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Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

This article aims to provide a comprehensive literature review on how professional ethics can serve as an effective strategy to prevent fund misuse within organizational financial management. Professional ethics is viewed as a set of moral values, behavioral norms, and professional standards that guide financial managers to perform their duties with honesty, responsibility, and without conflicts of interest. In the context of financial management, these duties include recording, budgeting, monitoring, and reporting financial activities, all of which require accuracy and transparency. The study highlights five main principles of professional ethics: integrity, objectivity, professional competence, confidentiality, and professional behavior. These principles clarify rules, strengthen accountability, and ensure that financial processes comply with established standards. The literature review shows that applying professional ethics not only encourages individuals to act correctly but also enhances responsibility, improves performance, and strengthens financial oversight. Integrity and objectivity play a crucial role in preventing report manipulation, budget inflation, and fund misuse, as these principles demand moral courage and fair decision-making. Professional competence ensures that every financial process is carried out accurately and in accordance with regulations, while confidentiality protects sensitive information from misuse. Professional behavior emphasizes adherence to laws, organizational policies, and professional standards. The article also identifies several supporting factors that enable the effective implementation of professional ethics, such as strong internal policies, leadership commitment to integrity, an ethical workplace culture, layered supervision systems, and continuous ethics training. Conversely, common challenges include weak internal controls, limited understanding of ethics, organizational pressure, conflicts of interest, and inconsistent application of ethical standards. Therefore, this article underscores that integrating professional ethics into organizational financial policies, procedures, and management systems is a key step in preventing fund misuse and strengthening stakeholder trust in the organization’s transparency and accountability.

Problem solving in permutation and combination requires the ability to understand context, choose strategies, and perform calculation procedures accurately. Based on the analysis of students' answers, it was found that difficulties arose consistently at each stage of problem solving according to Polya, namely the problem understanding stage, the planning stage, the plan implementation stage, and the rechecking stage. In general, students' weaknesses are not only related to their understanding of permutation and combination concepts, but also to their inability to apply problem-solving steps systematically. The results of the study indicate the need for a learning approach that not only focuses on mastering formulas, but also strengthens problem literacy, the ability to identify relevant information, and the selection of solution strategies appropriate to the characteristics of the problem. In addition, the habit of reflection through reviewing the process and results of the solution needs to be developed consistently so that students are able to recognize mistakes and improve their accuracy in solving permutation and combination word problems in a more accurate, logical, and structured manner.

The rapid advancement of information technology has encouraged business actors to adopt digital transformation; this situation is also experienced by Pabrik Tahu Macanan, a small scale tofu factory in Magelang that still relies on manual systems in operation. This  study aims to analyze the implementation of management information systems in supporting digital transformation and risk management at Pabrik Tahu Macanan; a descriptive qualitative approach was applied, using interviews, observations, and documentation as date collection methods. The findings reveal that digital information systems have the potential to improve efficiency, recording accuracy, and internal control; however, their implementation remains limited due to human resource constraints and low adaptability to new technologies. The research also found that simple risk management practices such as regular machine maintenance and manual bookkeeping remain effective in maintaining business stability. The implication of this study indicates that a gradual implementation of digital based information systems, supported by training and supervision, can serve as a strategic step to enhance competitiveness, operational efficiency, and sustainability for traditional SMEs like Pabrik Tahu Macanan.

Objective– This article aims to comprehensively examine the main types of food crop pests and their attack patterns through a systematic literature review approach. The research focuses on the dynamics of pest attacks, changes in ecological patterns due to climate change, and advances in modern identification technology that enable more accurate early detection. This study also highlights the significance of new paradigms of pest identification based on artificial intelligence (AI), genomics, and landscape mapping in supporting food security at the regional and national levels. Design/methodology/approach– This study used the Systematic Literature Review (SLR) method for scientific publications from 2015–2025 from reputable sources such as Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, Taylor & Francis, Wiley, AGRIS, and Google Scholar. Of the 326 articles identified in the initial stage, 30 articles in English and Indonesian were selected through a screening process based on strict inclusion–exclusion criteria. All articles were then analyzed using thematic coding techniques to produce an in-depth, evidence-based synthesis. Findings– The study produced four key findings: (1) there are five dominant pests in global food crops, namely Thrips tabaci, Spodoptera exigua/frugiperda, Helicoverpa armigera, Nilaparvata lugens and Sitophilus oryzae; (2) attack patterns are strongly influenced by temperature, humidity, pesticide resistance, and monoculture; (3) modern identification technology AI, drone imagery, multispectral sensors, and DNA Barcoding have increased detection accuracy to 94–98%; and (4) community-based early warning systems accelerate field response and reduce the risk of crop failure. Practical implications– These findings provide a scientific basis for local governments, agricultural extension workers, and farmers to gradually adopt pest identification technology and strengthen integrated monitoring systems at a regional scale. Authenticity/value– This article offers a new conceptual model of “Pest Identification Pyramid – Attack Pattern – Early Warning System” that integrates pest biology, digital technology, and community response to improve national food security.

Arabic grammar (nahwu) instruction has long been dominated by rule-based approaches that emphasize memorization and formal analysis, often resulting in rigid learning structures and limited responsiveness to learners’ cognitive diversity. While such approaches play an important role in preserving grammatical accuracy, they frequently overlook individual learning trajectories, cognitive readiness, and adaptive instructional needs. In the era of artificial intelligence (AI), language education is increasingly shaped by adaptive learning systems that personalize content, pacing, and instructional strategies based on learners’ profiles. This study aims to reconceptualize Arabic grammar instruction by proposing a conceptual framework that integrates traditional nahwu principles with adaptive learning systems informed by AI. Using a qualitative conceptual analysis, this paper synthesizes classical Arabic grammar pedagogy, contemporary theories of adaptive learning, and recent developments in AI-supported language instruction. The proposed framework highlights key components, including learner profiling, cognitive-level alignment, hierarchical nahwu content structuring, and AI-assisted scaffolding mechanisms. The findings suggest that adaptive learning systems offer significant pedagogical potential to transform nahwu instruction from a static, rule-centered model into a flexible, learner-centered process. This reconceptualization is expected to enhance grammatical comprehension, reduce cognitive overload, and promote learner autonomy in Arabic language education, particularly in Islamic higher education contexts. The study concludes by discussing pedagogical implications and directions for future empirical research on AI-assisted Arabic grammar learning.

This study discusses the comparison of traditional and modern management accounting methods with the aim of analyzing the characteristics, advantages, and disadvantages of each method and providing practical guidance for corporate decision-making. The research method uses a literature review approach, collecting and synthesizing various recent studies related to Activity Based Costing (ABC), Target Costing, and Balanced Scorecard (BSC), and comparing them with traditional methods such as job order costing and process costing. The analysis results show that traditional methods are simpler and easier to implement, but less accurate in calculating costs and less relevant for strategic decision-making. In contrast, modern methods offer higher accuracy through detailed cost allocation, comprehensive performance monitoring, and data-based decision-making support, although they require greater implementation complexity and resources. This study concludes that the choice of method must be adjusted to the characteristics of the company and the complexity of business activities, so as to optimally improve cost efficiency and profitability.

Communication is a fundamental human need for conveying information and ideas. However, individuals who are deaf and mute face difficulties in communicating with the broader community that does not understand sign language. This study aims to design and implement a real-time static sign language translator into speech using five flex sensors, an MPU6050 sensor, a Raspberry Pi Pico, an ADS1115 ADC module, and a DFPlayer Mini module as the audio output medium. Testing results show that the device successfully recognizes finger movements and hand orientation. The system is capable of playing audio output corresponding to recognized gestures, with the shortest latency recorded at 1.1 seconds and the longest at 2.8 seconds, achieving a detection accuracy rate of 75% based on 60 tests across 12 sign words. This device supports the translation of 12 simple sign words. The implementation demonstrates potential as an assistive communication tool, although further development is needed to improve accuracy, expand vocabulary, and conduct trials directly with deaf or mute users.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

The attendance recording system at PLN ULP Tanjungbalai still relies on manual, paper-based methods, resulting in delays in data recap, reduced efficiency, and a high potential for recording errors. This condition affects the accuracy of employee attendance information, which is essential for administrative activities and managerial decision-making. Based on these issues, this practical work aims to design and develop a web-based e-attendance application as a solution to enhance efficiency, processing speed, and the accuracy of attendance recapitulation. The system was developed using PHP as the programming language and MySQL as the database management system, following several stages including requirement analysis, system design using UML, and implementation of a web-based user interface. The application provides essential features such as user login, daily attendance recording, employee data management, attendance notes (permission, sickness, etc.), and automatic attendance report generation. The system is designed for two types of users—Admin and Employees—each with specific access rights. The implementation results indicate that the e-attendance application significantly improves the efficiency of attendance administration at PLN ULP Tanjungbalai. Data collection and recapitulation become faster, more structured, and less prone to errors, while also enabling administrators to monitor employee attendance in real time. Therefore, this web-based e-attendance application serves as an effective solution to support operational activities and enhance the quality of employee attendance management.

Flooding is one of the most recurrent natural disasters in Central Java Province, particularly during the rainy season. Diverse geographical characteristics, high rainfall intensity, and rapid urban development contribute to the region’s high vulnerability to flood hazards. According to the Central Java Statistics Agency, a total of 414 flood events and 407,784 affected victims were recorded between 2019 and 2021. This study aims to develop a web-based Geographic Information System (GIS) capable of mapping the spatial distribution and impact levels of floods across Central Java. The methodology includes collecting flood event data from the Central Java Statistics Agency, processing spatial data such as administrative boundary shapefiles, performing attribute integration between spatial and non-spatial datasets, and creating thematic maps using QGIS. The visualization outputs were exported into an interactive web format using the qgis2web plugin and subsequently integrated into a website developed with HTML, CSS, and JavaScript. The results show that the GIS system successfully visualizes flood-prone areas, identifies regions with high flood intensity, and enables users to explore detailed information through interactive digital maps. Additional website features—such as historical flood data, statistical summaries, and descriptive impact indicators—enhance the system's usefulness for disaster analysis. This study demonstrates the crucial role of GIS in supporting disaster mitigation, spatial planning, and policy evaluation related to flood management. Future research is recommended to incorporate more recent datasets and additional non-spatial variables such as rainfall intensity and floodwater depth to improve the system’s analytical accuracy and comprehensiveness.

Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.  

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The Information System registration module for the Regional Taxpayer Identification Number (NPWPD) was developed using the Laravel framework and implemented by the Taxpayer Identification Agency (BPPKAD) at Kediri City. The system was designed to digitize the NPWPD registration process, which was previously done manually. This traditional approach often led to long queues, extended processing times, and, at times, errors in data entry. The new system offers several key advantages, including an online registration form that allows taxpayers to upload required documents such as photos of ID cards, business locations, and other necessary paperwork. Data validation is performed by officers to ensure accuracy, and automatic notifications are sent to taxpayers, informing them of the status of their applications. The implementation of this system has had several positive impacts, such as significantly improving the efficiency of administrative processes, reducing the manual workload for officers, and increasing transparency and accountability in public services. Moreover, it has improved customer satisfaction by providing faster, more accurate, and more responsive services. This system supports the creation of a streamlined, user-friendly, and effective method for taxpayers to register for NPWPD online, enhancing the overall quality of public sector service delivery.

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.

The determination of the beginning of the lunar month is an important issue in religious practice and the establishment of the Hijri calendar in Indonesia. The diversity of imkan rukyat criteria used across countries necessitates an evaluation of international standards, including the 2016 Turkish Criteria, which are considered more progressive with parameters of a minimum crescent altitude of 5° and an elongation of 8°. This article examines the relevance of the 2016 Turkish Criteria within the astronomical and jurisprudential context of Indonesia, as well as its implications for the process of determining the beginning of the lunar month by the government and Islamic organizations. Through literature review, comparative astronomical analysis, and examination of hisab–rukyat practices in recent years, this study finds that the 2016 Turkish Criteria exhibit strong astronomical consistency and can enhance calendar predictability. However, its application in Indonesia may lead to discrepancies with the government’s criteria, which currently require a crescent altitude of 3° and an elongation of 6.4°. These implications include potential differences in month beginnings, the need for harmonizing criteria, and the importance of dialogue between national and international astronomical authorities. This study recommends strengthening astronomical and jurisprudential assessments prior to adopting new criteria and encourages the integration of global data to improve the accuracy of the Hijri calendar in Indonesia.

This research aims to: (1) identify the variations of joyful learning in digital-based instruction using Interactive Flat Panel (IFP) tvs at SD Islam Nurul Ulum Kandangan, Kediri; and (2) determine the implications of joyful learning within digital-based instruction via IFP tvs at SD Islam Nurul Ulum Kandangan, Kediri. The data for this study were derived from visual records during lessons using IFP tvs and the students of SD Islam Nurul Ulum Kandangan. This study employed a qualitative method. Ata were gathered through observation and documentation. The analysis used a descriptive qualitative technique, presenting the subjects and research findings in a narrative form. The results from several classes utilizing IFP tvs based on joyful learning revealed various engaging learning activities. These include: Student digital literature, Digital learning matches, English language learning, Fine arts stencil printing, Honesty education based on Pancasila values. These activities integrate fun, game-based learning with IFP TV technology. The digitalization of learning through IFP tvs fosters the development of students' critical thinking, agility, accuracy, and precision in their work. Additionally, students become more enthusiastic and do not experience boredom during the learning process.

The integration of artificial intelligence (AI) in forensic investigation has significantly transformed the analysis and authentication of digital evidence. This paper explores the role of AI technologies, specifically machine learning and deep learning algorithms, in examining digital evidence from various sources, including computers, mobile devices, and network systems. We provide an in-depth analysis of current AI-based forensic tools, their efficiency in evidence authentication, and the challenges they face regarding legal admissibility. Our findings indicate that AI-powered forensic systems can detect digital evidence tampering with 94.7% accuracy, drastically reducing analysis time from weeks to hours. However, challenges remain, particularly in areas such as algorithmic transparency, bias prevention, and ensuring the integrity of the chain of custody. This research offers a framework for incorporating AI in forensic laboratories, while also addressing crucial legal and ethical concerns to ensure the admissibility of AI-analyzed evidence in court. These considerations are essential for the widespread acceptance and use of AI in forensic investigations.

The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.

The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.