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In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

Thіs studу aіmеd to examіnе the eligibilitу оf tеасhеr honorаrium bу іmрlemеntіng a classifiсatіon method usіng thе Decіsіon Тree algorithm. Тhе primary іssuе addressеd in thіs rеsеаrсh is the absеnce of а fair and dаtа-driven salarу sуstеm аt SМA YPKPР. А сlаssificatіon aрproach was emрloуеd to сatеgorіze teасhers intо "Elіgіble" and "Not Elіgiblе" grоuрs basеd оn attributеs such аs tеachіng hоurs, hourly wаge, eduсatiоn lеvel, jоb роsіtіon, сеrtіfіcаtіоn allowаnсe, аnd schoоl status.Thе сlаssifiсatіon model was dеveloрed usіng RаріdМiner sоftwаrе. Тhе datasеt was dіvidеd into trainіng and tеsting sets usіng a sрlіt datа technique. Тhe modеl wаs еvaluatеd usіng metrісs such as acсuraсy, рrecisіоn, rеcall, and сonfusiоn matrіх. The rеsults indіcаtеd that thе Dесіsiоn Тree model аchіеved аn асcurасy оf 93.75% in сlаssіfуing tеаchеr honorarium еligіbіlity. Тeасhing hours and hourlу wаge werе idеntifіеd as thе twо most іnfluеntial variables іn the сlаssіfісаtіоn рroсеss.Аs a form of vаlіdatіоn, addіtionаl statistіcal аnalysis was соnducted usіng SРSS. The Рeаrsоn cоrrelаtіon tеst showеd а sіgnіficаnt relаtionshір bеtween teaching hours and hourlу wаge wіth thе tоtаl honоrаrіum rеcеivеd. Мultіplе .Lineаr rеgressiоn аnalysis resulted іn аn R Squаre valuе of 0.860, indicаting that 86% оf thе varіation in hоnorarium сan be eхрlаіnеd bу thе twо vаrіаblеs.Тhis study іs expесtеd tо serve аs а foundаtіоn for mоre objеctive аnd dаtа-drіven dеcisіоn-mаking іn thе tеaсhеr comреnsation sуstem. Тhe findіngs dеmоnstrаte thаt а combinаtiоn of datа minіng аnd stаtіstісаl аnаlуsis aрprоаches сan bе usеd to devеlop a trаnsparent, fair, аnd efficient sаlаrу system.

The increasing number of Indonesian Migrant Workers (TKW) working abroad, particularly through programs organized by BP2MI, has become a significant concern in managing the labor export process. One of the challenges faced is the uncertainty of the number of TKW to be sent each year, which is influenced by various external and internal factors. Therefore, this study aims to apply artificial neural networks (ANN) with a backpropagation algorithm approach to predict the number of TKW that will be processed by BP2MI. This method was chosen due to its ability to recognize patterns and nonlinear relationships between variables that affect the decision-making process for TKW export. In this study, the data used includes factors such as the number of job seekers, government policies, and the condition of the international labor market. The artificial neural network with the backpropagation algorithm is used to train the model based on existing historical data, with the goal of generating accurate predictions regarding the number of TKW to be processed in the coming years. The results of the tests show that the developed model can provide fairly accurate predictions and can serve as a tool for BP2MI in planning and managing the export of TKW more effectively. With the application of this technology, it is expected that the decision-making process related to TKW export can become more efficient and well-predicted.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

The increasing intensity of traffic object movement in urban areas has not been accompanied by adequate road infrastructure, resulting in traffic congestion, air pollution, and a higher risk of traffic accidents. One of the primary causes of accidents is traffic violations, particularly wrong-way driving behavior. This study develops a video-based automated traffic violation detection system using the YOLOv5 algorithm. A computer vision approach is employed to detect, classify traffic objects, and count wrong-way violations in real time. Due to limited access to real-world traffic violation footage, simulated traffic scenarios are used as testing data. The system is evaluated on four traffic object classes: motorcycles, cars, buses, and trucks. Experimental results demonstrate strong performance, achieving a precision of 90%, a recall of 92%, and an F1-score of 91%, while the traffic object counting accuracy reaches 89%. These findings indicate that the proposed system has significant potential to support traffic analysis and assist authorities in making more effective decisions to reduce congestion and traffic accidents.

The retail industry generates an extremely large and continuously growing volume of transactional data along with the advancement of digital technology, thereby requiring sophisticated and systematic data analysis approaches to support effective and evidence-based business decision-making. This study aims to analyze retail sales data by utilizing the Retail Sales Dataset obtained from the Kaggle platform, which consists of 100,000 transaction records and broadly represents the characteristics of retail transactions. The main focus of this study is to classify product categories and predict customer segments, including the identification of high-spending customers (high spenders), based on demographic attributes such as age and gender, as well as various transaction-related features. The research methodology includes data preprocessing, label encoding, and feature engineering to generate additional variables, including Age_Group, Is_Holiday, and Spender_Group, which are expected to enhance the predictive capability of the models. Several machine learning algorithms, namely Decision Tree, Random Forest, and XGBoost, were implemented and evaluated to compare their respective performance. The experimental results indicate that multiclass product category classification achieves relatively low accuracy, ranging from 27% to 34%. These findings suggest the high complexity of retail data and highlight the need for further model optimization, class balancing techniques, and feature refinement to improve predictive performance in future studies.

Because it directly impacts revenue, customer loyalty, and long-term business sustainability, customer churn is a critical issue for the e-commerce industry. High churn rates indicate that a business is unable to retain existing customers, which means it is more expensive to acquire new customers. Therefore, a precise analytical approach is needed to identify customer behavior patterns that are likely to churn. Using machine learning methods, this study analyzes and predicts customer churn. For this study, the E-Commerce Customer Churn 2025 dataset, obtained from Kaggle, was used. This dataset consists of 10,000 customer data and contains fifteen variables covering transaction behavior, customer characteristics, and churn status. Data preprocessing, descriptive analysis, exploratory data analysis (EDA), and classification model development using Logistic Regression and Random Forest algorithms were part of the research project. Model evaluation was conducted using a Confusion Matrix and Receiver Operating Characteristic (ROC) Curve to evaluate the model's accuracy and ability to distinguish between churned and non-churned customers. The results showed that the Random Forest model performed better than Logistic Regression, with an ROC-AUC of 1.00. Furthermore, feature importance analysis revealed that the days_since_last_purchase variable was the most dominant factor in predicting customer churn. These findings are expected to help e-commerce companies design more effective, data-driven customer retention strategies.  

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

The era of globalization and the rapid advancement of information technology have transformed the landscape of dakwah (Islamic propagation), shifting from conventional pulpits to virtual communication platforms (social media, podcasts, streaming). This transition, in line with the Diffusion of Innovation Theory, enables Islamic messages to reach a broad audience without geographical boundaries. Digital dakwah is highly relevant and urgent to research given the dominant use of social media by Muslim Youth, a group that seeks attention but also possesses a tendency toward critical thinking. This research has a dual aim: (1) to analyze the Interpretation of Muslim Youth towards virtual dakwah studies, and (2) to analyze the Effectiveness of Implementing Dakwah Studies through social media platforms. The accessibility of fragmented and competitive content makes the interpretation process active and crucial, where youth are no longer passive recipients but choose sources based on algorithms and their own interests. This study uses the Constructivism Paradigm to understand a dynamic and complex social reality, employing a Descriptive Qualitative Approach and the Online Ethnography Method. Data is collected through Observation, Interviews, and Documentation, with Data Triangulation (source, technique, and time) to ensure the validity of the findings. Social media holds great potential as an effective and inclusive dakwah tool. However, the effectiveness of its expansion must be balanced with the quality of the content and the capacity of Muslim Youth to perform critical, contextual, and responsible interpretation.

Mathematics is a core subject that develops critical thinking skills; however, many third to fifth-grade elementary school students face difficulties with conventional teaching methods that tend to be uniform and less adaptive. This study aims to develop and implement a mobile-based educational game, "Ethno Run," which integrates the Bayesian Knowledge Tracing (BKT) algorithm to provide an adaptive learning experience. The method used is Research and Development (R&D) with the Multimedia Development Life Cycle (MDLC) framework. The system uses BKT to track students' mastery in real-time by analyzing their responses to pre-tests and exercises within the game, which then adjusts the difficulty level and focuses the post-test on areas identified as weak, such as arithmetic operations and geometry. The findings show that this adaptive approach significantly improves learning outcomes, with the average score increasing from 44.33 on the pre-test to 85.33 on the post-test among 30 students. This study concludes that the integration of Artificial Intelligence through BKT effectively personalizes learning, enhances student motivation, and provides data-driven insights for teachers regarding students' progress. The implication of this research is that adaptive game-based learning serves as a feasible interactive solution to bridge the gap in conventional basic mathematics education.

The use of Artificial Intelligence (AI) in higher education learning has increased significantly, particularly among Informatics Engineering students. Although AI provides various benefits in supporting the learning process, its utilization also raises ethical concerns, especially related to algorithmic bias and responsible use of technology. This study aims to analyze the perceptions of Informatics Engineering students regarding bias and ethics in the use of artificial intelligence in learning. The research employed a quantitative descriptive approach. Data were collected through a Likert-scale questionnaire distributed to 80 Informatics Engineering students who had experience using AI in learning activities. Descriptive statistical analysis was conducted using mean scores and percentages. The results indicate that students demonstrate a high level of ethical awareness and responsibility in using AI; however, their perception of potential bias in AI systems remains at a moderate level. These findings reveal a gap between normative ethical awareness and critical understanding of algorithmic bias. This study recommends strengthening contextual and applied AI ethics literacy within the Informatics Engineering curriculum to promote responsible and ethical use and development of artificial intelligence technologies.

Data security is a critical aspect in the digital era due to the increasing exchange of sensitive information through electronic media. One widely used approach to protect data confidentiality is cryptography, particularly asymmetric encryption algorithms. This study aims to analyze the implementation of the Rivest–Shamir–Adleman (RSA) algorithm as a data security mechanism through an encryption and decryption process. The research method used is an experimental approach by implementing the RSA algorithm in a text-based data security simulation. The stages include key generation, encryption, and decryption processes, followed by analysis of the correctness and effectiveness of the algorithm in maintaining data confidentiality. The results show that the RSA algorithm is capable of converting plaintext into unreadable ciphertext and successfully restoring it to its original form through the decryption process using the correct private key. This confirms that RSA provides a high level of security based on the difficulty of factoring large prime numbers. The implication of this study is that the RSA algorithm can be effectively applied to secure sensitive data transmission in information systems, especially in environments requiring strong authentication and confidentiality.

Password security is a critical component in protecting information systems, as passwords are often the primary target of various attacks, particularly brute force attacks. A brute force attack works by systematically attempting all possible character combinations until the correct password corresponding to a stored hash value is found. Therefore, the choice of an appropriate hash algorithm plays a significant role in determining a system’s resistance to such attacks. This study aims to analyze and compare the password cracking time of MD5, SHA-256, and SHA-512 hash algorithms under brute force attack scenarios. The research methodology involves generating hash values from a set of test passwords using each hash algorithm, followed by conducting brute force attacks to recover the original passwords based on the generated hash values. The collected data are analyzed by measuring the time required to crack passwords for each algorithm. The results indicate that MD5 has the fastest cracking time compared to SHA-256 and SHA-512, indicating a lower level of resistance to brute force attacks. SHA-256 demonstrates better security than MD5 but remains less resistant when compared to SHA-512. The SHA-512 algorithm requires the longest cracking time, reflecting the highest level of resistance to brute force attacks among the tested algorithms. In conclusion, hash algorithms with larger bit lengths provide stronger protection against brute force attacks and are more suitable for secure password storage in information systems.

Information security is an essential aspect of digital communication, particularly in the exchange of text-based messages through open networks. Messages transmitted without protection are vulnerable to interception and unauthorized modification. One classical cryptographic technique that remains relevant as a foundational learning tool is the Caesar Cipher algorithm. This study aims to implement the Caesar Cipher algorithm for message encryption and decryption and to analyze its effectiveness and security level. The research method employed is a descriptive approach through literature review and a case study by applying character-shift techniques to text messages. The results indicate that the Caesar Cipher algorithm successfully transforms plaintext into ciphertext and restores it back to its original form through the decryption process. Although the algorithm is simple and easy to implement, it has significant limitations in terms of security due to its small key space and vulnerability to brute-force attacks. Therefore, Caesar Cipher is not suitable for protecting sensitive data but remains valuable as an introductory model for understanding basic cryptographic concepts.

Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

The rapid advancement of artificial intelligence (AI) in the digital age offers substantial benefits by enhancing efficiency and productivity. Nevertheless, these developments also pose significant challenges to the protection of human rights. Issues such as privacy violations, algorithmic bias, discrimination, and opaque automated decision-making highlight the need for a strong integration of ethical values and legal frameworks in the use of AI. This study applies a normative legal method supported by literature-based research to examine the existing regulatory frameworks and the ethical principles underpinning them. The findings indicate that ethical principles such as transparency, accountability, fairness, and human-centeredness serve as essential moral guidelines to prevent AI misuse. Meanwhile, legal rules ensure certainty, establish accountability mechanisms, and provide sanctions for violations. The synergy between ethics and law forms a crucial foundation to ensure that technological innovation aligns with the protection of human rights, upholds human dignity, and supports the creation of a safe and just digital environment

This study aims to analyze the relevance of Jürgen Habermas's thought in understanding the phenomenon of modern spirituality and the social dynamics of Generation Z in the digital era. Using a qualitative research method with a philosophical-analytical approach, this study examines Habermas's central concepts such as communicative action, the public sphere, the lifeworld, and discourse ethics. The results indicate that modern spirituality, which is personal and fluid, serves as a form of cultural resistance against the colonization of the lifeworld by the instrumental rationality of economic and bureaucratic systems. Meanwhile, Generation Z faces significant challenges within a digital public sphere that is often distorted by algorithms, polarization, and misinformation, which hinders the establishment of rational and deliberative communication. The study concludes that Habermas's theory provides a crucial normative framework for revitalizing healthy dialogue, constructing emancipatory digital ethics, and assisting Generation Z in restoring the depth of life's meaning amidst the pressures of the modern system.

This study aims to design, implement, and compare the performance of the Backpropagation algorithm from Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in predicting the optimal daily rice requirement at Grillme Restaurant in Pontianak. The main problem faced by the restaurant is the uncertainty in determining the required daily rice stock, which periodically results in either understocking (shortage) or overstocking (wastage), leading to operational losses. To address this, the study utilizes historical daily rice sales data from January 2023 to April 2025 as the database for training and testing both predictive models. The SARIMA approach is employed to capture time series components (trend and seasonality), while Backpropagation is utilized to model non-linear patterns. Comparative test results indicate that the SARIMA model achieved superior accuracy compared to the Backpropagation model. This is confirmed by the Mean Absolute Percentage Error (MAPE) value of the SARIMA algorithm being 17.35%, which is lower than the MAPE value of Backpropagation at 19.62%. The MAPE values obtained by both models demonstrate good predictive capability, but it is concluded that SARIMA is more recommended for a more efficient and planned management of rice stock at Grillme Restaurant in Pontianak.

The development of social media has shifted the way humans interpret spirituality and construct self-identity, giving rise to the phenomenon of digital religiosity that is all-visual, instant, and performative. This study aims to analyze how representations of spirituality in social media culture contribute to the crisis of self in modern humans, by interpreting this phenomenon through the metaphysical perspective of Seyyed Hossein Nasr. Using a qualitative approach with a descriptive-analytical design that enriches digital ethnography, this study collects data through documentation of spirituality-themed content on TikTok, Instagram, and YouTube, as well as a literature review of Nasr's works and literature related to digital spirituality. Thematic analysis shows that spirituality in social media is formed through symbolic aestheticization, the commodification of religious values, and identity performances oriented towards algorithms and public validation. These findings demonstrate the symptoms of the desacralization of modernity as criticized by Nasr, namely the erosion of spiritual depth due to the dominance of images and the narrowing of transcendent meaning. This study emphasizes that social media is not just a medium, but a space for the formation of consciousness that can facilitate and endeavor the spiritual search of modern humans. Theoretically, this research contributes to the study of digital spirituality and the critique of modernity; In practice, he encourages more critical digital literacy so that people can manage spiritual experiences more authentically.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.