The use of e-government has become a global trend, with government success based on public continuance intention toward useful and reliable services. Systematic literature reviews on continuance intention are still limited in the context of e-government. Therefore, this study was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The literature search included four databases: Google Scholar, ACM (Association for Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), and ScienceDirect, spanning 2021–2025. This yielded 656 initial data points, which were rigorously screened against inclusion and exclusion criteria, leaving 28 articles eligible for analysis. The results show that the global trend in research publications on continuance intention in e-government is relatively stable each year, with the largest contributions coming from China, Indonesia, and Malaysia. Most of the articles were published in reputable Q1 and Q2 journals, demonstrating the high quality and relevance of this topic in the academic world. Among models, the IS Success Model, ECM (Expectation-Confirmation Model), and UTAUT (Unified Theory of Acceptance and Use of Technology) are the most widely used. Meanwhile, a quantitative approach dominates the research method, reflecting the research focus on empirically testing the relationship between variables. Determinant factor analysis shows that Satisfaction, Trust, and System Quality are the most influential variables in the intention to continue using e-government. This finding confirms that the success of e-government implementation depends not only on technology, but also on user experience and trust in digital government services. This research contributes to enriching the literature on e-government by mapping the model used for e-government continuance intention. Practically, it helps the government improve the quality of the system, as well as increase user satisfaction, to continue using government services.

One of the most popular and widely used methods in various fields, not only in information technology, including bioinformatics. With the increasing complexity of data and problems faced, ML offers solutions for analyzing and interpreting very large and dense data. This study aims to analyze gene expression, which has a crucial role in disease diagnosis, especially cancer, requiring techniques capable of identifying patterns and relationships in complex genetic data. Several techniques that play a role in improving the performance of deep learning models, including PCA reduction techniques, TruncatedSVD, PaCMAP, and TorchDR, successfully simplified data with high dimensionality without eliminating important information structures, thereby speeding up the training process and improving prediction accuracy. DNN was able to achieve a perfect score with accuracy and F1-Score 1 and remained superior to TorchDR with accuracy using test data of 0.9990, PaCMAP 0.979, and TruncatedSVD 0.988. DNN is able to obtain a perfect score with accuracy and F1-Score 1, and remains superior to TorchDR with accuracy using test data of 0.9990, PaCMAP 0.979 and TruncatedSVD 0.988. The dimensionality reduction techniques of PaCMAP and TorchDR provide a more stable data representation, by producing a more separated data distribution and supporting high performance in DNN and MLP models, which makes it the right choice in the dimensionality reduction method of gene expression data.

The facility management process at Politeknik Caltex Riau is still conducted manually, using paper media and Excel records, which causes inefficiency, delays, and difficulties in monitoring real-time facility availability. To address these problems, a web-based information system was developed with a UI/UX approach using the Design Thinking method. The uniqueness of this study lies in the use of manual clustering as a user segmentation method during the initial stage, performed based on survey results. This technique resulted in three user groups: (1) active borrowers who need tracking and notifications, (2) infrequent borrowers who require clear information, and (3) non-borrowers who focus more on attractive and easy-to-use interface design. These clusters serve as the foundation for creating personas, defining problem statements, and designing key system features. This study was conducted in two cycles. The first cycle involved initial design and testing, while the second cycle involved iterative improvements based on previous evaluation results. Testing using the System Usability Scale (SUS) showed an increase in scores from 75 to 77. Meanwhile, the User Experience Questionnaire (UEQ) exhibited all dimensions in the positive range (>0.8), with the highest score in Stimulation (2.202) and the lowest in Novelty (1.721). These results demonstrate that the manual clustering approach is effective in identifying user needs contextually and supports the design of an efficient, relevant, and user-friendly system.

The escalating volume and often irregular structure of social assistance data pose significant challenges for efficient data retrieval in management systems. Traditional search algorithms, such as linear and binary search, frequently encounter limitations when handling these large-scale datasets. This research conducts a comparative study between two hybrid algorithms, Jump Binary Search (JBS) and Interpolation Extrapolation Search (IES), aiming to identify the most effective method for a web-based social assistance data management system. Evaluations were performed on a dataset comprising 480 names of social assistance recipients, measuring the number of iterations, execution time, and search accuracy. The results demonstrate IES's superiority over JBS in both iteration efficiency and execution speed. IES exhibited an execution time ranging from 0.002 to 0.006 ms, whereas JBS had an execution time ranging from 0.015 to 0.039 ms. Based on these findings, IES was successfully implemented into a Laravel-based application utilizing a MySQL database. This system is capable of executing searches in less than one second per request. This implementation significantly enhances the system's adaptability and provides an effective search solution for dynamic, large-scale data environments, offering rapid and efficient access to data.

Augmented Reality (AR) has emerged as a prominent solution to address customer concerns in online shopping. This study aims to conduct a literature review on the factors influencing the adoption of AR in the retail industry. The reviewed literature spans the last five years, from 2020 to 2025, and was sourced from databases such as Scopus, Semantic Scholar, Web of Science, and Google Scholar. The PRISMA method was applied to facilitate the selection of relevant studies, resulting in a final dataset of 34 publications. The review identified 94 variables and 13 moderators commonly used in 11 prior studies. Among these, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were the most frequently applied frameworks. Research on AR adoption in retail has been conducted across various countries in Asia, Europe, and the Americas. This study is expected to provide insights beneficial for both researchers and retail practitioners in integrating AR technology into their business processes.

The Fourth Industrial Revolution has led to significant advances in digital devices and social media, contributing to the emergence of artificial intelligence (AI) as a key tool for improving the educational process. Computing and information technologies have enabled the use of computers in education, particularly in computer-assisted instruction and improving classroom interaction. Artificial intelligence in education (AIEd) aims to support teaching strategies, enhance student learning, and improve educational outcomes through performance monitoring, adaptive learning, providing educational resource recommendations, and identifying educational gaps. The study focuses on exploring the role of AI applications in improving the quality of learning, assuming that these applications contribute to developing the educational process, addressing traditional challenges, and improving the performance of teachers and students. The study adopted a descriptive-analytical approach and collected data through a survey of academics and teachers in Iraq during the 2024-2025 academic year. The study recommends holding training workshops, providing necessary resources, promoting the effective use of AI applications, and developing future development plans, while proposing additional research on innovation and interactive lesson design.

E-voting systems are prone to challenges such as lack of transparency, risks of data manipulation, and dependence on centralized authorities, which can undermine trust in electoral processes. This research develops a blockchain-based e-voting system on the Polygon network, leveraging smart contracts and Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) to enhance security, transparency, and voter anonymity. The study employs an application development approach, implementing a structured methodology with initialization, registration, voting, and tallying phases. Smart contracts automate voter verification, vote casting, and result tabulation, while ZK-SNARK ensures voters can cast ballots anonymously without revealing their identities. The system’s transparency and immutability are tested using PolygonScan, demonstrating effective prevention of manipulations like double voting through cryptographic credentials (nullifier, commitment, and nullifier hash) and Merkle Tree structures. Results indicate that the system provides a secure, verifiable, and decentralized framework for elections. This implementation offers a robust foundation for future e-voting systems, promoting trust and integrity in digital voting processes.

Mental health problems are increasingly prevalent among the younger generation, particularly those active on social media, yet early detection efforts often remain limited. Previous studies have explored text-based approaches for identifying mental health issues, but many are constrained by low accuracy in differentiating multiple psychological states or lack integration into accessible tools for end-users. This study addresses these gaps by proposing a hybrid machine learning model for early detection of mental health conditions through social media text analysis. Five algorithms were evaluated, and a soft voting ensemble combining Logistic Regression and Support Vector Machine (SVM) was developed to improve classification across five mental states (Anxiety, Depression, Stress, Emotional Exhaustion, and Healthy) and three risk levels (Low, Medium, High). To ensure practical utility, the model was deployed in an Android-based application, SmartRisk, which allows users to input free text and receive automated assessments. The findings show that the proposed hybrid approach significantly improves detection performance, particularly in identifying depression and high-risk cases, while maintaining high usability in real-world application. The novelty of this study lies in combining hybrid ensemble learning with mobile deployment for practical, text-based early detection of mental health, offering both methodological advancement and societal impact.

The palm oil industry in Indonesia continues to face serious problems related to environmental degradation and small farmers limited access to information on sustainable cultivation practices. To address these challenges, this study developed EduSawit, an Augmented Reality (AR)-based educational application that supports the implementation of environmentally friendly palm oil cultivation practices. The application was designed using the Multimedia Development Life Cycle (MDLC) method, which consists of six main stages: concept formulation, design, material collection, assembly, testing, and distribution. Its main feature is interactive 3D visualization that displays important processes such as site selection, provision of superior seeds, mixed planting patterns, use of organic fertilizers, water management, biological pest control, and palm oil waste management. Validation was carried out using Black Box testing to ensure that all functions, including AR marker scanning, 3D object display, and information panels, worked as expected. The research results show that EduSawit is a technically reliable and pedagogically relevant learning medium, with the potential to improve farmers understanding of sustainable cultivation practices. The next step is a field trial with smallholder farmers to assess the application's effectiveness in increasing knowledge, confidence, and adoption of environmentally friendly practices.

In the digital transformation era, web-based warehouse management systems play an essential role in ensuring operational efficiency, yet their effectiveness depends not only on functional accuracy but also on a user-centered interface design. This study aims to redesign the interface of Company XYZ’s warehouse management system using the Task-Centered System Design (TCSD) method within the Human-Computer Interaction (HCI) framework. TCSD emphasizes mapping real user tasks, analyzing user-centered requirements, prototyping through scenarios, and conducting usability evaluations. Data were collected through direct observation, structured interviews, and literature review, followed by interface prototyping in Figma and testing with the Maze platform. Six main warehouse tasks login, item distribution, returns, receiving, cycle counting, and searching were evaluated with five participants. The results demonstrated a Task Success Rate (TSR) of 100% across all tasks, with the shortest average Time on Task (ToT) for login (7.89 seconds) and item search (8.58 seconds), and the longest for cycle counting (58.10 seconds). Click Rate (CR) and Error Rate (ER) varied depending on task complexity, with login and item search showing higher ER values, which were influenced by predefined task flows. Overall, findings confirm that TCSD yields a practical and measurable design aligned with user workflows, producing an interface that is efficient, user-friendly, and adaptable to warehouse operations. This research contributes to HCI studies by demonstrating the effectiveness of scenario-based design in logistics systems, while also highlighting the need for larger-scale usability testing in future work.