 DMUs 5.1.2 Two State DEA 5.2 Methodology 5.2.1 Data Collection 5.2.2 Selection of Input and Output Variables 5.2.3 Mathematical Modeling 5.3 Results and Discussion 5.3.1 Stage 1: F-CRS 5.3.2 Stage 2: F-VRS Model 5.4 Conclusion References 6 Life Insurance Pricing and Reserving—A Fuzzy Approach 6.1 Introduction 6.2 Methods 6.2.1 Fuzzy Approach 6.2.2 Trapezoidal Fuzzy Membership Function 6.2.3 General Form of Fuzzy Premium and Reserve Equations 6.3 Results and Discussion 6.4 Conclusion References 7 Fixed-Point Approach on Ě Fuzzy-Metric Space with JCLR Property by Implication 7.1 Introduction 7.2 Preliminaries 7.3 Implicit Relations 7.4 Main Results 7.5 Conclusion References 8 Fixed Point Results on Derived Fuzzy Norm Using Fuzzy 2-Normed Space 8.1 Introduction 8.2 Preliminaries 8.3 Fixed Point Theorem References 9 Solving Fuzzy Linear Programming Problems by Using the Limit Existence of the Fuzzy Exponential Penalty Method 9.1 Introduction 9.2 Preliminaries 9.2.1 Fuzzy Sets 9.2.2 Fuzzy Number 9.3 Fuzzy Exponential Penalty Method 9.3.1 Fuzzy Exponential Penalty Lemma 9.3.2 Fuzzy Exponential Penalty Convergence Theorem 9.3.3 Fuzzy Exponential Penalty Function Algorithm 9.4 Numerical Example 9.5 Conclusion References 10 Robust Optimization of Stock Portfolio Index IDX 30 Using Monte Carlo Simulation in Insurance Company 10.1 Introduction 10.2 Materials and Methods 10.2.1 Mean–Variance Portfolio 10.2.2 Optimization Robust Portfolio 10.2.3 Robust Portfolio Allocation 10.2.4 Monte Carlo Simulation in Portfolio 10.3 Results and Discussion 10.3.1 Robust Optimization 10.3.2 Capped Free Float Adjusted Market Capitalization Method 10.3.3 Comparison Between Mean–Variance Optimization, Robust Optimization, and IDX30 Index 10.4 Conclusions References 11 Predicting Claim Reserves When the Loss Development Factors are Unstable: A Case Study from Indonesia's General Insurance Company 11.1 Introduction 11.2 Theoretical Background 11.2.1 Chain-Ladder Method 11.3 Decision Tree 11.3.1 Classification and Regression Tree 11.3.2 Random Forest 11.4 Extreme Gradient Boosting 11.4.1 Objective Function 11.4.2 Gradient Tree Boosting 11.4.3 XGBoost Algorithm 11.5 XGBoost for Run-off Triangle Dataset 11.5.1 Feature Space 11.5.2 Tweedie Loss Function 11.5.3 Preprocessing Dataset 11.5.4 The Flow of Using the XGBoost Algorithm in Predicting Claims Reserves 11.6 The Real Dataset's Claim Reserve Calculation 11.7 Summary and Discussion References 12 Stability Analysis of the Fractional Order Lotka-Volterra System 12.1 Introduction 12.2 Methodology 12.2.1 Stability Analysis Theorems for Fractional-Order Dynamical System 12.2.2 Stability Analysis of Two-Dimensional Fractional-Order Lotka-Volterra System 12.2.3 Adams–Bashforth–Moulton Method 12.3 Results and Discussion 12.3.1 Commensurate Fractional-Order Lotka-Volterra System 12.3.2 Incommensurate Fractional-Order Lotka-Volterra System 12.4 Conclusion References Part II Statistics 13 Gender Comparative Patterns of Online Gaming Among University Students 13.1 Introduction 13.2 Methodology 13.3 Results and Discussion 13.3.1 Types of Online Games University Students Play 13.3.2 The Amount of Time and Money Spent in Online Gaming 13.3.3 Perception on Positive and Negative Implications to Online Gaming Toward Students’ Academic Performance 13.3.4 Gender Differences in Online Gaming Behavior 13.4 Conclusion References 14 Data Mining Classifier for Predicting India Water Quality Status 14.1 Introduction 14.2 Methodology 14.2.1 Data Preprocessing 14.2.2 Water Quality Index Identification and Calculation 14.2.3 Classification Method 14.3 Results and Discussion 14.3.1 Water Quality Index for India River Dataset 14.3.2 Classification Result for India River Datasets Using Seven Classification Methods 14.4 Conclusion and Recommendations References 15 Improvement of Learning Outcomes on the Limit Chapter Using KahootTM 15.1 Introduction 15.2 Material and Methods 15.2.1 Materials 15.2.2 Methods 15.3 Result and Discussion 15.3.1 Based on the Calculation of t-output and t-table 15.3.2 Based on Probability Values 15.4 Conclusion References 16 Mapping of Fish Consumption in Indonesia Based on Average Linkage Clustering Methods 16.1 Introduction 16.1.1 Background 16.1.2 Purpose of the Study 16.2 Materials and Methods 16.2.1 Data 16.2.2 Method of Analysis 16.3 Results and Discussion 16.3.1 Data Description 16.3.2 Clustering Based on Average Linkage Methods 16.3.3 Validity Cluster 16.3.4 Mapping 16.4 Conclusion References 17 Analysis of Income and Expenditure of Households in Peninsular Malaysia 17.1 Introduction 17.2 Materials and Methods 17.2.1 Data Description 17.2.2 Chi-Square Test and Bivariate Analysis 17.2.3 Linear Regression Analysis 17.3 Results and Discussion 17.3.1 Impact of Demographic Variables in Household Revenue 17.3.2 Relationship of Household Income and Household Expenditure Region 17.3.3 Significant Demographic Determinants of Household Expenditure 17.4 Conclusion References 18 A Comparative Analysis of Credit Card Detection Models 18.1 Introduction 18.2 Related Work to Credit Card Fraud Detection 18.3 Methodology 18.3.1 Dataset 18.3.2 Algorithms 18.3.3 Evaluation Metrics 18.4 Results and Discussion 18.5 Conclusions References 19 Credit Scoring Model for Tenants Using Logistic Regression 19.1 Introduction 19.2 Methodology 19.2.1 Rental Information Data 19.2.2 Logistic Regression 19.2.3 Maximum Likelihood Estimation 19.2.4 Factor Reduction in Model 19.2.5 Tenant’s Credit Scoring Model 19.2.6 Performance of Model 19.2.7 Graphical User Interface 19.3 Results and Discussion 19.3.1 Multivariable Logistic Regression Results 19.3.2 Graphical User Interface for Tenant’s Credit Scoring 19.4 Conclusion References 20 Optimization of Bayesian Structural Time Series (BSTS) Applications in Forecasting Stock Prices Through State Components Selection 20.1 Introduction 20.2 Bayesian Structural Time Series (BSTS) 20.2.1 Basic Structural Time Series Model 20.2.2 State Components 20.2.3 Markov Chain Monte Carlo (MCMC) 20.3 Other Methods 20.3.1 Holt-Winters Exponential Smoothing 20.3.2 Seasonal Autoregressive Moving Average (SARIMA) 20.4 Comparison Metrics 20.4.1 Root Square Mean Error (RMSE) 20.4.2 Mean Absolute Error (MAE) 20.4.3 Mean Absolute Percentage Error (MAPE) 20.5 Data Description 20.6 Results 20.6.1 Trend Models 20.6.2 Trend + Seasonal Models 20.6.3 Trend + Monthly Annual Cycle Models 20.6.4 Trend + Seasonal + Monthly Annual Cycle Models 20.6.5 The Best Model 20.6.6 Comparison 20.6.7 Forecast 20.7 Conclusions References 21 Prediction of the Number of BPJS Claims due to COVID-19 Based on Community Mobility and Vaccination in DIY Province Using the Bayesian Structural Time Series 21.1 Introduction 21.2 Bayesian Structural Time Series 21.2.1 Structural Time Series 21.2.2 Spike and Slab Regression 21.2.3 Markov Chain Monte Carlo 21.3 ARIMAX 21.4 Analysis and Discussion 21.4.1 Data Exploration and Visualization 21.4.2 BSTS and ARIMAX 21.5 Conclusions References 22 A Study on the New Cases of Influenza A, B, and Covid-19 in Malaysia 22.1 Introduction 22.2 Methodology 22.2.1 Dataset 22.2.2 Descriptive Statistics 22.2.3 Normality Test 22.2.4 Correlation Coefficient 22.3 Results and Discussions 22.4 Conclusion and Recommendations References 23 An Evaluation of the Forecast Performance of Neural Network 23.1 Introduction 23.2 Materials and Methods 23.2.1 Kuala Lumpur Composite Index Stock Price Data 23.2.2 Preliminary Analysis 23.2.3 Forecast Method 23.2.4 Forecast Performance Evaluation 23.3 Results and Discussion 23.3.1 Forecast Performance in Training Part 23.3.2 Forecast Performance in Testing Part 23.4 Conclusion References 24 The Impact of Government Policy Changes on Stock Market Forecasting 24.1 Introduction 24.2 Methodology 24.2.1 Naïve Method 24.2.2 Box—Jenkins Model 24.2.3 Box–Cox Transformation 24.2.4 Differencing 24.2.5 Augmented Dickey–Fuller Test 24.2.6 Autocorrelation and Partial Autocorrelation 24.2.7 Ljung–Box Test 24.2.8 Diagnostic Checking of ARIMA Model 24.2.9 Double-Exponential Smoothing Model Diagnostic Checking of ARIMA Model 24.2.10 Mean Absolute Error 24.2.11 Mean Absolute Percentage Error 24.2.12 Trend Change Error 24.3 Results and Discussion 24.3.1 Data Separation 24.3.2 Naïve Forecast Model 24.3.3 Double-Exponential Smoothing Forecast Model 24.3.4 Box–Jenkins Forecast Model 24.4 Conclusion References 25 Determining Auto Insurance Pure Premium Based on Mileage (Pay-As-You-Drive Insurance) Using Tree-Based Machine Learning 25.1 Introduction 25.2 Random Forest 25.3 Gradient Boosting Machine 25.4 Loss Function for Insurance Data 25.5 Interpretations of Machine Learning 25.5.1 Shapley Values 25.5.2 Partial Dependence Plot 25.5.3 Friedman's H-Statistic 25.6 Data Description 25.6.1 Data Source 25.7 Results 25.7.1 Modeling Claim-Frequency Using Random Forest 25.7.2 Modeling Claim-Severity Using Random Forest 25.7.3 Modeling Claim-Frequency Using Gradient Boosting Machine 25.7.4 Modeling Claim-Severity Using Gradient Boosting Machine 25.7.5 Model Evaluation 25.7.6 Interpretations of the Best Model 25.8 Discussion 25.9 Conclusion References Part III Engineering 26 The Mitigation Model of Greenhouse Gas Emissions Reduction in Coal Mining Company with Surface Mining System 26.1 Introduction 26.2 Research Method 26.2.1 Time and Location 26.2.2 Research Framework 26.2.3 Calculation of GHG Emissions in the Energy Sector 26.2.4 Calculation of Mitigation Actions Using Solar Panels 26.2.5 Calculation of Mitigation Actions with the e-Reporting System (e-SIAP) 26.2.6 Dynamic Systems Analysis 26.3 Results and Discussion 26.3.1 Total CK-KIM GHG Emissions in 2021 26.3.2 Results of GHG Emission Mitigation from the Use of Solar Panels in Extensometers 26.3.3 Results of GHG Emission Reduction Mitigation with the Implementation of the e-Reporting System (e-SIAP) 26.3.4 Modeling 26.3.5 Simulation 26.3.6 Model Validation 26.3.7 Model Scenario 26.4 Conclusions and Suggestions 26.4.1 Conclusions 26.4.2 Suggestions References 27 The Influence of Fibre Feeder Speed and Stacking Layers on Physical Properties of Needle-Punched Nonwovens from Industrial Cotton Waste 27.1 Introduction 27.2 Materials and Methods 27.2.1 Materials 27.2.2 Web Processing 27.2.3 Physical Test 27.2.4 Design of Experiment 27.3 Results and Discussion 27.4 Conclusions References 28 Exploring Cultural Learning with Vertical Chatbot: Korda 28.1 Introduction 28.2 Related Work 28.3 Methodology 28.4 Evaluations 28.5 Conclusions References 29 Robot Model to Identify the Quality of Air Indoor Area Based on Internet of Things (IoT) 29.1 Introduction 29.2 Materials and Methods 29.2.1 Project Planning 29.2.2 Electrical Design 29.2.3 Part Testing 29.2.4 Software Design 29.2.5 Mechanical Design 29.3 Results and Discussion 29.3.1 Overall Testing 29.3.2 Application of Robot Mapping 29.3.3 Wall Dimensions 29.3.4 Back to the Early Point/Back Home 29.3.5 Air Quality Error 29.4 Conclusion References Part IV Natural Sciences 30 Onion Peel for Tinted Film Applications 30.1 Introduction 30.2 Methodology 30.2.1 Materials 30.3 Results and Discussion 30.4 Conclusion References 31 Optimization of Spinning Speed for Thin Layers Formation Using Spin Coating Technique 31.1 Introduction 31.2 Materials and Methods 31.2.1 Materials 31.2.2 Thin Film Deposition 31.3 Result and Discussion 31.4 Conclusion References 32 Effect of Environmental Stress on Biomolecules Production and Cell Wall Degradation in Chlorella vulgaris 32.1 Introduction 32.2 Methodology 32.2.1 Microalgae Culture 32.2.2 Experimental Setup 32.2.3 Biochemical Analyses 32.2.4 Measurement of Cell Wall Thickness 32.3 Results and Discussion 32.3.1 Effect of Salt Stress 32.3.2 Effect of Nitrogen Stress 32.3.3 Effect of Temperature Stress 32.4 Conclusion References