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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.

Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.

The increasing global energy demand drives the search for efficient and sustainable renewable energy solutions. Solar panels have become one of the most widely used technologies; however, their efficiency remains limited when installed in a static position. This research aims to analyze the performance of a single-axis auto tracking system on a 10WP solar panel integrated with the Internet of Things (IoT) for real-time monitoring, specifically in powering a portable powerbank. The research method employed was a quantitative experimental design with three testing scenarios: powerbank charging using an auto-tracking solar panel, a static solar panel, and conventional household electricity as a comparison. Charging data were collected via an IoT system integrated with the Blynk application in real-time. The results indicate that the auto-tracking system increased charging efficiency by around 10%, compared to only 6% with a static panel in one hour. This performance is nearly equal to household electricity charging, which reached approximately 10–11%. The study concludes that the single-axis IoT-based auto-tracking system significantly enhances the performance of small-scale solar panels and holds strong potential for portable energy solutions in remote areas.

Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.

Barru Regency tourism has a variety of tourist attractions, but information related to the location and potential of tourism has not been digitally integrated, making it difficult for tourists and the government to access data. This study aims to design and build a web-based Geographic Information System (GIS) that can map the location of tourist attractions in Barru Regency interactively, easily accessible, and equipped with supporting information in the form of descriptions, types of tourism, photos, and travel routes. The research method used is Research and Development (R&D) with the stages of tourist attraction surveys, interface design, feature development, and system testing. The results of the study are in the form of a website "Web-Based Geographic Information System for Mapping Tourist Attractions in Barru Regency" which is able to present tourist information systematically and easily understood. The conclusion of this study shows that the developed system can be a supporting medium in disseminating tourism information, helping tourists find tourist locations, and supporting the local government in managing and developing the tourism sector based on spatial data. This application also has the potential to be an educational and promotional tool to increase tourist visits to Barru Regency

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.

Indonesia has extraordinary resources and potential in developing renewable energy sources (RES), but various obstacles must be overcome in implementing RES. The purpose of this study is to analyze the gap in the application of RES. This gap includes energy knowledge, community participation, battery waste management, service quality, regulation, and legal policy. This study uses a mixed-methods approach, by conducting a structured questionnaire in quantitative data collection, while qualitative data collection through special interviews, focused group discussions, and conducting policy regulation analysis. The results show that 62% of people do not understand RES, 28% are involved in project planning, and 74% are unaware of SOP (standard operating procedures) regarding battery waste recycling. The results of the correlation analysis reveal a positive relationship between the level of knowledge and interest in RES (R = 0.56). Also, the developed community-based participation model includes initial involvement, transparency of information, and local incentives. These findings further strengthen the compatibility of the innovation diffusion theory, planned behavior theory, SERVQUAL, and the theory of public interest. This study will make a practical contribution through evidence-based strategies in increasing resilience, especially for policymakers and energy service providers. The impact of the policy aspects includes the need for large reforms, education, public campaigns, and the realization of battery waste management systems. This study also provides an opportunity for further study by expanding the geographical scope and related industrial sectors.

Improving the accuracy of emission monitoring in the Continuous Emission Monitoring System (CEMS) is crucial to support compliance with environmental regulations, especially in gas and steam-based power plants (PLTGU). At PT Jawa Satu Power's PLTGU, the purging system on the CEMS sample probe is still performed manually and limited to scheduled preventive maintenance, potentially reducing emission data reliability due to particle contamination. This study aims to design and simulate an automatic purging system based on a Programmable Logic Controller (PLC), taking into account technical parameters such as timer, gas pressure, temperature, and sample flow rate. The system design was carried out through the modeling of automatic control logic using CX-Programmer software, with a protection approach based on real-time conditions and timing. The design results show that the automatic purging system can improve cleaning consistency, reduce the risk of contamination, and enhance the integrity of emission monitoring data. This study is expected to serve as a foundation for developing a more applicable and integrated automated purging system for CEMS in the future.

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring.

The increasing need for electrical energy in Indonesia encourages the development of new renewable energy (EBT), including hydroelectric power plants (PLTA) as an environmentally friendly solution. This research was conducted at the Mentawa Dam, West Toili District, Banggai Regency, Central Sulawesi, to analyze the potential of water energy that can be utilized as a source of micro-hydro power plants (PLTMH). This dam currently functions as irrigation and a tourist attraction, but has significant potential to generate sustainable electrical energy. The research methods include measuring water discharge using the float method, measuring the height of the water fall (head) with an altimeter and GPS, and analyzing the potential for electrical power using the formula P = η ⋅ ρ ⋅ g ⋅ Q ⋅ H. The measurement results show an average water discharge of 3.01 m³ / s and a water fall (head) of 6.56 meters. With a turbine efficiency of 80%, the potential electrical power that can be generated reaches 154.76 kW. This study recommends the use of Kaplan turbines in Mentawa Dam, which are ideal for large discharge conditions and low to medium heads, and can adapt to discharge fluctuations, making it an optimal choice to maximize energy potential. This research is expected to provide sustainable energy solutions for the people of Toili Barat District and support the development of the tourism sector.

This research aims to design and test a genset fuel conversion system from gasoline to HHO gas (brown gas) as a more environmentally friendly alternative. Conventional fossil-fueled generators face challenges in theform of limited resources and the impact of environmental pollution. In this study, HHO gas is produced through a water electrolysis process using an HHO generator that separates water molecules into hydrogen and oxygen using electric current. The test results show that increasing the voltage from 2.58 V to 5.12 V significantly increases the volume of gas produced. At a voltage of 2.58 V, the gas volume reached 110 ml, and increased to 750 ml at a voltage of 4.72 V. The generator set was successfully operated for 1 minute 15 seconds with HHO gas using a separate battery power source. These results demonstrate the potential of HHO gas as an efficient alternative fuel for generator sets. This study recommends further testing with voltage and current variations to find the optimal configuration, as well as the use of a larger power source to increase the efficiency of HHO gas electrolysis.

This study aims to analyze the sentiment of iPhone product reviews fromAmazon using the BERT (Bidirectional Encoder Representations from Transformers) model to classify reviews as either positive or negative. The dataset, sourced from Kaggle, includes text reviews and star ratings, where high ratings indicate positive sentiment and low ratings indicate negative sentiment. After text preprocessing steps, including data cleaning, tokenization, and sentiment labeling, the BERT model was fine-tuned for sentiment classification, with the data split into training, validation, and test sets. Evaluation results demonstrate that the BERT model achieves a high classification accuracy, with an accuracy rate of 93.9% and a balanced F1 score between precision and recall. Confusion matrix evaluation also indicates that the model consistently identifies both positive and negative sentiments. This study shows that Transformer-based models like BERT are highly effective in understanding customer opinions in e-commerce, with broad application potential for data-driven decision-making in marketing strategies and product development.

This research endeavours to delve into the potential of Artificial Intelligence (AI) in bolstering Project Resource Management (PRM), discerning the principal challenges inherent in project resource planning, acquisition, and human as well as physical resource management, while also appraising the utilization of AI tools by project team members (PTM) and their efficacy in daily tasks. This research contributes a theoretical basis for what lies ahead studies in PM and unveils the benefits of AI in task monitoring, scheduling, team assignment, cost estimation, and the monitoring of physical project resource availability. This study employs a mixed-method approach, commencing with an initial literature review to identify project challenges. Data collection is facilitated through the administration of questionnaire surveys and interviews with project managers, encompassing both closed-ended and semi-structured questions. The research reveals that PTM readily embraces the utilization of AI for daily tasks and Project Resource Management can be enhanced through AI.

Football School (SSB) is a supporting facility for children who have potential in the field of soccer in order to improve their skills. Football is a large ball game played in teams with a team of 10 players and 1 goalkeeper. In this game, teamwork is an important value that must be possessed to achieve competitive victory. However, along with problems in the form of discrepancies in operational standardization in soccer schools, causing negative public perceptions of the existence of this school. Therefore, a research was conducted to build a decision-making model in determining the choice of soccer schools in accordance with legal standards for children, namely using the Analytical Hierarchy Process (AHP) method. The AHP method is one method that can solve a problem because there is a hierarchy where there are several important aspects, namely Objects, Criteria, Sub Criteria, and Alternatives. By combining data and knowledge, it can increase effectiveness in decision makers. It is hoped that in the future, this research can help the community to support their children in the field of soccer by determining the appropriate soccer school.

The purpose of this study is to produce a design to predict, analyze and determine the level of potential bankruptcy of a company using the Altman Z-Score method. Predictions are made by analyzing the financial statements of a company. The research approach used is a qualitative approach. Data analysis technique in this research is descriptive analysis technique. The results of the first phase of research are in the form of a review of bankruptcy prediction analysis using secondary data from banks in Indonesia that are already on the stock exchange and have branch offices in the city of Semarang, while the second year produces an information system design that has added value from the first year to the third year. testing the system that has been designed using actual financial statement data.

Rice is the most important staple food for humans to have energy to carry out activities. The purpose of this study is to provide information to the public in the form of a reference for selecting rice based on consumer interest, so that it can be used as a decision making in buying rice. The Mamdani method is a method that is able to solve problems in the case of rice selection based on consumer references. The work process of the Sugeno method consists of four parts, namely fuzification, inference engine, implication function amplification and the last one is defuzzification. The final result, the Mamdani method has the characteristics of using the AND operator and using the min-max value. This research is in the form of a decision-making system in rice selection based on references from consumers by looking at four aspects of criteria such as price, quality, taste, and shape variables, these four aspects can be used as a reference in rice selection. Sugeno fuzzy logic to get the final value.The Mamdani method is a very effective method in selecting rice according to the needs and interests of consumers so that potential consumers can easily choose rice according to their interests and desired criteria.

The waste processing and renewable energy are being the issues in the industrial 4.0 era. Kitchen and organic trash in the rural areas will become a disaster if there is no solutions. On the other hand, kitchen and organic waste have a positive side, that they can be converted into biogas which can be converted into electricity. On the Minister of Energy and Mineral Resources Regulation Number 27 of 2014, supports the development of biogas and will buy electricity from the biogas conversion. The potential for biogas that obtained by the organic trash per 1000 people is 7619.8 liters of biogas, it meas that the energy contained is still abundant and the production can still be increased if processed with a modern digester system. This research produces a tool in the form of a portable digester that named GenBIoT (IoT-based Biogas Generator) which can be used by the public to produce biogas that  used by home industries or general factories. This research used the research method that similar to System Development Life Cycle (SDLC), this tool is built with several stages starting from planning analysis, designing, functional testing, data analysis and reporting of the results or implementing this tool that can be used as an alternative, as a cheap and easy source energy. This tool is based on the Internet of Things so that this tool can be monitored regarding gas pressure in the reactor, biogas volume and it will detect if there is a biogas leak so it will displayed on the system. To produce the maximum biogas composition, 50% of cow dung is required, 40% of kitchen trash and 10% of water with a ratio of 5: 4: 1 so it will produce 40 kg of biogas with a volume of methane gas (VGM) 0.11 m3 of biogas for cow dung. and 0.04 m3. The third experiment becomes the basis of future biogas production with energy produced from biogas per day of 716.39 Wh

Waqf information system In April 2020 shows the number of waqf land in Indonesia has an area of ​​51,259.84 Ha. More waqf received in the form of land creates a problem of disputes and disputes that lead to legal cases Muhammadiyah is the second-largest Islamic organization that has assets in the form of waqf land that has not been able to resolve existing problems. In this study, an analysis of legal potential and waqf land disputes is used using ID3 Algorithm to classify potential land disputes based on the status of the land deed, owner's name, location of waqf and its use so that it can produce data on the ownership rights of waqf land which has the potential for legal disputes and no legal disputes. research shows that potential legal problems are still present. The ID3 algorithm is able to produce information as a basis for reducing disputes and the loss of waqf land.

Utilization of a mobile device with the Android operating system that is used as a supporting medium and supplement in improving learning outcomes combined with demonstration and discovery learning or demon-disco learning methods is needed to help students improve learning outcomes in vocational competence subjects in hydroponic crop cultivation in vocational high schools Kristen Terang Bangsa, because the use of android-based applications can provide new experiences regarding the use of information and communication technology, as well as provide opportunities for students to be able to learn more independently. The concept chosen as a learning media is to explain the material about understanding, introduction of parts, types, and quiz containing questions about hydroponic plant cultivation. This research uses the Research and Development (RnD) product development method, using 6 out of 10 stages research that is the potential and problems, data collection, product design, design validation, design improvement, product trials. The results of this study are to produce learning media in the form of Android-based applications. The validity value of media experts is 2.6 which is declared feasible, while the validity value of material experts is 3.6 which is declared feasible, and is able to increase the average value in the subject of vocational competence in hydroponic cultivation in Christian Terang Bangsa Vocational Schools which initially 67 (with 13 students who had not yet reached KKM) to 80 (with 4 students who had not yet reached KKM).