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Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.

The DC motor serves as the main drive of the vessel and is equipped with a rotary encoder that functions to regulate the movement of the sensor in measuring sediment levels. This rotary encoder is also used to monitor and represent the rotational speed of the DC motor. System testing was carried out by implementing a Fuzzy Logic Controller (FLC) algorithm to control the DC motor speed in moving the vessel, ensuring stable motion. This fuzzy logic–based approach is expected to improve accuracy and efficiency in sediment volume calculations, while also reducing potential errors that commonly occur in manual methods. Simulating motor speed control using the fuzzy logic algorithm in MATLAB, the best test results were achieved over several trials, with a rise time of 376.310 ms and an overshoot of 83.33%. Motor speed measurements using both a tachometer and Arduino produced consistent results, with an average relative error of 0.18%.

This study presents the integration of a Large Language Model (LLM) Ollama with the OpenStreetMap (OSM) API within a Business Intelligence (BI) framework to develop an intelligent, location-based recommendation system. The system is designed to assist users in finding dining, leisure, and resting places through natural language interaction and contextual understanding. The LLM interprets user input semantically, transforms it into structured spatial queries, and retrieves relevant geospatial data from OSM. The data are then analyzed, categorized, and visualized using BI methods to enhance interpretability and decision-making. The system was implemented using Next.js, Leaflet.js, ensuring interactivity and scalability for web-based deployment. Technical evaluation focused on system accuracy, response time, and output consistency. Results demonstrate an average response time of 1.74 seconds, 80% accuracy, and 80% consistency, proving the model’s efficiency in producing relevant, context-aware recommendations. This integration highlights the potential of combining open geospatial data, local LLMs, and BI analytics to create intelligent, data-driven decision support systems applicable to tourism, urban planning, and spatial information management.

In the modern computing era, servers face significant challenges in data storage due to hardware failures, cyber attacks, or human errors. The problem highlighted focuses on the impact of file systems on three critical aspects: data integrity (accuracy and consistency of data without corruption), data recovery (the ability to restore data after a failure), and failure resilience (fault tolerance, such as redundancy and journaling to prevent downtime). The main issue is that traditional file systems like FAT32 or NTFS are often susceptible to fragmentation, metadata loss, or long recovery times, which can lead to data loss of up to 20-30% on enterprise servers, especially in high-traffic environments like cloud computing.A simple problem-solving process is conducted through a straightforward comparative analysis approach: (1) A literature review of popular file systems (ext4, ZFS, Btrfs); (2) Failure simulations using tools like fsck and stress testing on virtual servers (e.g., via KVM or Docker); and (3) Measuring performance metrics with benchmarking tools like Bonnie++ for I/O throughput, recovery time, and error rates. This process is designed to be simple, requiring only a virtual lab setup without expensive hardware, and is analyzed quantitatively with descriptive statistics.The solution to the problem indicates that advanced file systems like ZFS or Btrfs provide significant improvements: data integrity is up to 95% more secure through automatic checksums, data recovery is achieved in minutes through snapshots and RAID integration, and failure resilience is higher with copy-on-write features. The main recommendation is to migrate to journaling-based file systems for servers, combined with automated backups, which can reduce the risk of downtime by up to 50%. This research provides practical guidance for system administrators to enhance server reliability without excessive additional costs.

This study aims to develop a web-based Virtual Assistant to improve the efficiency of academic information services at SMA Negeri 1 Parittiga. The research was motivated by the delays and inaccuracies in information delivery caused by the manual system still used in the school. The system development was carried out using the Research and Development approach with the Waterfall model, which includes the stages of needs analysis, design, implementation, and evaluation. The main technologies used are Natural Language Processing (NLP) and the Long Short-Term Memory (LSTM) machine learning algorithm, which allow the assistant to understand and respond to user questions in natural language in a contextual way. The system architecture uses Flask as the backend, Vue.js as the frontend, and Laravel for administrative data management. The testing results show that the system has an accuracy level of 88.4% in providing correct answers and a user satisfaction level of 92%, surpassing the target success rate of 80%. These findings prove that integrating NLP and LSTM can enhance the system's ability to understand conversational context and speed up the distribution of academic information. The study concludes that a web-based Virtual Assistant is an effective solution for the digitalization of school information services and has the potential to support the implementation of artificial intelligence technology in secondary education in Indonesia.

The rapid growth of social media as a primary channel for information dissemination has triggered a significant surge in the distribution of hoaxes, potentially damaging social order, instigating mass disinformation, and threatening national security. This research aims to design an intelligent algorithm for hoax detection by integrating a critical thinking approach into Natural Language Processing (NLP)-based text processing. The algorithmic model is built using a combination of linguistic features, argument logic, and cognitive indicators such as the detection of unsubstantiated claims, identification of source bias, and evidence testing. To ensure accountability and transparency of the system, an Explainable AI (XAI) approach is applied so that classification results can be understood by non-technical users. The research results show that integrating critical thinking significantly improves detection accuracy to 93.1%, with an increase in precision and recall for detecting hoaxes based on emotional narratives. Beyond technical aspects, this model aligns with the mandate of Law of the Republic of Indonesia Number 11 of 2008 concerning Information and Electronic Transactions (ITE Law), particularly Article 28 paragraph (1), which prohibits the dissemination of false and misleading news that harms the public. Therefore, this system is not only scientifically relevant but also supports law enforcement and strengthens digital literacy in the post-truth era. These findings are expected to be a strategic contribution to the development of an ethical, critical, and responsible digital ecosystem.

Anxiety disorders are common mental health problems in society, often unrecognized by the sufferer. Identifying the type of anxiety disorder and its influencing factors is crucial for proper treatment. This research aims to apply the K-Nearest Neighbor (K-NN) method in identifying types of anxiety disorders based on influencing factors, focusing on patient data from Sylvani Hospital, Binjai. The K-NN method was chosen because of its ability to classify based on data proximity. This study used medical record data of patients with anxiety disorders, which were processed using MATLAB and Microsoft Excel software. The results show that the K-NN method is effective in identifying types of anxiety disorders, with a high level of accuracy, especially in the identification of Panic Disorder (K05) and Social Anxiety Disorder (K03). The use of MATLAB simplified the identification process by automating results, while data processing in Excel improved classification accuracy. This study concludes that the K-NN method can be an effective alternative in identifying anxiety disorder types based on the factors that influence them. It is recommended for future research to involve more variables and mental health experts for a more comprehensive validation of the results.

This study aims to describe the management of the Tahfidz class program in improving students’ memorization quality at MTs Sabilul Muttaqin Pungging and to identify the supporting and inhibiting factors. The research focuses on three main aspects: (1) the implementation of the Tahfidz program, (2) the improvement of students’ memorization quality, and (3) the factors influencing the program. This study employed a qualitative approach with a descriptive method. Data were collected through direct observation of the program implementation, in-depth interviews with teachers and students, and analysis of related documents. The results show that the management of the Tahfidz class program at MTs Sabilul Muttaqin Pungging follows good program management principles through planning, implementation, and evaluation stages. The program has positively impacted students’ ability and interest in memorizing the Qur’an. Students demonstrated significant improvement in memorization accuracy and fluency, with fewer errors recorded by teachers. Supporting factors include qualified human resources, a conducive peer environment, and special Tahfidz classrooms, while inhibiting factors involve limited time, variation among teachers receiving memorization submissions, and the absence of a written curriculum.

Conventional attendance systems often face various problems such as inefficiency, inaccuracies in attendance logging, and limitations in recapitulation processes. Manual systems are prone to human error and time-consuming, while fingerprint-based systems may fail when the sensor is affected by dirty, wet, or damaged fingers. This study aims to develop an attendance system based on Artificial Intelligence (AI) by utilizing the face_recognition function in Python and implementing a microservice architecture to improve efficiency and accuracy in attendance recording. The system is developed using the Agile Feature-Driven Development (FDD) method, which focuses on building system features based on prioritized business values. This method is applied within the Software Development Life Cycle (SDLC) to ensure a structured, iterative, and user-oriented development process. Facial recognition is performed by comparing the encoding of the captured face image with the data stored in the database. The results show that the system is capable of recording attendance automatically, accurately, and in real-time. Furthermore, the recapitulation process becomes more efficient as the system manages and presents the data systematically.

Sentiment analysis of guest reviews is a crucial aspect in improving the quality of hotel services. This study aims to analyze the sentiment of guest reviews regarding the services of Grand Diamond Hotel Yogyakarta using a machine learning approach with the Support Vector Machine (SVM) algorithm. SVM was chosen because it can handle high-dimensional data such as text and is capable of forming an optimal separating hyperplane between sentiment classes. The research data was obtained through web scraping from Traveloka, yielding 1,119 reviews, which were processed through preprocessing, translation, and sentiment labeling using the TextBlob library. After TF-IDF weighting, the data was divided into 80% for training and 20% for testing. The linear kernel SVM model achieved 80% accuracy in classifying the reviews into positive, negative, and neutral categories. The results of this study were implemented in a web-based application equipped with data visualization and model evaluation features, allowing hotel management to efficiently monitor and analyze guest sentiment and support data-driven service quality improvement.

Bea Cukai has recently been in the public spotlight, especially regarding the supervision of goods from abroad. News and public responses regarding Bea Cukai's supervision create pros and cons, thus triggering a variety of responses from the public. This study aims to analyze the sentiment of Indonesian people towards the performance of Bea Cukai in monitoring goods from abroad by utilizing Twitter social media. In this research, the Support Vector Machine (SVM) algorithm is applied to classify public comments on Twitter into positive or negative sentiments. Through the crawling process carried out from June 1, 2023, to May 12, 2024, 9,051 entries of data were collected. The analysis results showed an accuracy of 93.87%, precision 94%, recall 93%, and F1-score 94%. These results show that the SVM method is effective in analyzing public sentiment, especially related to Bea Cukai's supervision.

This study aims to examine in depth the level of effectiveness and obstacles faced in the use of RME. This study uses a qualitative descriptive approach that aims to describe in depth the effectiveness and obstacles in the use of RME at the Baleendah Health Center. Data collection techniques were carried out through triangulation, namely, observation, interviews, and documentation. The informants in this study consisted of three people, namely the Head of the Puskesmas, medical records officer, and a doctor. Overall, the results showed that the implementation of RME at the Puskesmas received full support from the management, including aspects of funding, training, technology, and regular evaluation of the problems faced. RME has become the main focus because it is considered more effective and efficient than manual systems, especially in accelerating and improving the accuracy of services. However, the implementation still encountered a number of obstacles, such as internet network disruptions in the early stages, which have now been resolved by adding devices at certain points. The limited number of IT experts is also an obstacle because it slows down the handling of technical problems. In addition, server disruptions still occur because data storage is centralized at Diskominfo along with 62 other Puskesmas in Bandung Regency, which causes delays in data access.

The rapid growth of social media has yielded vast digital traces with high potential for improving corporate forensic auditing. Their utilization, however, lags behind through technological reliability, privacy, and adherence to the law. The aim of this study is to explore effective utilization of social media digital traces in forensic auditing and develop a functional framework that lags neither behind through technological efficiency nor adherence to the law and ethics. A mixed-method design was utilized, combining quantitative machine learning analysis with qualitative document analysis and semi-structured interview insight. Quantitative data drawn from social media digital traces were processed using Random Forest algorithm with SMOTE for class balancing, while qualitative data were processed using thematic analysis. The results indicated high model performance with 91.3% accuracy and AUC-ROC of 0.94, together with three emergent themes: digital integration, ethics and privacy, and regulation and legality. The results demonstrate that digital footprints may serve as an effective early and reliable indicator for fraud detection, provided they are accompanied by clear regulatory and ethical frameworks. Its principal contribution lies in the development of an operational model that combines machine learning with legal and ethical perspectives, a new strategy which matures methodological refinement and practical application in today's forensic auditing.

The implementation of the Frequent Pattern Growth (FPG) algorithm in a web-based drug purchasing application at Sumber Sehat Pharmacy aims to improve efficiency and accuracy in analyzing customer drug purchasing patterns. The FPG algorithm is a method used to identify frequent purchase patterns or frequent itemsets in purchase transactions, which can then be used to make relevant drug recommendations for customers. This study uses a case study at Sumber Sehat Pharmacy to explore drug purchasing patterns and provide a data-driven solution that can help pharmacies improve service and adjust drug stocks according to customer needs. The results show that the application of the FPG algorithm can identify significant purchasing patterns and assist pharmacies in determining more appropriate promotional strategies and inventory management. By using a web-based application that implements this algorithm, Sumber Sehat Pharmacy can provide drug recommendations that are more in line with customer preferences, thereby increasing customer satisfaction and pharmacy operational efficiency.

The increasingly rapid use of information technology is now an important need for various sectors of people's lives. The various conveniences offered, such as speed and accuracy in data processing, make information technology difficult to separate from daily life, especially in the world of education. At SMP Negeri 1 Tanarighu, the processing of student payment data is still carried out conventionally, which risks causing errors in data processing. Therefore, it is very necessary to have an information system that can process student payment data centrally and integrated. This information system is expected to reduce human error and increase efficiency in the administrative process. The Scrum method, known for its ability to manage projects with limited time and dynamic needs, is very appropriate to apply in the design of this information system. By using the Scrum method, the process of creating information systems can be carried out faster and more flexibly, adjusting to evolving needs, and ensuring more accurate and efficient data management at SMP Negeri 1 Tanarighu.

Rapid developments in information technology have made laptops an essential device for students, especially those in their final year of study. Choosing the right laptop plays an important role in supporting academic productivity, such as writing theses, analyzing data, and developing software. This study aims to classify the preferences of mathematics students at Gorontalo State University in choosing laptops based on usage characteristics and factors that influence purchasing decisions. The method used is Kernel Discriminant Analysis (KDA) with a Gaussian kernel function and an optimal bandwidth of 0.8. The research data involved 268 respondents divided into training and testing data. The analysis results show that the KDA model has an accuracy rate of 60% on the training data and 52% on the testing data, which indicates the model's ability to recognize student preference patterns despite a decrease in accuracy on new data. Based on the kernel density estimation results, Acer is the most widely used laptop brand, while Zyrex and Apple are rarely chosen. The most influential factor in purchasing decisions is processor specifications, with a contribution of 35.739%, followed by brand, warranty, and price. These findings indicate that hardware characteristics are the main consideration in laptop selection, with most students choosing laptops with Intel Core i5 processors, a minimum of 8GB of RAM, and SSD storage. The results of this study can also be used by universities to provide recommendations for selecting laptops that suit students' academic needs.  

In the digital age, information travels quickly, which has made it easier to learn new things but also made it easier for false information to spread. In Indonesia, fact-checking efforts by online news sites like Kompas, Tempo, and Liputan6 have become very important in dealing with this problem. This study examines the linguistic and discursive strategies utilized in fact-checking articles published from 2022 to 2024 to foster critical literacy among readers. Utilizing the principles of Critical Literacy and Critical Discourse Analysis (CDA), the study examines five fact-checking texts addressing political, health, and social matters. The results show that all the articles follow the same story structure: they start with claims, then go into detail about the investigations, present evidence, and give final judgments. At the micro level, hedges, boosters, and evidential markers are used on purpose to change how certain and credible something is. At the meso level, discourse structures and intertextual references bolster institutional authority, whereas at the macro level, fact-checking serves as an educational instrument that promotes critical and contemplative reading habits. Overall, the study shows that fact-checking serves two purposes in Indonesia: it helps people check the accuracy of information and it helps people build their resistance to false information.

The Web-Based Information System for Family Planning (FP) Participant Registration at the Wee Karou Community Health Center in West Sumba Regency aims to simplify the registration process for FP participants, improve efficiency, and provide easier access for the community to FP services. Previously, the registration process was conducted manually, which often led to delays, data processing errors, and time inefficiency. Therefore, an information system is needed to digitalize the registration and data management processes of FP participants. This system is developed using web-based technology that enables the Wee Karou Health Center to conduct online FP participant registration, monitor program progress, and generate reports more quickly and accurately. The main features of the system include online FP participant registration, participant data management, service schedule monitoring, and the generation of statistical reports related to FP services. The implementation of this system is expected to reduce data entry errors, accelerate the registration process, and enhance transparency and data accuracy. In addition, it provides convenience for the community to register without having to visit the health center directly. Thus, the Family Planning services at Wee Karou Community Health Center are expected to become more optimal, effective, and efficient in supporting government programs for population control and better family planning.

This study aims to design and develop a web-based Asset and Goods Management Information System (SIPAB) implemented at Universitas STEKOM, Kendal Campus. The primary issue in asset management at this institution lies in the continued use of conventional tools such as Microsoft Excel and Word, which are prone to errors and inefficiency in reporting and monitoring. The research method used is Research and Development (R&D) with the Waterfall development model. Data collection techniques include observation, interviews, and literature study. The result of this research is a web-based system comprising modules for asset data, maintenance, disposal, and reports accessible internally. The system is expected to improve the efficiency, accuracy, and security of campus asset management.

This study is a literature review that examines the role of QRIS as a payment gateway in improving the efficiency of Accounting Information Systems (AIS) in MSMEs. Digital transformation in payment systems is key to speeding up, enhancing accuracy, and securing transaction records. QRIS provides a practical solution by integrating various non-cash payment methods into one system, enabling automated real-time recording and faster financial reporting. A review of ten studies shows QRIS helps speed up recording, ease reconciliation, reduce input errors, and serve as a strategic tool for AIS digitalization in the digital economy era.