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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.

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.

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.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.

Historically, water resource management in Indonesia has faced significant complexities, often dominated by commercialization and investment-oriented policies that threaten the fundamental rights of farmers. The Constitutional Court’s decision to annul the previous Water Resources Law marks a crucial turning point, demanding a comprehensive policy reformulation. This entails a shift from a market-based paradigm to one grounded in human rights and social welfare, reaffirming state sovereignty over water for the prosperity of the people. The urgency of this reformulation is compounded by the imminent threat of climate change, which introduces high uncertainty into the hydrological cycle—manifesting as extreme droughts and floods—thereby directly impacting the agricultural sector. This research aims to formulate a climate-adaptive water resource management policy that secures the priority rights of farmers. Employing a Normative Legal Research Method with Statutory and Conceptual Approaches, the findings present a new, holistic legal and institu-tional framework. This framework centers on two pillars: first, the firm assertion and absolute legal protection of irrigation water allocation for farmers, even during periods of scarcity; and second, the adoption of a Climate-Adaptive Governance Model. This governance model integrates a unified climate monitoring system, resilient water infrastructure, and the empowerment of Water User Associations (P3A) at the grassroots level to independently respond to shifting climate patterns. This reformulation is essential for achieving water management that is just, sustainable, and effective in maintaining national food security

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

Street vendors (PKL) constitute an essential component of the informal economic sector in Gresik Regency, yet their presence often generates issues related to urban order, cleanliness, and aesthetics. This study aims to: (1) analyze the effectiveness of implementing Gresik Regency Regional Regulation Number 7 of 2013 concerning the Arrangement and Empowerment of Street Vendors, and (2) identify the obstacles faced by the local government in carrying out the regulation. This research employs a socio-legal method with a normative-empirical approach, utilizing statutory analysis, field observation, interviews, and documentation. The findings indicate that the arrangement and empowerment of street vendors have been carried out through the designation of trading locations, regulation of operating hours and physical facilities, establishment of requirements for becoming street vendors, and the enforcement of rights, obligations, prohibitions, and sanctions. The Gresik Regency Government has also undertaken relocation initiatives to designated areas. However, the effectiveness of the regulation remains hindered by limited human resources, weak monitoring systems, and low compliance among some street vendors. Overall, the implementation of Regional Regulation Number 7 of 2013 has been conducted but has not yet achieved optimal outcomes in ensuring order and promoting the independence of street vendors. Strengthened coordination, enhanced guidance, and improved regulatory systems are needed to support the sustainability of the informal sector in Gresik Regency.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

The manual archiving process within the Gresik Education Office continues to encounter significant challenges, including difficulties in correspondence tracking, document misplacement, and inefficiencies in disposition management. To address these issues, this study proposes the design and implementation of a Web-Based Archiving and Correspondence Monitoring System developed using the Waterfall methodology. The system aims to facilitate the digital transformation of administrative correspondence through three primary pillars: secure user authentication, flexible digital workflows, and compliance with electronic archival standards. Core features include centralized document management, automated disposition processes, electronic signature verification, and real-time notification integration via WhatsApp. Implementation results demonstrate that the proposed system enhances efficiency, traceability, and accountability in correspondence management at the Gresik Education Office.

Household waste management is a significant challenge in creating clean, healthy, and sustainable residential areas. Dependence on manual monitoring methods often leads to delays in waste collection, waste pile-ups, and operational inefficiencies. This community service program implements a Smart Trash Monitoring Sistem (STMS) based on the Internet of Things (IoT) in Graha Panyindangan Estate, Indramayu. The system utilizes ultrasonic sensors and ESP32 to detect waste levels and sends real-time data to a dashboard accessible by residents and cleaning staff. The results of implementing STMS show improved efficiency in waste management, with waste collection being carried out on time based on accurate, real-time data. This system also reduces the need for manual inspections and encourages responsible waste management behavior among residents. In addition to technical benefits, there have been social changes, such as increased environmental awareness and the formation of new social norms within the community. The involvement of local leaders in supporting the system’s sustainability further strengthens the implementation of the smart living concept, resulting in a cleaner, healthier, and more sustainable residential environment in the long term.

Household waste management is a significant challenge in creating clean, healthy, and sustainable residential areas. Dependence on manual monitoring methods often leads to delays in waste collection, waste pile-ups, and operational inefficiencies. This community service program implements a Smart Trash Monitoring Sistem (STMS) based on the Internet of Things (IoT) in Graha Panyindangan Estate, Indramayu. The system utilizes ultrasonic sensors and ESP32 to detect waste levels and sends real-time data to a dashboard accessible by residents and cleaning staff. The results of implementing STMS show improved efficiency in waste management, with waste collection being carried out on time based on accurate, real-time data. This system also reduces the need for manual inspections and encourages responsible waste management behavior among residents. In addition to technical benefits, there have been social changes, such as increased environmental awareness and the formation of new social norms within the community. The involvement of local leaders in supporting the system’s sustainability further strengthens the implementation of the smart living concept, resulting in a cleaner, healthier, and more sustainable residential environment in the long term.

Household waste management remains a critical challenge in creating clean and sustainable residential environments. Manual monitoring methods often lead to delayed waste collection, overflowing bins, and operational inefficiency. This community engagement program implements an Internet of Things (IoT)-based Smart Trash Monitoring SIstem (STMS) at Graha Panyindangan Estate, Indramayu. The sIstem utilizes ultrasonic sensors and ESP32 microcontrollers to detect the fill level of household waste containers and transmit data in real time to a user-accessible dashboard. The results demonstrate that the STMS provides accurate waste-level information, reduces manual inspections, enhances collection efficiency, and fosters data-driven behavior among residents. Social changes also emerged, including improved environmental awareness, new social norms, and the emergence of local leaders supporting sIstem sustainability. This program contributes to the realization of smart living in modern residential communities, ensuring better waste management, cleaner environments, and more sustainable practices for future generations. It also provides valuable insights into the role of technology in addressing environmental issues at the community level.

PAMTIRTA Tempino is an institution that provides clean water services in the Tempino area. The process of recording water use and monitoring water turbidity is still done manually, making it prone to recording errors and making it difficult to monitor the water quality distributed to the community. This study aims to design a website-based water turbidity recording and monitoring system by focusing on User Interface (UI) and User Experience (UX) aspects using the Design Thinking method. The research follows five stages of Design Thinking: empathize, define, ideate, prototype, and test. Data collection involves observation and in-depth interviews with PAMTIRTA officers. The results include a design with key features such as digital water meter recording, turbidity monitoring dashboards, and complaint services. The prototype was tested using Maze and the System Usability Scale (SUS), achieving a score of 80.1 and falling into the "Good" category (grade B). These results demonstrate that the UI/UX design effectively provides an easy-to-understand, operationally suitable, and efficient solution for PAMTIRTA Tempino's water recording and turbidity monitoring needs. This design offers a ready-to-implement solution to improve the efficiency, accuracy, and quality of clean water services in the Tempino area.

The main problem with the water heating system on offshore platforms is the absence of water level monitoring and automatic overflow detection. This has the potential to cause hot water spills that endanger workplace safety and operational efficiency. This research designs and implements a water level monitoring system based on the Arduino Uno microcontroller with HC-SR04 ultrasonic sensors. The system is equipped with LED indicators, a buzzer alarm, and a 16x2 LCD to display water level status in real-time. Water levels are classified into three zones (low, medium, high), and overflow is detected if the water is within 3 cm of the sensor. Testing was conducted on a 5-liter simulation tank representing actual 500-liter tank conditions. Test results showed a reading accuracy of 96% and a quick system response to overflow conditions (<1 second). This system is economical, easy to develop, and highly applicable for offshore environments. In addition, this system can be integrated with IoT technology for remote monitoring.

This study analyzes the gap in the implementation of Information Technology IT service management at an internet service provider in Purwokerto using the ITIL V4 Service Value System SVS framework. The company faces a number of major problems, such as delays in incident handling, data fragmentation between operational applications, low automation in network monitoring, and suboptimal coordination between the technical and customer service divisions. The purpose of this study is to evaluate the level of compliance of current service practices with ITIL V4 SVS best practices and to formulate recommendations for improvements that can increase operational efficiency and service reliability. The research uses a qualitative descriptive method with a case study approach, with data collected from interviews and observations in the technical and customer service divisions, then analyzed based on the five main components of ITIL V4 SVS. The results show that the company has implemented several ITIL principles, particularly in terms of customer value focus and process simplicity, but the implementation of iterative feedback, data integration, and automation is still at a low level of maturity. This study concludes that strengthening the real-time monitoring system, increasing cross-divisional visibility, and establishing a Continual Improvement Register are important steps towards realizing more efficient, responsive, and sustainable IT service management.

A steel truss bridge is a structure constructed from a series of interconnected steel bars. The loads received by this structure are analyzed and transferred to the steel bars that make up the structure. Factors that need to be analyzed in bridge construction include the location and surrounding environmental conditions. After identifying the type of soil and bedrock beneath the surface, the materials used must meet strength or durability test standards before the bridge can be operated. The materials used must comply with the Indonesian National Standard (SNI). In addition to the factors mentioned above, the bridge must undergo a feasibility evaluation. Among the various tests used to evaluate bridge strength, one is vibration testing. The vibration test method, when compared to the load test method, shows higher cost efficiency and does not cause damage to the structure.

This study aims to evaluate the effectiveness of the implementation of the public sector accounting cycle in the planning, implementation, administration, and evaluation of catfish cultivation programs run by BUMDes Ajoeng Jaya as part of the village food security program. The focus of the research departs from the need to ensure that each stage of the management of the BUMDes program runs according to the principles of public accountability, considering that BUMDes is a strategic entity in the management of village resources to improve the welfare of the community. This study uses a qualitative descriptive approach through field observation, in-depth interviews with the management of BUMDes Ajoeng Jaya, and a review of documentation related to catfish farming program activities and financial records. This approach allows researchers to gain a comprehensive understanding of the accounting practices applied as well as the various obstacles that arise during the implementation of food security programs. The results of the study show that the catfish cultivation program has failed due to weak internal supervision, the absence of SOPs, lack of financial recording and reporting, and lack of technical competence of managers. The implementation of the public sector accounting cycle has not been running optimally so that it is not able to support effective control and evaluation of programs. In addition, crop failures are exacerbated by theft by internal parties and the absence of a structured monitoring system throughout the program, which indicates weak governance and internal control. This study has limitations in the form of a small number of informants and a lack of formal documents, so the analysis—especially the financial aspect—cannot be generalized widely. Nevertheless, this study contributes by integrating the evaluation of food security programs through the implementation of the public sector accounting cycle and offering recommendations to improve the accountability, transparency, and sustainability of BUMDes programs.

This dissemination activity aims to share findings and reflections from research on the digital transformation of traditional handicraft MSMEs in Makassar, as well as formulate recommendations for collaborative action. Through a multimodal approach (hybrid seminars, interactive discussions) the activity involved 25 participants. The results show success in knowledge transfer, the creation of direct business opportunities, and the preparation of the digital transformation roadmap and Quintuple Helix collaboration protocol. It is concluded that contextual and participatory dissemination is effective in transforming research findings into real action. The suggestions put forward include the need for a strategy of stakeholder engagement, segmentation of materials based on digital literacy levels, and a long-term impact monitoring system to ensure the sustainability of inclusive digital transformation initiatives based on local wisdom. In addition, it is important to strengthen the capacity of MSME actors through advanced training that focuses on the use of digital marketing technology, product management, and market analytics. The sustainability of collaborative networks between academics, governments, communities, industry players, and the media is also a determining factor in accelerating the digital transformation process in a comprehensive and sustainable manner.

The Family Welfare Empowerment (PKK) is a vital community organization with a significant role in addressing social issues. One such issue is the increasing number of juvenile delinquency cases in schools, which has become a major concern for both educational institutions and parents. Schools must be proactive in monitoring their students to address this problem effectively. However, traditional manual monitoring processes are inefficient and time-consuming. To overcome this, there is a need for an efficient, user-friendly, and multi-user monitoring system that enables both schools and parents to access information about their children's behavioral violations. This student violation monitoring system is designed to be web-based, utilizing UML (Unified Modeling Language) for system design and employing the Waterfall development method. The goal of this system is to provide an effective solution for managing and tracking student violations, allowing for better control and prevention of delinquent behavior within the school environment. By implementing this system, both school authorities and parents will be able to collaborate more effectively in monitoring and addressing student behavior.

Information technology (IT) has emerged as a critical component of public administration, offering significant potential to enhance transparency and accountability in governance. This comprehensive qualitative case study research examined how information technology implementation influences transparency and accountability mechanisms in public policy administration, while identifying critical challenges and success factors. The research employed embedded multi-case study methodology, collecting data through 118 semi-structured interviews with government officials (n=45), citizen-users (n=38), IT professionals (n=15), civil society representatives (n=12), and academic researchers (n=8), complemented by document analysis and direct observation (280 hours) across four case sites representing diverse governance contexts. Key findings demonstrate that information technology implementation significantly enhances government transparency through multiple mechanisms: public information portals increased citizen information access from 25-30% to 78-82%, mobile applications extended service accessibility from 15-20% to 42-55% in rural areas, and social media platforms reached 60-70% of citizens with policy information. Similarly, IT implementation strengthened accountability through online complaint systems that reduced government response times from 28-45 days to 5-12 days (60-75% improvement), automated audit systems that detected 35-55% more compliance violations, and real-time monitoring systems that reduced audit completion time by 40-50%.  The findings have implications for government practitioners seeking evidence-based guidance for IT implementation, policymakers developing governance policies leveraging technology, and academic researchers studying digital governance and public administration innovation.