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The physiological changes that occur during pregnancy, both physically and mentally, may be rather uncomfortable, particularly in the second and third trimesters. Pregnant women often report back discomfort, muscular aches, trouble sleeping, excessive exhaustion, and irregular sleep patterns. Mothers' physical and mental health as well as the health of their unborn children may be significantly impacted by inadequate sleep quality during pregnancy. To enhance comfort and the quality of sleep during pregnancy, one non-pharmacological technique is to use an aromatherapy maternity pillow. An ergonomic and ecologically sustainable invention to enhance mother comfort during pregnancy was the goal of this research, which intended to produce a Pregnancy Pillow Therapy product with pineapple leaf fiber and aromatherapy. A descriptive research design using a prototype creation technique was used in this study. The stages of the research included problem identification, literature review, product design, material selection, prototype manufacturing, and product evaluation. Data were collected through literature studies and observations related to sleep discomfort in pregnancy, maternity pillow utilization, aromatherapy therapy, and pineapple leaf fiber characteristics. The developed product was designed ergonomically to support the back, abdomen, waist, and legs of pregnant women. The addition of aromatherapy was intended to provide a relaxing effect and improve sleep quality. The use of pineapple leaf fiber also supports environmentally friendly product innovation through agricultural waste utilization. The results indicate that Pregnancy Pillow Therapy has the potential to become a supportive product for improving comfort and sleep quality among pregnant women. Further studies are recommended to evaluate product effectiveness directly among pregnant women.

This study discusses the design and testing of a growing media moisture and water level monitoring system based on ESP32-MQTT. The system was developed to support real-time monitoring of growing media conditions and water availability through a monitoring dashboard. This research used an experimental method with a design and implementation approach by developing an ESP32 circuit connected to a capacitive soil moisture sensor and a water level sensor, then sending sensor data to the dashboard through the MQTT protocol and Node-RED. Testing was carried out using a plant pot as the moisture testing medium and a water container as the water level testing medium. The results showed that the growing media moisture sensor displayed 0% in dry and very dry soil conditions, 61% in moist soil, and 89% in very moist soil. The water level sensor displayed 0 cm, 2.4 cm, and 4 cm according to the testing conditions. These results indicate that ESP32-MQTT can be used as an initial prototype for monitoring growing media moisture and water level before being applied to a complete hydroponic or aquaponic system.

Breast tumors are classified into two types, namely benign and malignant tumors, the latter commonly referred to as breast cancer. Breast cancer is one of the major health problems affecting women worldwide, including in Indonesia. According to WHO data in 2022, there were 2.3 million breast cancer cases with 685,000 deaths globally, while in Indonesia, 396,914 cases and 234,511 deaths were reported. The high incidence rate is exacerbated by low public awareness in recognizing early symptoms and performing early detection, resulting in 60–70% of cases being diagnosed at an advanced stage, supported by findings that 65.6% of female students still have a low level of knowledge. Female students were selected as research subjects because they are in a vulnerable reproductive age group and have an important role in increasing awareness of early detection, yet they still have limited knowledge. Based on this, this study aims to design a web-based early detection system for breast tumor risk using the Forward Chaining method, which functions as a tool to identify early symptoms, assess risk levels, and provide information on prevention and initial management. This study employed the method with the Expert System Development Life Cycle (ESDLC) model, consisting of the stages of assessment, knowledge acquisition, design, testing, and documentation, along with the Forward Chaining inference method.

This community service activity aims to design a prototype of a web-based Intellectual Property Rights (IPR) registration information system at the South Sumatra Provincial Industry Office as an effort to support the improvement of public service quality. The main problem faced by partners is the manual registration and management process for IPR data, which has the potential to cause service delays, data recording errors, and difficulties in monitoring the status of IPR applications. This condition requires an information technology-based solution that can improve the efficiency, effectiveness, and transparency of services. The method used in this community service activity is the prototype method, which includes the stages of problem identification, user needs analysis, system design, and initial evaluation of the resulting prototype. The designed information system prototype includes features for online IPR registration, applicant and document data management, and application status monitoring. The results of the activity indicate that the IPR registration information system prototype is able to provide an overview of digital solutions that are in accordance with partner needs and have the potential to improve service efficiency and ease of data management. This activity is expected to become the basis for the development of a more comprehensive and sustainable IPR information system within the South Sumatra Provincial Industry Office.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

This study aims to analyze and design the electrical installation requirements for a prototype residential building with dimensions of 11 × 19 meters. The research applies a technical calculation approach based on lighting standards, air conditioning capacity estimation, load analysis, and electrical protection planning. Lighting requirements were determined using the lumen method by considering room area, illumination standards, coefficient of utilization, and light loss factor. Air conditioning capacity was calculated based on room area and standard cooling load values in BTU/hr. Furthermore, total electrical loads were analyzed to determine installed capacity, nominal current, Miniature Circuit Breaker (MCB) ratings, and appropriate cable cross-sectional areas with safety factors applied. The results indicate that proper load grouping, appropriate MCB selection, and correct cable sizing significantly enhance system safety and reliability. The study provides a systematic planning model for residential electrical installations that meets safety, efficiency, and technical standards, and can serve as a reference for similar residential building designs.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

The development of automation and robotics technology has driven innovation in various industrial fields, particularly in automatic sorting systems. Manual sorting processes often lead to inefficiencies and human errors, creating the need for an automatic, fast, and accurate system. This research employs a qualitative method which includes experimentation, testing, and system documentation. The system is designed as a robotic arm for sorting objects based on color, utilizing a TCS3200 color sensor and an ESP32 microcontroller. An ultrasonic sensor detects the presence of objects, while the sorting results are displayed through a real-time web monitoring system. The test results show that the prototype successfully sorts four primary colors (red, green, blue, and yellow) with a high level of accuracy. This research is expected to serve as a reference for the development of automation systems and robotics learning tools in both educational and industrial applications. In addition, this research also contributes to the development of technology that can increase efficiency and accuracy in industrial production processes and provide more environmentally friendly solutions by reducing the need for manual labor.

PT UVW uses jigs in the vacuum metalizing process that require washing, which is currently done manually, resulting in inefficiency and risks to operators, thus, jig washer machine was created in order to reduce the risk for operators and increasing efficiency, which based on horizontal mixer. This study analyses the effect of agitator design on fluid flow patterns in a jig washing machine to obtain an optimal design. Two agitator design alternatives were developed and tested using Computational Fluid Dynamics (CFD) simulations at rotational speeds of 70, 75, and 100 rpm, followed by prototype fabrication and performance testing. Simulation results show that the second design produces a higher fluid velocity (20.4 m/s) and a more turbulent and uniform flow pattern compared to the first design (1.7 m/s). Field tests confirmed that the second design achieves higher washing effectiveness (97.14%) with reduced water consumption and shorter washing time. The optimally designed agitator washing machine increases productivity by six times compared to manual washing and reduces operator exposure to hazardous chemicals. This study recommends the second agitator design for improving washing efficiency and effectiveness.

The increasing demand for electrical energy each year and the high dependence on fossil energy, which has negative environmental impacts, necessitate the development of alternative renewable energy sources. One potential source that can be utilized is mechanical energy from human activities through the application of piezoelectric technology in paving blocks. In addition, studies on the effect of piezoelectric circuit configurations, particularly comparisons between series and parallel circuits in generating electrical power, are still limited. This study employed an experimental method using a piezoelectric paving block prototype, with testing conducted under a static load of 60 kg. The measured parameters included output voltage and current, which were then used to calculate the generated power. The experimental results show that the parallel circuit configuration produced a higher average electrical power of 1.51 mW compared to the series circuit, which generated an average power of 1.37 mW. The increase in power in the parallel configuration was mainly influenced by the higher output current, while the difference in voltage was relatively insignificant. These findings contribute to determining a more optimal circuit configuration for the development of piezoelectric paving blocks as a renewable energy harvesting system based on mechanical pressure.

Stock monitoring is a critical phase that must be performed regularly to maintain the accuracy and efficiency of inventory management. Continuous monitoring ensures that all items remain under proper oversight, thereby making stock management processes simpler, more controlled, and highly accurate. PT XYZ operates in the general contracting sector and provides a range of services, including land transportation, crude oil rental, heavy equipment and light vehicle rental, material supply, and well maintenance services. At present, stock monitoring at PT XYZ still relies on a general-purpose application designed solely for numerical calculations. This approach leads to several limitations in the current system, most notably the lack of a login feature and the requirement to recreate reports using a separate application. Based on these problems, this research aims to design a prototype of a stock monitoring information system at PT XYZ. The system is developed using the waterfall development model and documented using use case diagrams. The main output of this study is a prototype information system that allows users to change their own password, perform CRUD operations on data entities including users, items, categories, brands, units, vehicles, suppliers, incoming goods, and outgoing goods, generate various reports related to inventory/stock at PT XYZ.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

Health services at the Puskesmas (Community Health Center) are an important sector directly related to the community. However, there are still various challenges in patient data management and handling complaints that can hinder service efficiency. One of the efforts to improve service quality is by developing a complaint information system that can efficiently manage and record patient complaints. This study aims to develop a complaint information system for services at the Puskesmas Waimangura using the Prototype method. This method was chosen because of its ability to produce system prototypes that can be immediately tested and developed according to user needs. The system is designed to allow patients to submit complaints related to the services received, as well as enabling Puskesmas staff to follow up on and record each complaint systematically. With the implementation of this system, it is expected to increase efficiency in managing complaint data, speed up problem resolution processes, and improve accuracy in recording patient and complaint data. The results of prototype testing show that this system simplifies the complaint process and provides convenience for staff in following up on patient complaints. The implementation of this information system is expected to improve the quality of services at Puskesmas Waimangura and accelerate responses to issues faced by patients.

This study aims to design, implement, and test a prototype that automates three functions, namely watering, fertilizing, and pest control based on Arduino Uno with the ability to directly monitor soil moisture and pH. This system is equipped with four main types of sensors. Soil condition monitoring involves an FC-28 soil moisture sensor and a soil pH sensor, water level measurement involves an HC-SR04 ultrasonic sensor, and pest detection in the plant area involves a RIP sensor. All data obtained from these sensors is then processed by the Arduino Uno microcontroller to automatically activate actuators such as water pumps, liquid fertilizer pumps, buzzers, and DC motors according to soil conditions and plant needs. Prototype testing was conducted on simulated land with various scenarios of moisture, soil pH, and pest activity. The test results revealed that the system was proven to be able to significantly optimize water and fertilizer utilization, as well as reduce pest disturbances that could potentially damage plants.  In addition, this system also displays the operational status directly through an LCD screen, making it easy for users to monitor. The advantage of this system is its multi-function integration in a single device that is cost-effective and easy to operate. In the future, the functionality of this system can be improved through integration with Internet of Things (IoT) technology, enabling remote monitoring and control with greater efficiency. More broadly, this study is expected to support increased production and sustainable agricultural practices in Indonesia.

The increasing volume and complexity of data have made traditional 2D visualization methods insufficient for effectively exploring and understanding high-dimensional datasets. Immersive Virtual Reality (VR) presents a promising solution by providing an interactive 3D environment that enhances spatial understanding, task efficiency, and user satisfaction. This research aims to evaluate the user experience (UX) and interaction design quality of immersive VR interfaces for 3D data visualization in complex environments. The study employs a mixed-methods approach, combining usability testing, UX questionnaires, and task-based performance analysis. Participants interacted with VR prototypes designed to visualize complex data and were assessed on their ability to manipulate and explore the data efficiently. The findings show that immersive VR interfaces significantly improve spatial comprehension, reduce cognitive load, and increase task performance efficiency compared to traditional 2D systems. Additionally, user satisfaction was notably high, with participants appreciating the intuitive and engaging interaction methods. The study concludes that immersive VR can provide substantial benefits in real-world data visualization applications, particularly in domains requiring the exploration of complex and high-dimensional data. However, further research is needed to optimize VR interfaces and address challenges such as motion sickness and interaction complexity.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

Semantic interoperability remains a major challenge in large scale distributed information systems due to heterogeneous data schemas, diverse contextual interpretations, and the dynamic nature of distributed environments. Traditional metadata-based interoperability approaches are often insufficient to address these challenges, as they lack semantic expressiveness and adaptability. This study proposes a context aware knowledge graph framework to enhance semantic interoperability across heterogeneous distributed systems. The research adopts a design-oriented methodology involving requirement analysis, knowledge graph construction, ontology modeling and alignment, context aware semantic representation, and semantic reasoning. A prototype implementation is developed to evaluate the effectiveness of the proposed framework through interoperability scenarios and cross-system semantic queries. The results demonstrate that the proposed approach significantly improves semantic alignment accuracy, query precision, and recall compared to conventional metadata-based solutions. The explicit integration of contextual information and ontology-based reasoning enables adaptive semantic interpretation and reduces ambiguity across systems. Overall, the findings confirm that combining knowledge graphs with ontology modeling and context aware mechanisms provides a robust and scalable solution for improving semantic interoperability in complex distributed information systems.

The management of borrowing and returning goods in government institutions is still largely carried out manually, which often gives rise to various problems, such as difficulties in tracking borrowing histories, the high risk of recording errors, and the lack of transparency regarding the availability of goods. This condition indicates the need for support from an information system that is capable of assisting the recording process and inventory management in a more structured, systematic, and easily monitored manner. This research aims to design a prototype of a goods borrowing and returning application as an initial solution to these problems as well as a preliminary representation of a system that can be further developed by the institution. The research method used is a Research and Development (R&D) approach with a prototype Software Development Life Cycle (SDLC) model, which allows system development to be carried out gradually and flexibly in accordance with user needs. Data collection was conducted through observation and interviews to obtain an overview of the ongoing work processes and to identify the required system needs. The collected data were then analyzed as the basis for designing the system workflow, data structure, and user interface design. The results of this research are in the form of an application prototype that represents the main system features, including goods borrowing, goods returning, availability checking, goods data management, and report generation.

This study examines community capacity building for flood mitigation in flood-prone regions of Central Kalimantan through disaster education, technical training, and the construction of an amphibious house prototype. Using a community-based disaster risk reduction (CBDRR) framework, the program integrates participatory training, field surveys, and adaptive structural innovation. Findings indicate a 40% increase in community knowledge based on  Community Empowerment Level Analysis results, active engagement of 35 participants in disaster education, and significant improvement in technical skills among 22 trainees involved in amphibious foundation construction. The prototype achieved 100% completion within four effective working days, demonstrating the feasibility of amphibious technology using locally available materials. Strengthening youth organization structures further enhances community readiness and institutional resilience. Overall, the integration of participatory learning and adaptive technology effectively builds community self-efficacy and disaster preparedness in flood-prone environments.