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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.

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.

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.

This research aims to be able to meet the water supply of lettuce plants automatically by using three sensors such as soil moisture, water level, and water discharge. The goal is to provide water needs to plants automatically and regularly. The developed tool uses YL-96 sensor for soil moisture, HC-SR04 for water level and YF-S201 for water discharge. Sensor data is sent to the arduino to be processed using the fuzzy mamdani method so that these three data values affect the movement of the tap servo motor that flows to the lettuce plant. Fuzzy logic here as a decision maker from the value of 3 sensor data and then processed automatically by arduino using fuzzy mamdani to determine how many degrees the servo motor moves. The result is that the Lettuce Plant Water Needs Analysis System Automation Tool is able to maintain the water supply of lettuce plants and soil moisture ideally at 76% with a servo motor movement system success rate of 100%.

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.

This research aims to design and simulate a DC motor speed control system using integral state feedback (KISF) control. This control is designed to maintain DC motor speed stability with high accuracy despite disturbances or load changes. The research stages include mathematical modeling of the DC motor, PID control design, integral state feedback control design, simulation implementation and simulation results analysis. The show that althought integral state feedback control has a rise time 273.501 ms, slightly slower than PID 197.604 ms, integral state feedback compensates with higher slew rate of change 6.540 V/ms compared to PID 4.344 V/ms. Integral state feedback offers greater flexibility through pole placement values with Ackerman’s formula J3 = [-12 -100 -600], indicating that this system has good capability in responding to input changes, thus maintaining motor speed stably and efficiently. This indicates that integral state feedback control is superior in system adaptation and handling complex dynamics.

This study aims to develop an automated pipeline for data cleaning using Pandas and Scikit-learn. The data cleaning process is often performed manually, requiring a long time and prone to errors. This study uses a quantitative experimental method with a dataset of 100,000 rows of e-commerce transaction data. The results show that the automated pipeline reduces missing values by 95.7% and outliers by 91.7%, and accelerates processing time by 35% compared to manual methods. The distribution of data after cleaning becomes more stable, allowing for more accurate analysis. This study contributes to the development of a more efficient and accurate automated data cleaning approach.Keywords: Systematic Literature Review, Artificial Intelligence and Marketing Strategy.

The study titled "The Influence of System and Service Quality on User Satisfaction of Silalapbaja Website" aims to evaluate the impact of each independent variable on the dependent variable. There were 51 respondents involved in this research, with data analysis conducted using SPSS version 25. The results indicate that System and Service Quality significantly influence User Satisfaction, as evidenced by the significant value of Fhitung = 0.000 < 0.05. From the t-test, System and Service Quality variables partially affect User Satisfaction, while Information Quality does not have a significant partial effect. For instance, the significant values of thitung X1 = 0.000 < 0.05 and X3 = 0.49 < 0.05, whereas X2 = 0.094 > 0.05. The variables that significantly influence are System Quality with a Standardized Coefficients Beta of 0.552 and Service Quality of 0.220. Based on the research findings, it can be concluded that System, Information, and Service Quality contribute to 59% of User Satisfaction, while other factors beyond this study affect 41%. Therefore, it is recommended for the developers of the Silalapbaja Website to prioritize improvements in System and Service Quality to enhance User Satisfaction.

According to the Jakarta Air Quality Index (AQI US) 12 July 2023, 200 indicates unhealthy air quality with an index value between 151 and 200. This figure even shows that Jakarta is currently the second most polluted city in Southeast Asia. (CNN Indonesia., 2023). This incident gave rise to responses from the public which were expressed via social media Twitter. From this incident, sentiment analysis was carried out regarding Jakarta's air quality. The amount of data used for this research was 500 tweet data. The results of the positive and negative sentiment analysis show that negative sentiment appears more frequently than positive sentiment with a percentage of 7% positive sentiment and 14% negative sentiment, by using the Rstudio application. This method uses the naïve Bayes classifier. Data division in the dataset with training data 1:499 and test data 1:476. It was found that the results of the Accuracy, Precision, Recall, and F1-Score values were Accuracy 87.50%, Precision 87.50 Recall 93.33%, and F1-Score 82.35%.       

Mushrooms are very diverse with characteristics of each type, there are 1,433,800 types of mushrooms that have not been recognized. In this study, researchers used the Neural Network and Deep Learning Inception V3 methods as a feature extraction process in images to classify mushroom images based on genus with the Orange Data Mining application. There are 9 genera of mushrooms used in this study, namely Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, and Suillus. The total dataset used is 2,700, with 300 images for each genus. The test uses the cross-validation method which is applied to the confusion matrix to get precision, recall, F1-score, and accuracy values. In this study, the final classification results were obtained with an accuracy of 82.5% and the genus Boletus mushroom obtained the best results with an accuracy of 98.9%.

Lot of problems arise in selecting scholarship recipients in a large number of submissions, the existence of several searches used, and the selection of files for scholarship applicants is still manual, so a system is needed that can speed up, help, and make it easier in the decision-making process to lighten work. student section. In supporting decisions this system will use the Naïve Bayes Classifier Method to determine what is acceptable and not acceptable. The NBC method can analyze and make improvements to old data, and the resulting data will provide simpler probability values that can later be used to make decisions. From the results of the research that has been carried out, it can be realized that the application of the data mining algorithm using the Naïve Bayes Classifier can be carried out to select scholarship recipients at Stikubank University Semarang. The result of the selection of Unisbank Semarang scholarship recipients is the accuracy value. 72% of the 135 data which is divided into 100 training data and 35 test data.

Selection of the best employees is a semi-structured matter that can be scheduled every month or year. It is not uncommon for companies to determine the best employees not in accordance with the company's assessment. Kean shops still use the manual method and often do not meet the criteria set by the company. By making the best employee decision support system in a grocery store using the simple additive weighting method. assist managers in making the best employee decisions and admins in editing, deleting and adding values in each of the existing criteria and directly connected to the system database. This system was developed using the Visual Studio Code website, PHP, CSS, XAMPP V3.3.0 and MySQL. The method used in this decision support system is simple additive weighting (SAW). With this website, decision making is more structured and value data for each employee can be stored in a database. The results of this decision support system will simplify the process of determining the best employees and this system does not need to use manual calculations and is far more efficient.

Stroke is the number one cause of disability and the second cause of death in the world. Blood flow that is not smooth in stroke patients causes hemodynamic disorders including changes in the value of oxygen saturation in the blood (SpO2) which can interfere with the function of internal organs including the heart due to lack of oxygen intake. So we need a system to monitor the oxygen saturation value which can be used as an early indicator in recognizing stroke patients. This system uses detection of two sensors to measure oxygen saturation in the blood, a microcontroller to process data, a monitor to display data, a buzzer as a warning of the lower limit of measurement and a micro sd card module to store measurement data that has been running. Parameters displayed include the date of measurement, patient ID, SpO2-1, SpO2-2, last SpO2-1 and last SpO2-2. This system will measure and display the SpO2 value of both right and left arms simultaneously. Stroke patients who experience muscle weakness in one hand may experience different values with a hand that does not experience muscle weakness. This study aims to develop an SpO2 monitoring system that will be used to detect stroke patients with non-invasive methods. The results show that the system has been developed and can be used to measure the patient's SpO2 from both hands simultaneously with a measurement error rate of ±2% for each sensor from standard medical equipment.

Pusattinta.com Store is engaged in selling printer ink. The problem faced is difficult to determine the amount of stock to meet customer needs and not cause accumulation of goods for a long time. With the above problems, a forecasting application was made using the Java programming language with the WMA, SMA, and DES methods. The WMA method is assigned a different weight for each available past historical data, SMA is a value smoothing forecasting method by taking a group of observed values ​​and looking for the average, DES processes smoothing twice. Where each data is given a weight symbol. The magnitude of is 0 to 1. From the 3 methods, it is known that the average MSE of each method is WMA : 102,898.57, SMA : 89,579,446 and DES : 87,156,989. The method with the smallest MSE average is the most appropriate method to use.

Water is very important for all aspects of life on earth, agriculture is one of the fields that really need large amounts of water because of the use of water in the process of plant photosynthesis. Water that has good quality is if the water is not excessively polluted by harmful chemicals or minerals. One indicator that water is polluted is a change in temperature and pH (acidity) of the water. Temperatures that are too hot in the water will interfere with the growth of plants and other microorganisms. While the normal pH of water has a pH that ranges from 6.5 to 7.5. The quality of water and soil is very important in agriculture. The level of acidity (pH) and soil temperature are one of the things that affect plant fertility. Therefore, the quality of water and soil on agricultural land is one of the important things that needs special attention in its management. One solution so that water and soil quality can be monitored and managed efficiently is to utilize a Wireless Sensor Network based on the Internet of Things (IoT). The use of the ESP8266 Module as a WIFI module, is widely used by Internet of Things-based applications because the price is cheap so it reduces a lot of costs and has a fairly good speed of 80 MHz. This study aims to develop the concept of a Wireless Sensor Network by utilizing the ESP8266 module to monitor pH values using a pH Meter Analog Kit sensor and temperature from agricultural land using a DS18B20 Waterproof sensor and can be monitored at any time using a smartphone.

Puzzle game is a game that shifts numbers from a box consisting of nine boxes. Eight boxes must have values arranged in numerical order starting from numbers 1 to 8. Puzzle games can produce the correct sequence according to the initial state provided that they follow the rules established rules. Completion of this game using a heuristic method, using the Ascent hill Climbing algorithm. The working process of the Ascent hill Climbing method is a process of looking for several possible solutions in order to get the optimal value for solving the problem by arranging the values from the position of the smallest value to the position of the largest value. The problem that is often experienced in this case is a lack of user knowledge in the concept of puzzle game rules so that search results are difficult to find,with this method it can make it easier to solve puzzle game cases by following the game rules and done systematically so that Goals are quickly found. The Goal results obtained are in the form of steps in the process of finding a solution and calculating the time required in the search to find a solution.

Forecasting is an estimate of future demand based on several forecasting variables based on historical time series or a process of using historical data (past data) that has been owned to use this model and use this model to estimate future conditions.The Ivori mini market SME group is known to be a mini market that sells daily necessities. The goods provided by the ivori mini market are not focused on only one type of goods, but include all types of goods. Ivori mini market often runs out of stock because there is no inventory planning. The main purpose of making this application is to assist employees in determining inventory planning that must be provided next month. While the method used to make this forecast is a single moving average, one of the time series methods in forecasting. Single Moving Average is a forecasting method that is done by collecting a group of observed values, looking for the average value as a forecast for the future period. The result of this forecasting is to predict the number of sales that will occur in the coming month.

The implementation of the 2020 Pilkada in Gunungpati District as a whole has improved in terms of the quality of its implementation. This is the result of the cooperation of all competent parties at the sub-district and village levels. This study aims to analyze the collaboration of the Supervisory Committee for the Election of Governors and Deputy Governors in 2020, Gunungpati District and to understand in depth the flow of information using Social Network Analysis (SNA). The results showed that the overall density of the supervisory committee network was 0.53 or 53%, so the characteristics of the network of members of the Panwaslu Kec.Gunungpati network were in the high category. Panwaslu members with the initials DAP, RV, WPU and M are the most dominant members of the Panwaslu with values of Centrality, Closeness and Betweness Centrality in the network.  

The infrared detector (or IR detector) is an optoelectronic component and represents the core element of gas analyzers, flame sensors, devices of spectral analysis, as well as non-contact temperature measurement. pyroelectric infrared detectors work with  stabilization time  in a wide operating temperature range (-55 ... 85) °C, capable of measuring the slightest amounts of infrared radiation (as little as a fraction of a nW) with wavelengths between 1 µm and > 25 µm. The difference of the good pyroelectric or No good (NG) devices shown by graph of voltage offset operating that can check by manual using Digital Multi Meter or semi auto by using microcontroller One of importance measuring of IRD sensors  is Offset Voltage, stabilization of offset voltage indicate the sensor is stable with certain level value. Stabilization value measurement during measuring time means the average value not so much different with fluctuation value.  10 bit ADC on Atmega 328 that main microcontroller on Arduino Uno is capable to measure the Offset Voltage of IRD sensor and check the measurement values is within specification of IRD sensor.