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This paper explores the multifaceted world of IoT, exploring its historical evolution, core definitions, enabling technologies, architectural considerations, communication models and future predictions. Moreover, the paper critically scrutinises the potential benefits and challenges associated with IoT adoption. This study employed a systematic literature review (SLR) methodology to review existing literature between January 2020 and January 2024 on IoT technology. 20 relevant studies out of an initial pool of 45,312 studies were reviewed. The study followed the established Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework to ensure a rigorous and unbiased selection process. Findings revealed the lack of a universally accepted definition for IoT and significant variations in architectural models across scholarly works. Moreover, the study acknowledges the complex and ever-evolving nature of IoT technology, recognising its early stages of development. This dynamic technological landscape calls for continuous exploration of future research. Findings will provide valuable insights for academics, researchers, and industry professionals interested in the understanding the intricate technical landscape of IoT and its development. Moreover, by providing insights into growth predictions, benefits, and existing challenges, this will empower stakeholders to navigate the evolving IoT landscape and contribute to its future progress.

The usability and security of information system applications significantly affect the users’ willingness to adopt the applications; online banking is one such service. The emergence of innovative technologies in all facets of our daily activities makes usable security critical to protect users’ privacy and personal information. The paper aims to investigate the usability and security of the online banking authentication process. The study is based on users’ perceptions of the login system of their respective banks' online banking services, using the attitude questionnaire statements related to usability and security aspects of the authentication process. The paper presents the results of 1190 survey responses in South Africa. The findings show that younger and inexperienced users are not satisfied with the usability of online banking authentication systems as they scored the system very low compared to the older and experienced users. Given the prevalence of online security breaches, improving the authentication process' usability will help create a secure online environment.

There is inadequate knowledge on change management model (CMM) for post-acceptance phase of information systems. This paper intends to determine and model change management factors (CMFs) that explain effective use of Examination Administration System (EAS) in South Africa. A survey was used to collect data from 215 EAS users in Technical and Vocational Education and Training colleges and was analyzed quantitatively. Empirical results evince existence of casual nexuses between user involvement and change recognition, user satisfaction, performance measurement with use of EAS which through EAS adaptive use (EAU) in turn together with EAS verification, user learning representational fidelity and informed actions influence effective use of EAS. This study results add to literature by explaining the indirect mechanisms (i.e., EAS and EAU) through which the CMFs can influence effective use. This paper also offers a novel integration of Effective Use Theory and Unified Theory of Acceptance and Use of Technology with three CMFs which provides a holistic model. This study recommends that various communities endeavour to use CMM as a guide to influence user communities to effectively use information systems in diverse contexts. The model also has potential to drive improved return on investments and enhance employees’ productivity.

This study aims to generate trajectories of students in learning by using a comparison worth growing terraced paper of form informal toward formal shape. The method used in this research is the research design consisted of three stages, namely the preliminary, design experiments and retrospective analysis. In this study, a series of learning is designed and developed based on the notion of the learning process and approach PMRI. This research was conducted in SMP PGRI 1 Palembang by involving students in grade VII.2 totaling 30 students. This research produces Learning trajectory (LT), which includes three activities namely 1, activity starts from drawing two wake up the same but different size, measure each side at the wake and determine the corresponding sides to determine the ratio. Activity 2, the activity determines the relations between the two ratios to determine whether the two ratios is a comparison worth or not. Activity 3, resolve issues relating to the comparative worth of daily life-today. Results of the experiments showed that a series of learning activities that have been done to help improve students' understanding of learning valued comparison.

The rise of portable electronic devices and social media has led to new criminal activities, necessitating advancements in digital forensics. This paper introduces Small-Scale Digital Device Forensics (SSDDF), focusing on the forensic examination of miniature digital devices often used in crimes. SSDDF addresses the challenges posed by these devices, particularly in extracting and analyzing data from them. A key aspect of this research is exploring ontology in social media forensics, particularly within the Android operating system. This involves extracting digital evidence like user accounts, messages, and images from social media platforms. While the paper primarily focuses on social media data, it acknowledges the importance of the devices used in crimes. The integration of SSDDF and the analysis of Android system structures are highlighted as key advancements in digital forensic methodologies. These enhancements are expected to improve the process of collecting and analyzing digital evidence from both compact electronic devices and social media platforms. The study offers significant contributions to the field of digital forensics. It provides new strategies for more efficient and effective forensic investigations, especially in the context of extracting and analyzing digital evidence from small electronic devices and social media, thus paving the way for more robust digital evidence handling in future forensic inquiries.

Cloud-based online learning is the electronic learning activity that supports teaching and learning (T&L) that could be done from anywhere and in the world. Some of its benefits are scalability and affordability that could in a decision-making support on the mechanisms of material selection. Cloud computing has been adopted by most universities around the world. In this regard, lecturers and students will use it to facilitate T&L; however, due to concerns of information technology or systems security, cloud-based online learning users are also not immune. In this regard, the users could be affected by different cybersecurity attacks. In this paper, a systematic literature review method was used to sift the different models and solutions used to address the cybersecurity concerns surrounding cloud-based online learning. A brief Likert-scale questionnaire was used to obtained data that could corroborate the systematic literature findings. In this regard, a group of 20 online learning designers were sampled as participants. It was found that the confidentiality, integrity, and availability issues are a concern. This led to issues of security awareness, authentication and blended attacks being issues. In this regard, a cloud-based online learning model is not immune from security issues. In this paper, a conceptual framework as the line-of-defense is proposed as a solution towards having a cybersecure cloud based online learning.

The tanh method is known as an effective method for solving partial differential equations and obtaining traveling wave solutions. While well-used in continuous systems, its application to discrete systems (nonlinear difference-differential equations, DDEs) is still less explored. This paper revisits the tanh method for discrete systems, providing a simple justification for the method to produce discrete solitary wave solutions, including pulse-type and (anti)kink-type. By transforming DDEs into nonlinear ordinary differential equations (ODEs) and using polynomial forms in terms of the tanh function, we outline steps for deriving and verifying exact solutions

In this paper, an application of N-soft set on data with grouped objects will be presented using the construction concept in strait soft set theory to solve a problem in the decision-making process. For each parameter, a weight value will be determined, and from this data, a threshold will be set in such a way that a T-level soft set form is possible. This research is a descriptive qualitative study with a case study approach. An interesting result from this study is that an object with the lowest rank can have the highest rank in a certain group of objects

This paper concentrates on the optimization of elementary association rule mining. The basic approach of association rule mining generates the positive association rule but focusing on both positive and negative association rule mining to find out efficient results is lacking. Thus our aim is to provide an approach to optimize all positive and negative association rules with the help of a modified genetic algorithm. A genetic algorithm is an optimization technique that provides the best possible solutions that are stronger than the other solutions. The present approach focuses on the importance of population through mean fitness value for further genetic algorithm operation. This paper also shows a comparison between normal Apriori, the Genetic Algorithm, and our proposed algorithm. Where in as a result the proposed approach worked better than others. We believe that the proposed methodology would increase the efficiency of the Decision support system of retail stores.

Cryptography is one of the parts in mathematics, especially applied mathematics. Cryptography is the science that studies how to secure information that we don't want others to know about the information we have. Based on the development of the times, cryptography is divided into two consisting of classical cryptography and modern cryptography. In this paper, the focus of discussion is on classical cryptography. Furthermore, there are many kinds of classical cryptographic algorithms, such as the Caesar cipher algorithm, the Playfair cipher algorithm, the Vigenere cipher algorithm, and the Hill Cipher algorithm. The focus of discussion in this paper is on the Hill cipher algorithm. The modifications made to the Hill cipher algorithm lie in its key matrix. In this paper, it will be shown how to encrypt and decrypt the message we want to send using the Hill cipher algorithm with the matrix being a adjacency matrix. In which case, the adjacency matrix itself is obtained from the representation of the graph. It is hoped that this algorithm can avoid crypt attacks, especially on ciphertext only-attack. Keywords: Hill cipher algorithm, cryptography, adjacency matrix, plaintext, ciphertext MSC2020: 94A60

The purpose of this paper is to introduce the idea of how to construct transfinite numbers and study transfinite arithmetic. The research method used is a literature review, which involves collecting various sources such as scientific papers and books related to Cantorian set theory, infinity, ordinal or transfinite arithmetic, as well as the connection between infinity and theology. The study also involves constructing the objects of study, namely ordinal numbers such as finite ordinals and transfinite ordinals, and examining their arithmetic properties. The results of this research include the methods of constructing both finite and transfinite ordinal numbers using two generation principles. Both finite and transfinite ordinal numbers are defined as well-orderings that are also transitive sets. Arithmetic of finite ordinal numbers is well-known, but the arithmetic of transfinite ordinal numbers will be introduced in this paper, including addition, multiplication, and exponentiation.

Student learning activeness is very important in the learning process. However, student learning activeness remains a problem at many schools. Students observed by researchers during the field practice programme had low activeness. Based on the results of the total percentage of student learning activeness observation sheets, 37% of the group belongs in the less active category. Students' learning activeness is influenced by the approach used by the teacher during the learning process. As an implementation of the independent learning concept, the Among learning approach provides freedom and space for students to be actively involved. The freedom concept certainly does not deviate from the correct understanding of a Christian perspective, which is freedom under the authority of God. Therefore, to overcome this problem, the researcher applied the Among system. The purpose of this paper is to determine the activeness of student learning in online informatics learning using the Among system. This research is descriptive qualitative research. The results showed that the students' learning activeness having implemented the Among system had increased. The researcher suggests that teachers need to first identify students’ existent situations and conditions before implementing the Among system due to its non-suitability in any context. BAHASA INDONESIA ABSTRACT: Keaktifan belajar siswa merupakan hal yang sangat penting dalam proses pembelajaran. Namun, keaktifan belajar siswa masih menjadi sebuah permasalahan di sekolah. Siswa yang diobservasi oleh peneliti saat menjalani program praktek lapangan memiliki keaktifan yang rendah. Berdasarkan hasil total persentase lembar observasi keaktifan belajar siswa yaitu sebesar 37% tergolong pada kategori kurang aktif. Pada dasarnya sikap keaktifan belajar siswa dipengaruhi oleh pendekatan yang digunakan oleh guru dalam pembelajaran. Pendekatan pembelajaran dengan sistem among memberikan kebebasan maupun ruang bagi siswa untuk terlibat aktif dalam pembelajaran. Kebebasan yang ada tentunya tidak menyimpang dari pemahaman yang benar menurut perspektif Kristen, yaitu kebebasan yang tetap berada di bawah otoritas Allah. Oleh karena itu untuk mengatasi permasalahan ini peneliti menerapkan sistem among. Tujuan penulisan paper ini yaitu untuk mengetahui keaktifan belajar siswa pada pembelajaran informatika yang dilakukan secara daring dengan menggunakan sistem among. Penelitian ini merupakan penelitian kualitatif deskriptif. Hasil penelitian menunjukkan bahwa keaktifan belajar siswa setelah penerapan sistem among mengalami peningkatan. Saran yang diberikan oleh peneliti sebelum menerapkan sistem among guru perlu terlebih dahulu mengidentifikasi situasi dan kondisi yang ada pada siswa. Sebab tidak semua permasalahan yang ada di dalam kelas dapat terselesaikan dengan menerapkan sistem among.

This paper addresses the problem of managing the significant rise in requests for credit products that banking and financial institutions face. The aim is to propose an adaptive, dynamic heterogeneous ensemble credit model that integrates the XGBoost and Support Vector Machine models to improve the accuracy and reliability of risk assessment credit scoring models. The method employs machine learning techniques to recognise patterns and trends from past data to anticipate future occurrences. The proposed approach is compared with existing credit score models to validate its efficacy using five popular evaluation metrics, Accuracy, ROC AUC, Precision, Recall and F1_Score. The paper highlights credit scoring models’ challenges, such as class imbalance, verification latency and concept drift. The results show that the proposed approach outperforms the existing models regarding the evaluation metrics, achieving a balance between predictive accuracy and computational cost. The conclusion emphasises the significance of the proposed approach for the banking and financial sector in developing robust and reliable credit scoring models to evaluate the creditworthiness of their clients.

Bonds are known as one of low-risk investments and worth to be considered as a part of an investor's portfolio, however there are still underlying risks that could affect its price. This paper focuses on the effect of early redemption risk and default risk to a bond’s value. Using binomial interest rate tree method and its adjusted for default risk version, this paper wants to analyse how these risks affect Indonesian bonds’ values through simulations, while showing how these bonds can be used to construct the binomial interest rate trees. In the default risk simulation, more assumptions are made because of data limitations, which causes the first period recovery fraction to soar higher than the other periods. The analysis shows that, compared to present value of standard bonds, early redemption risk tends to cause the bond's present value to drop, while on the contrary, default risk tends to cause the bond's present value to rise. The cause of higher present value of bonds with default risk is explained by the high first period recovery fraction.

Graphs that have the properties of odd harmonious labeling are odd harmonious graphs. The research objective of this paper is to obtain odd harmonious labeling on layered graph C(x,y) and layered graph D(x,y). The research used in this paper is a qualitative method. The research flow consists of data collection, processing, and analysis. The data collection stage consists of constructing the definition of the new class graph, the data processing stage consists of constructing the vertex labeling and edge labeling, and the data analysis stage consists of constructing the theorem and proving it. The research results show that the layered graph C(x,y) and layered graph D(x,y) fulfill odd harmonious labeling. Such that the layered graph C(x,y) and layered graph D(x,y) are odd harmonious graphs. The benefit of this research is to add new properties of odd harmonious graphs. In addition, it does not rule out the possibility that this research can be developed again both in theory and application.

The Business Intelligence (BI) and Data Warehouse (DW) system deployed in the Nigerian National Petroleum Corporation should provide cooperate decision makers with real-time information to help them identify and understand key business factors to make the best decisions for the situation at any given time. The relentless collection of data from user interactions have introduced both a high level of complexity, as well as a great opportunity for businesses. In addition to connecting not just people, but also machines to the internet, and then collecting data from these machines via sensors would result in an unimaginable repository of data. This ever-increasing collection of data is known as Big Data. Integrating this with existing Business intelligence systems and deep analysis using Machine Learning algorithms, Big Data can give useful insights into business problems and perhaps even to make suggestions as to when and where future problems will occur (Predictive Analysis) so that problems can be avoided or at least mitigated. This paper targets at developing a system capable of optimizing a business intelligence using big data and machine learning approach. The design of a system to optimize the Business Intelligence System using Machine Learning and Big Data at NNPC was successfully carried out. The System was able to automatically analyze the sample report under NNPC permission to use and it generated expected predictive outputs which serves as a better guide to managers. When applying Deep Learning, one seeks to stack several independent neural network layers that, working together, produce better results than the already existing shallow structures.

This paper presents a novel approach for cyberbullying detection and classification in social media text using an ensemble model that combines BERT (Bidirectional Encoder Representations from Transformers) and Support Vector Machine (SVM) with grid search for multiclass classification. We have also compared the performance of our proposed with various machine and deep learning models and the results show that our proposed model outperforms other models achieving an accuracy of 90% on testing data. Further, we have used to used SHapley Additive exPlanations (SHAP) an Explainable (XAI) technique to interpret the predictions of the BERT-SVM ensemble model.

Internship programs are essential for tertiary students to gain relevant experience before entering the workforce. Managing these programs, especially in institutions such as the East Java Province Marine and Fisheries Agency (DKP), poses challenges due to the reliance on paper-based processes and the need for centralized management across 17 branches. Therefore, this research developed a "Centralized Internship Information System" (CIIS) program designed with the aim of simplifying internship registration, student placement, document management, and program monitoring at DKP and its branches. This research focuses on designing a user-friendly interface for CIIS to address these issues. The design process follows the Design Thinking method, which emphasizes deeply understanding user needs and creating innovative solutions. The effectiveness of CIIS was evaluated using the Short User Experience Questionnaire (UEQ), which assesses user experience across key aspects. The UEQ results showed an average pragmatic quality score of 1.954 (excellent) and an average hedonic quality score of 1.856, with an overall average of 1.91. These results indicate that CIIS provides a high-quality user experience, facilitating easier access to internship opportunities for students, efficient candidate selection for companies, and effective monitoring for internship managers at DKP.

This study is unearthing the localizing process of SDG in Bangladesh in meeting the Sustainable Development Goals (SDGs) targets. Though nations bear the main responsibility for the SDGs, these goals specifically call for involvement from local authorities. Around 12 of the 17 SDGs require comprehensive strategies at the grassroots level to tackle interconnected issues like poverty, poor health, social challenges, and environmental degradation, with exceptions being Goals 9, 12, 13, 14, and 17. Bangladesh's constitution wisely allocates significant responsibilities for social and economic development, including the formulation and execution of plans concerning public services and economic progress, to the local government bodies, particularly the union parishad (UP), which serves as the primary interface with the community [Article 59(2)(c)(Constitution, 2004)]. Local Government Institutions in Bangladesh are positioned to have a significant influence on the localization of the SDGs, given their proximity to the most marginalized and frequently vulnerable rural communities, allowing for direct impact. Despite this potential, they have yet to make a substantial contribution to SDG localization, and with the deadline approaching, there is a pressing need to address this issue. This paper seeks to examine the obstacles encountered by local government in Bangladesh in their efforts to localize the SDGs, aiming to gain insight into the challenges hindering effective policy implementation.

The learning and mastering of programming language syntax pose a significant challenge for non-computing students. Most teaching approaches and existing educational tools often fail to address this issue. Therefore, this paper introduces an interactive learning environment called PyLe, specifically designed for introductory programming in Python programming courses. We evaluated the effectiveness of PyLe on first-year students at North-West University in South Africa and the University of Yaoundé 1, Cameroon. Firstly, the study conducts an experiment to assess the effect of PyLe on the time taken to solve a problem and the response quality. Secondly, PyLe’s usability and its instructional value were evaluated by the students and the instructors, respectively. The results from post-test method and a quantitative survey indicate that PyLe improves students’ ability to learn and master program syntax and has a high usability rate. Moreover, feedback from students and teachers affirms PyLe’s potential to address programming syntax challenges for non-computing students. However, the analyses revealed no real relationship between the time taken to complete a task in PyLe and the quality of the solution. This study contributes to improving the teaching and learning of computer programming, which has been considered difficult for both computing and non-computing students.

This paper aims to determine students' communication skills and mathematical disposition in implementing problem-based learning strategies using the Geogebra application. This type of research was a quasi-experiment with a control group design. The subjects of this research were classes A and B. The population in this research was all students in the odd semester 2023/2024 who were taking the Plane and Space Geometry course, which consists of two classes that were tested using the t-test with data from the student's final test. Based on calculations of the results of students' mathematical communication skills, specifically the experimental class 81.00 and the control class 71.16. Thus, students' mathematical communication abilities in problem-based learning assisted by Geogebra were better than those in conventional education. On the other hand, the results of students' mathematical disposition abilities were 76.59 for the experimental class and 68.16 for the control class. Thus, students' mathematical disposition abilities in problem-based learning assisted by Geogebra were better than those in conventional education.

The purpose of this paper is to investigate the existence, uniqueness, and stability solution to a non-linear system of integro-differential equations by using both methods Picard approximation and Banach fixed point theorem. The existence, uniqueness, and stability theorems are established under necessary and sufficient conditions of closed and bounded domain.

This study focuses on the analysis of a Canva-based instructional textbook specifically designed for fifth-grade students preparing for the National Science Olympiad (OSN) in Mathematics. This study uses a qualitative approach with a literature review to develop a Canva-based Mathematics OSN textbook for fifth-grade elementary school students. Primary data is obtained from direct analysis of the textbook, while secondary data comes from articles, books, and papers that discuss digital book development, the use of Canva, and competitive math learning strategies. Data was collected through literature searches with relevant keywords and analyzed thematically to identify patterns and insights. The results are used to compile textbooks that suit the needs of teachers and students. The study's findings suggest that such a resource not only improves student readiness for the OSN competition but also promotes educational equity by mitigating the impact of the digital divide and other systemic barriers. This research contributes to the broader discourse on digital education, offering insights into the effective integration of technology in the development of competitive academic resources. Future research is recommended to evaluate the long-term impact of these resources on student performance and to explore further enhancements in digital educational tools.

Waste, encompassing both domestic and industrial materials, presents a significant environmental challenge. Effectively managing waste requires accurate identification and classification. Convolutional Neural Networks (CNNs), particularly the Residual Network (ResNet) architecture, have shown promise in image classification tasks. This research aims to utilize ResNet to identify types of waste, contributing to more efficient waste management practices. The ResNet101 architecture, comprising 101 layers, is employed in this study for waste classification. The dataset consists of 2527 images categorized into six classes: Cardboard, Glass, Metal, Paper, Plastic, and Trash. The ResNet model is pre-trained, leveraging existing knowledge to enhance classification accuracy. The dataset is divided into training and testing sets to evaluate the model's performance. The testing results, evaluated using a Confusion Matrix, demonstrate strong performance in waste classification. The ResNet101 model achieves 92% accuracy in detecting inorganic waste objects within the training dataset and maintains a high accuracy of 90% on the testing dataset. This indicates the effectiveness of the ResNet architecture in accurately identifying various types of waste, contributing to improved waste management efforts. he utilization of ResNet101 for waste classification yields promising results, with high accuracy rates observed across both training and testing datasets. By effectively identifying types of waste, this approach facilitates more efficient waste management practices, enabling better resource allocation and environmental conservation. Further research and application of CNN architectures in waste management could lead to enhanced sustainability efforts and improved waste-handling strategies.

The purpose of this study is to examine the place of ChatGPT writing in the current academic environment. Significant attention has been drawn to the amazing capacity of ChatGPT, a sophisticated language model created by OpenAI, to produce text answers that nearly mimic human speech. The current study examines ChatGPT's effects on a number of academic areas, including writing support, data analysis, literature reviews, and scientific cooperation. The paper looks at the benefits and drawbacks of using ChatGPT in academic research and offers some insight into prospective uses for this technology in the future. To efficiently respond to the research questions and accomplish the stated goals, the present study used a quick review of the literature technique. The study has discovered several ChatGPT uses in academic writing, including data gathering, teamwork, implications, and restrictions. The study also looked at how to prevent plagiarism in written work produced using ChatGPT. In conclusion, if ChatGPT is used wisely and responsibly, it has the potential to dramatically enhance and revolutionize academic research, enabling multidisciplinary cooperation.

In the evolving landscape of Software-Defined Networking (SDN), the strategic placement of controllers poses a critical challenge that necessitate a precise balance between network performance and security. This paper presents an integrated framework for enhancing security and performance in SDN by combining controller placement and intrusion detection systems (IDS). Unlike existing solutions which were implemented disjointedly, we propose a holistic approach that leverages the proximity of controllers to network traffic for real-time threat detection, rapid response, and mitigation of security attacks. We employ an advanced clustering model for optimal controller placement, reducing costs and latency while ensuring reliability and balanced loads. In addition, we utilize k-nearest neighbour (KNN) for efficient anomaly detection in our IDS for improved network security. Experimental results confirm the framework’s effectiveness in strengthening SDN security and resilience. The enhanced-DBSCAN-based CPP model significantly minimized the cost, and latency, and ensured continuous operation in dynamic SDN environments while the KNN-based IDS shows effectiveness in improving threat detection capabilities, achieving high detection accuracy of 100% on the LAN dataset, outperforming other machine learning models such as Random Forest and Naïve Bayes. The indication is that strategic controller deployment, in conjunction with IDS, can significantly bolster threat detection, response times, and the overall security stance of the SDN environment.

The increase in the use of digital technology led to an increase in online activities. In this regard, many organizations adopted cloud computing systems to manage this online traffic. It is plan of every cloud computing resource provider to manage their system effectively and efficiently. This paper uses bibliometric analysis technique to look at the prevalence of utilization of data analytics techniques in addressing cloud computing resource allocation challenges. In this regard, the following research databases the Association for Computing Machinery, the Institute of Electrical and Electronics Engineering, Web of Science and Scopus databases, were consulted. The research articles published before the beginning of 2017 to 2023 were considered as part of the analysis. The results showed that the prevalent data analytics techniques used to address the cloud computing resources allocation challenge are Support Vector Machine, Spatio-temporal and edge-cloud collaborative scheme. Failure to effectively and efficiently provide cloud computing management resource allocation will lead to system bottlenecks especially during peak periods. In this regard, such a failure could lead to dissatisfied clients.

Criminal intention is a critical aspect of human interaction in the 21st-century digital age where insecurity is on the high side as a major global threat. Kidnapping, killings, molestation of all sorts, gender-based violence, terrorism, and banditry are the trends of criminality in our nation, as such, there is a need to effectively explore innovative means to identify and cope with this evil menace in our society. The facial positioning of humans can tell their evil intention even if they pretended to smile with the evil in their minds. In normal instances, it may be very difficult to predict the heart of man, but with the trending information technology like image processing, the state of a human face could be used as a means to read their tendencies. This paper proposes a deep learning model based on the FER2013 dataset through the implementation of a CNN model that predicts criminal tendencies with the help of facial expressions. With this goal in mind, we explore a new level of image processing to infer criminal tendency from facial images through a convolutional neural network (CNN) deep learning algorithm in other to discriminate between criminal and non-criminal facial images. It was observed that CNN was more consistent in learning to reach its best test accuracy of 90.6%, which contained 8 convolutional layers. To increase the accuracy of this model, several procedures were explored using Random Search from the Keras tuner library, testing out various numbers of convolutional layers and Adam optimizer. It was also noticed that applying the dissection and visualization of the convolutional layers in CNN reveals that the shape of the face, eyebrows, eyeball, pupils, nostrils, and lips are taken advantage of by CNN to classify the images.

Security threats are constantly evolving in various computerized systems. As in many other systems, security threats and attacks have been directed to Students Academic Information System (SAIS) in Higher Education Institutions (HEIs). The seven steps cyber kill chain model offers preventive defense against such security threats. Little is known, however, on how well the model is applicable in the protection of SAIS. This study was therefore carried out to investigate the applicability of the cyber kill chain model on the protection of SAIS. The study was qualitative in which empirical evidence from literature was employed to gather data which were then analysed thematically through content analysis. Results showed that the cyber kill chain model is very relevant and applicable in the protection of SAIS. Each of the seven steps of the model practically applies differently in SAIS which entails for distinct protective measures as detailed in the paper. The study calls upon HEIs stakeholders to leverage the proposed preventive measures against security threats in SAIS.

This paper presents the development of the Archipelago Cultural Insight Education Application, an Android-based platform designed to enhance the learning of Nusantara’s rich cultural heritage. Utilizing Extreme Programming (XP), an agile software development methodology, the application was developed to accommodate the dynamic requirements of educational content and interface design. The XP approach facilitated rapid iterations and continuous feedback, ensuring the application remained aligned with educational goals and user needs. The application features a user-friendly interface with dedicated sections for Traditional Houses, Local Attractions, Regional Foods, Folk Songs, and other cultural elements. Each section provides comprehensive data and detailed descriptions that aim to educate and engage users. The design prioritizes intuitive navigation and ease of content management, which is critical for the educational effectiveness and sustainability of the app. Moreover, the integration of multisensory learning elements, such as auditory content in the Folk Songs section, enhances the educational experience by providing a more immersive understanding of the cultural context. The application's development process and its features illustrate the benefits of applying agile methodologies in educational technology, highlighting how they can be used to produce a robust, engaging, and informative educational platform. This study contributes to the field by demonstrating the practical application of agile principles in the design and implementation of educational technology that effectively bridges cultural education and digital innovation.

Graph theory, introduced by the Swiss mathematician Leonhard Euler in 1736, has played a pivotal role in solving real-world problems since its inception, notably exemplified by Euler's solution to the Konigsberg Bridge problem. Its applications extend to various domains, including scheduling, shortest path routing, and chemical structure representation. In chemistry, graphs are extensively used to depict molecular structures and chemical compounds, aiding in visualizing atomic connections and overall compound configurations. Topology indices, such as the Padmakar-Ivan (PI) and Randic indices, provide numerical values capturing chemical bonding relationships. Beyond chemical structures, these indices find applications in abstract algebraic graph representations. Recent research, exemplified by Husni et al.'s work on the harmonic and Gutman indices, explores these indices in coprime graphs of integer groups modulo prime power orders. Additionally, studies on non-coprime graphs of integer groups modulo reveal unique characteristics and invariants, shedding light on their structure. The non-coprime graph is a graph with two vertices said to be adjacent if the greatest common divisor (GCD) of their orders is not equal to one. This paper aims to investigate the topological indices, specifically the Padmakar-Ivan and Randic indices, in non-coprime graphs of integer groups modulo, adding depth to our understanding of their applicability and significance in abstract algebraic representations. Keywords: Graph theory, padmakar-ivan index, randic index, non-coprime graphs MSC2020: 05C09

In this paper, the mathematical formula of the reliability function of a special (3+1) cascade model is found, where this model can work with three active components if it is able to cope with the stresses to which it is subjected, while the cascade 3+1 model consists of four components, where the components (A1,A2 and A3) are the basic components in the model and component (A4) is a spare component in an active state of readiness, so when any of the three core components (A1,A2 and A3) fails to copy with the stress they are under and stops working, they are replaced by the standby component (A4) readiness to keep the model running, it was assumed that the random variables of stress and strength follow one of the statistical life distributions, which is the Frechet distribution. The unknown parameters of the Ferchet distribution were estimated by three different estimation methods (maximum likelihood, least square, and regression), and then the reliability function of the model was estimated by these different methods. A Monte Carlo simulation was performed using MATLAB software to compare the results of different estimation methods and find out which methods are the best for estimating the reliability of the model using two statistical criteria: MSE and MAPE and using different sample sizes. After completing the comparison of the simulation results, it was found that the maximum likelihood estimator is the best for estimating the reliability function of the model among the three different estimation methods

Foundational mathematical concepts like ratios and proportions are indispensable for solving both everyday problems and those encountered in professional settings. However, many students need help to grasp and apply these concepts effectively, often due to ineffective teaching methods, variations in students' levels of comprehension, and shortcomings in existing learning models. Realistic Mathematics Education (RME) and Pendidikan Matematika Realistik Indonesia (PMRI) have emerged as promising solutions to address these challenges. This study aims to systematically review the literature on ratio and proportion learning design using RME and PMRI approaches. It focuses on three specific research inquiries: profiling the literature, analyzing employed learning designs, and identifying gaps and recommendations for further research in learning design development. Conducted through a Systematic Literature Review (SLR) approach, the research involved planning, implementing, and disseminating review results. One hundred forty-four documents were scrutinized, leading to the identification of 19 pertinent papers. This study provides a comprehensive analysis of the literature on learning design in the context of ratio, proportion, scale, and level, utilizing RME and PMRI approaches while highlighting research gaps and offering insights for future studies. Its findings hold significant implications for educators and researchers seeking to enhance mathematics education through RME and PMRI approaches.

Challenges in the 21st century are increasingly complex, technology is developing rapidly and competition is getting tougher. Therefore we need quality human resources that can keep up with and anticipate the times. The use of technology involves computational thinking (CT) skills which are closely related to the problem-solving process. The stages in computational thinking are part of mathematical thinking, meaning that learning mathematics can support students' CT skills. Through the development of digital pedagogical models in CT integrated mathematics learning, it can improve problem-solving skills. This research uses design based implementation research with 4 phases including; preliminary research, prototyping, results, and design principle. The participants were 28 grade 8 junior high school students who took part in two rounds of experiment in direct CT activities and digital CT activities. In this paper, we present an iterative mathematical problem-solving process in the digital pedagogy model. The computational task, environment, tool and practices were iteratively improved over two rounds to incorporate CT effectively in mathematics. The results from CT environment demonstrated that direct CT activities are more effective than digital CT activities in mathematical problem-solving. Based on empirical research, we summarize the characteristic of the digital pedagogy model from computational tasks, computational environment and tools, and computational practices in mathematical problem solving.

Banyumasan batik is one of Indonesia's cultural heritages from Banyumas Regency, which has unique and interesting patterns and motifs. This research paper describes the mathematical modelling of geometric transformations in Banyumasan batik. Geometric transformation is a mathematical technique for changing the shape or position of an object on a plane. This is qualitative research with an ethnographic approach. Data is collected through observation, interviews, and documentations. The data obtained were analysed using content analysis to identify various Banyumasan batik motifs that can be transformed into geometric forms. This study describes the basic geometric transformations, namely translations in Sekar Tirta batik motif, reflections in Bawor Kembar batik motif, dilations in Kawung Jenggot batik motif, and rotations in Manggar Bawor batik motif which applied to Banyumasan batik motifs. The results of this study include geometric transformations of Banyumasan batik motifs which are expected to positively contribute to the development of Indonesian art and culture, especially in the field of batik, enrich insight and knowledge about the application of mathematics in everyday life, and can be used for mathematical modelling and mathematics education based on local culture.

Hyperstructures find numerous applications across various disciplines. One notable application is in chemistry, particularly in the context of chemical reactions. In 2014, Davvaz introduced the concept of bi-hyperstructures, but their application specifically in chemical reactions, has yet to be thoroughly explored in previous studies. Thus, the primary aim of this paper is to examine and analyze the different types of bi-hyperstructures present within chemical hyperstructures. The scope of this study focuses on two types of chemical hyperstructures: redox reactions and reactions in electrochemical cells. Within these chemical hyperstructures, we investigate the possibility of bi-hyperstructures among bi-semihypergroups, bi-hypergroups, bi-H_v-semigroups, and bi-H_v-groups. Next, some properties of bi-hyperstructures related to hyperstructures are also investigate.

In this paper, steady infiltration problems into a homogeneous soil from a single triangular and trapezoidal irrigation channel are considered. The governing equation is Richard's equation that represents the movement of water in unsaturated soil. It is a non-linear equation and can be solved by linearizing to become a modified Helmholtz equation. Dual Reciprocity Boundary Element Methods (DRBEM) are used in this study to numerically solve the modified Helmholtz equation. Therefore, by using a provided solution, the numerical Matric Flux Potential (MFP) is calculated. This method was applied to the homogeneous soil problem of stationer infiltration from triangular and trapezoidal single irrigation. Both numerical solutions were compared. The result show that the MFP value from the triangular irrigation is higher than the trapezoidal irrigation. This indicates that content water from the triangular irrigation channel is higher than the trapezoidal irrigation channel.

Salah satu peranan langsung Lembaga Keuangan Mikro (LKM) terhadap industri kecil dan mikro adalah memberikan dana pinjaman berupa kredit kepada nasabah yang membutuhkan. Dalam hal ini nasabah LKM dapat mengajukan kredit dengan memenuhi persyaratan dari LKM lalu kredit didapatkan jika LKM menyetujui kesepakatan pinjaman. Dalam proses pemberian kredit yang dilakukan oleh LKM sering dihadapkan pada suatu risiko yang dikenal sebagai risiko kredit bermasalah (problem loans). Berdasarkan risiko gagal bayar tersebut, paper ini bertujuan untuk melakukan analisis faktor-faktor yang mempengaruhi risiko gagal bayar dari calon debitur. Metode yang digunakan adalah regresi logistic dan Ant Coloni Optimization (ACO). Terdapat beberapa tahap dalam penelitian ini: (1) melakukan standarisasi data pada data faktor risiko calon debitur, (2) menetapkan asumsi model regresi logistic, (3) melakukan estimasi parameter model regresi logistik menggunakan algoritma Ant Coloni Optimization (ACO), dan (4) melakukan uji signifikansi setiap variabel. Dalam paper ini, data yang digunakan adalah data historis debitur pada LKM di Bandung, Indonesia. Hasilnya menunjukkan bahwa lima faktor yang dianalisis berpengaruh signifikan terhadap risiko gagal bayar, yaitu usia, jumlah tanggungan keluarga, nilai jaminan, besarnya kredit yang diajukan, dan jangka waktu pengembalian kredit. dengan kekuatan korelasi sebesar 93.5%. Menggunakan lima factor ini, yang digunakan untuk menentukan probabilitas gagal bayar dari calon debitur. Probabilitas risiko gagal bayar calon debitur ini, sangat berguna bagi LKM guna menentukan klasifikasi faktor kelayakan pemberian kredit berdasarkan predikat risiko calon debitur. Demikian sehingga, LKM dapat mengetahui faktor-faktor risiko gagal bayar dan mengambil keputusan pemberian kredit yang layak atau tidak layak.

Some mathematics teachers in Indonesia still find it challenging to conduct a formative assessment in a class with many students. A problem emerges in how to facilitate an environment that uses the type of engaging discussion needed for formative assessment to be successful. Meanwhile, in mathematics learning, proceptual (process and conceptual) understanding is considered prominent for success in learning mathematics. This understanding could be enhanced by using an appropriate strategy of discussion, which could be managed along with conducting a formative assessment. Overcoming this challenge, promoting a connected classroom might be beneficial to facilitating formative evaluation in a large class. Therefore, this paper aims to develop a method for formatively assessing students’ mathematics understanding that mathematics teachers could use with a focus on proceptual understanding. Reviewing some literature on the topics of mathematics understanding, formative assessment, and connected classroom has established a framework to help teachers develop guidance for conducting formative assessment in proceptual understanding using a connected classroom. The framework based might be used to help mathematics teachers prepare formative assessments in a class with many students. An example of how to develop a guide to assess students' proceptual understanding of algebra is also provided in this paper. Keywords: proceptual understanding, formative assessment, connected classroom

Students' conceptual understanding in Indonesia is still low. In the school that the author observed, students in grade 8 have an average total conceptual understanding of 48% with sufficient criteria. The solution to overcome this problem is by implementing RME. The purpose of writing this paper is to describe the application of RME in helping students understand concepts using descpritive qualitative research methods. The results of this study indicate that RME is applied in online learning in the following steps: first, providing and explaining contextual problems to be solved by students; second, students solve contextual problems for the development of mathematical models in their own way; third, discuss and compare any problem solving given by students; fourth, students conclude and summarize the concept of the topic being studied. The implementation of RME in online learning is carried out using a flipped classroom approach. In this reverse class approach, horizontal mathematics is done asynchronously while vertical mathematics is synchronous. Students' understanding of concepts after the implementation of RME has increased. In RME knowledge is acquired through horizontal and vertical mathematics, while interactivity in this approach helps students understand concepts. BAHASA INDONESIA ABSTRACT: Pemahaman konsep siswa di Indonesia tergolong masih rendah. Pada suatu sekolah yang diobservasi peneliti, siswa kelas VIII memiliki total rata-rata pemahaman konsep 48% berkriteria cukup. Untuk mengembangkan pemahaman konsep siswa, matematika tidak seharusnya diberikan secara siap saji kepada siswa. Oleh sebab itu solusi untuk mengatasi masalah ini adalah dengan penerapan Realistic Mathematics Education (RME). Tujuan penulisan paper ini yaitu mendeskripsikan penerapan RME dalam membantu pemahaman konsep siswa dengan metode penelitian kualitatif deskriptif. Hasil penelitian ini menunjukkan bahwa RME diterapkan dalam pembelajaran online dalam beberapa langkah berikut yaitu pertama, memberikan dan menjelaskan masalah kontekstual untuk diselesaikan oleh siswa; kedua, siswa menyelesaikan masalah kontekstual untuk pengembangan model matematika dengan cara masing-masing; ketiga, mendiskusikan dan membandingkan setiap penyelesaian masalah yang diberikan oleh siswa; keempat, siswa menyimpulkan dan merangkum konsep pembelajaran tersebut. Penerapan RME dalam pembelajaran online ini dilakukan dengan pendekatan kelas terbalik (flipped classroom). Pada pendekatan kelas terbalik ini, matematika horizontal dilakukan secara asinkronus sementara matematika vertikal secara sinkronus. Pemahaman konsep siswa setelah penerapan RME mengalami peningkatan. Dalam RME pengetahuan diperoleh melalui matematika horizontal dan vertikal, sementara interaktivitas dalam pendekatan ini membantu pemahaman konsep siswa.

The low level of mathematical disposition ability of the students in one of the Junior High Schools in Palembang became a special matter during the field experience. It was found that the average percentage of students' mathematical disposition ability was 53.01% in the moderate category. In addition, there were students who had a view of mathematics as a difficult and unpleasant science. In fact, mathematical disposition has a great influence on students' mathematics learning and achievement. Moreover, it will help students to see the beauty of God's creation through mathematics if they have a good mathematical disposition. This makes the Indonesian Realistic Mathematics Approach (PMRI) an alternative solution to the problem of mathematical disposition. The purpose of writing this thesis was to find out whether the application of PMRI could develop the disposition ability of algebra material and to describe the application of PMRI in developing the mathematical disposition ability of students in algebra material using a descriptive qualitative method. The results and conclusions of this paper show that the students' mathematical disposition ability after the application of PMRI increased with an average percentage result to 61.48% in the good category. The increase was caused by the mathematical disposition indicators that were refined in all PMRI syntax. Thus, these indicators can be refined in any syntax. Reflecting on the application of PMRI, it is suggested that the application can be done more than twice and use physical learning media. BAHASA INDONESIA ABSTRACT: Rendahnya kemampuan disposisi matematis siswa di salah satu SMP di Palembang menjadi perhatian khusus saat dilakukannya praktik pengalaman lapangan. Didapatkan rata-rata persentase kemampuan disposisi matematis siswa berada pada 53,01% dengan kategori cukup. Ditambah lagi terdapat siswa yang memiliki pandangan terhadap matematika sebagai suatu ilmu yang sulit dan tidak menyenangkan. Padahal, disposisi matematis sangat berpengaruh terhadap pembelajaran matematika dan prestasi siswa. Apalagi, dengan baiknya disposisi matematis siswa, maka akan mendukung mereka memandang indahnya ciptaan Tuhan melalui matematika. Hal ini menjadikan Pendekatan Matematika Realistik Indonesia (PMRI) menjadi alternatif solusi terhadap permasalahan disposisi matematis. Tujuan dari penelitian adalah untuk mengetahui apakah penerapan PMRI dapat mengembangkan kemampuan disposisi materi aljabar dan mendeskripsikan penerapan PMRI dalam mengembangkan kemampuan disposisi matematis siswa pada materi aljabar dengan menggunakan metode kualitatif deskriptif. Hasil dan simpulan penulisan ini menunjukkan bahwa kemampuan disposisi matematis siswa setelah PMRI diterapkan mengalami peningkatan dengan rata-rata hasil persentase menjadi 61,48% dengan kategori baik. Hasil persentase yang meningkat ini disebabkan oleh indikator disposisi matematis yang terdapat pada keseluruhan sintaks PMRI. Dengan demikian, indikator-indikator tersebut dapat terasah di setiap sintaks. Berkaca dari penerapan PMRI ini, disarankan agar penerapannya dapat dilakukan lebih dari dua kali dan menggunakan media pembelajaran fisik.

Indonesia's position on the seismically active Pacific "Ring of Fire" necessitates advanced methods for earthquake detection and preparedness. This paper introduces an application of the Isolation Forest algorithm—an unsupervised machine learning technique—for detecting seismic anomalies in Indonesia's complex geotectonic landscape. Unlike traditional methods that rely on predefined thresholds or patterns, the Isolation Forest algorithm isolates anomalies based on their rarity and distinctness without the need for labeled data. We applied this algorithm to a comprehensive dataset from the Indonesian Meteorology, Climatology, and Geophysical (BMKG) Agency, featuring a decade's worth of seismic events, to identify outliers that may signify potential seismic hazards. Our findings reveal that the Isolation Forest algorithm effectively identifies seismic anomalies, with 874 out of tens of thousands of events flagged as statistically significant outliers. A comparative analysis with traditional seismic anomaly detection models highlights the robustness of the Isolation Forest algorithm in handling the high-dimensional and noisy nature of seismic data, emphasizing its superiority in detecting subtle anomalies

In this paper, we aim to derive the explicit forms of forward and backward difference formulas based on the Taylor series. These formulas are employed to approximate the N-th derivative of a single-variable function with N-th order accuracy. The explicit formula is determined by utilizing the properties of the determinant of the Vandermonde matrix. Subsequently, we apply the obtained formulas to an example case involving a first-order ordinary differential equation

Artificial Intelligence (AI) and Machine Learning (ML) technologies have revolutionized numerous industries and sectors, offering transformative potential for Results-Based Management (RBM). RBM is a management paradigm wherein organizations and government entities plan and assess the effectiveness of their projects, policies, or programs in achieving outcomes. Integrating AI and ML into RBM can significantly enhance outcomes, fostering data-driven and informed decision-making. AI and ML integration into RBM practices facilitates improved decision-making, resource optimization, accountability, and transparency. These technologies enhance RBM by enabling predictive analytics, real-time monitoring, task automation, customization, and scalability. The dynamic synergy of AI and ML extends beyond RBM into sectors like agriculture, public health, academia, and public administration. Despite their immense potential, AI and ML tools face challenges such as perpetuating inaccuracies and biases due to inherent biases or low data quality. Nevertheless, their application in RBM empowers organizations to plan better, monitor, evaluate, and refine projects and programs, optimizing resource allocation and performance. Ongoing research, ethical considerations, data quality, and accountability are essential priorities for harnessing the full benefits of AI and ML in RBM. Therefore, this research paper investigates the potential of AI and ML tools and technologies in improving results-based management. It comprehensively reviews existing literature, practical applications, and case studies to elucidate how AI and ML can enhance results-based management practices and contribute to better decision-making.

In today's rapidly evolving digital landscape, the pervasive growth of social media platforms has resulted in an era of unprecedented data generation. These platforms are responsible for generating vast volumes of data on a daily basis, forming intricate webs of patterns and connections that harbor invaluable insights crucial for informed decision-making. Recognizing the significance of exploring social media data, researchers have increasingly turned their attention towards leveraging this data to address a wide array of social research issues. Unlike conventional data collection methods such as questionnaires, interviews, or focus groups, social media data presents unique challenges and opportunities, demanding specialized techniques for its extraction and analysis. However, the absence of a standardized and systematic approach to collect and preprocess social media data remains a gap in the field. This gap not only compromises the quality and credibility of subsequent data analysis but also hinders the realization of the full potential inherent in social media data. This paper aims to bridge this gap by presenting a comprehensive framework designed for the systematic extraction and processing of social media data. The proposed framework offers a clear, step-by-step methodology for the extraction and processing of social media data for analysis. In an era where social media data serves as a pivotal resource for understanding human behavior, sentiment, and societal dynamics, this framework offers a foundational toolset for researchers and practitioners seeking to harness the wealth of insights concealed within the vast expanse of social media data.

This article presented some theorems on a novel non-linear multiple Integro-differential equations of boundary T-system. It has been studied the numerical-analytic method and Banach fixed point theorem for the existence and approximation of the solutions over considered boundary system in compact space. In this work, we demonstrate that the mention methods can be discussed and analyzed for the existence and uniqueness, of a solution for the vector system. The paper satisfies the Hölder condition.

One and two-compartment pharmacokinetic models with drug-drug interactions are proposed. Two drugs are given orally simultaneously, so that their interaction affects the drug absorption process and subsequently the elimination process. The aim of this paper is to estimate the elimination and absorption rate constants by evaluating the data set of time and drug concentration. This data set was divided into two time phases: large-time elimination phase to estimate the elimination rate constant, and small-time absorption phase to estimate the absorption rate constant. Since the models are nonlinear, the Taylor expansion is employed to so that the Wagner-Nelson and the Loo-Riegelman methods can be used for estimation. Finally, simulations were performed using the generated arbitrary data set of time and concentration, instead of an actual data set, to derive the solution of drug concentration concerning time numerically. In these simulations we compared the original parameter values with their estimates for the one and two-compartment models, and we concluded that the two-compartment model produced better estimates than the one-compartment model. Qualitatively, the two-compartment model gives smaller drug concentration curve deviations between the original and the estimated curve compared with the one-compartment model.

Let R_ε=R∪{-∞}, with R being a set of all real numbers. The algebraic structure (R_ε,⊕,⊗) is called max-plus algebra. The task of finding the eigenvalue and eigenvector is called the eigenproblem. There are several methods developed to solve the eigenproblem of A∈R_ε^(n×n), one of them is by using the characteristic max-polynomial. There is another algebraic structure that is isomorphic with max-plus algebra, namely min-plus algebra. Min-plus algebra is a set of R_(ε^' )=R∪{+∞} that uses minimum (⊕^' ) and addition (⊗) operations. The eigenproblem in min-plus algebra is to determine λ∈R_(ε^' ) and v∈R_(ε^')^n such that A⊗v=λ⊗v. In this paper, we provide a method for determining the characteristic min-polynomial and solving the eigenproblem by using the characteristic min-polynomial. We show that the characteristic min-polynomial of A∈R_(ε^')^(n×n) is the permanent of I⊗x⊕^' A, the smallest corner of χ_A (x) is the principal eigenvalue (λ(A)), and the columns of A_λ^+ with zero diagonal elements are eigenvectors corresponding to the principal eigenvalue.

Antara, the official news agency of the state, reported a record-breaking population of 303 Bali starlings in the West Bali National Park (WBNP) in June 2020, attributing this achievement to the park's captive reproduction initiative. This paper presents a study on the dynamic equilibrium of Bali Starlings and proposes a mathematical model for analyzing this dynamic. The research also examines parameters ensuring the stability of the captive breeding model for Bali starlings in WBNP in a sustainable manner. The Bali starlings are categorized into two groups: those in the wild and those in captive breeding, with hatched eggs in captivity included in the latter. The dynamic model is analyzed for system stability around the endemic critical point using the Routh-Hurwitz stability criteria. As an illustrative example, a simulation is conducted to assess the model's suitability under real field conditions. The model analysis reveals that the existence of an endemic critical point can be maintained if the percentage of stolen Bali starlings or eggs reintroduced to the wild is less than the difference between the percentage of Bali starlings laying eggs and the population growth rate in WBNP. Furthermore, the stability of the endemic critical point is confirmed as long as the percentage of Bali starlings laying eggs exceeds the population growth rate. This dynamic model offers a valuable tool for evaluating the sustainability of Bali starling breeding programs and optimizing the benefits associated with their conservation efforts.

To help insurers determine insurance rates incorporating maternity factors, it is crucial to understand the maternity recovery rate, which was a metric used by insurance companies to understand how much of the expenses associated with maternity care and related medical services are covered by their policies. This paper employed Extreme Gradient Boosting (XGBoost), a powerful method for handling complex data relationships and preventing overfitting, on North American Group Long-Term Disability dataset obtained from the Society of Actuaries, which listed maternity as one of its categories, to predict the maternity recovery rate. In comparison, other machine learning methods such as Gradient Boosting Machine (GBM) and Bayesian Additive Regression Tree (BART) were used, with Root Mean Squared Error (RMSE) values calculated the difference between predicted and observed maternity recovery rates. Four datasets, 3 imbalanced and 1 fairly-balanced, were created out of the original dataset to test each method’s predictive prowess. The study revealed that XGBoost performed exceptionally well on the imbalanced datasets, while BART showed slight superiority in fairly-balanced data. Furthermore, the model identified the duration, exposures, and age of participants in both predicting maternity recovery rates and the underwriting process.