Vocational high schools (SMK) play an important role in preparing students for the world of work. However, student learning outcomes in the subject of Electrical Lighting Installation at SMK Negeri 5 Kota Padang are still relatively low, with an average pretest score of 57.34. This study aims to test the effectiveness of the Student Teams Achievement Division (STAD) cooperative learning model in improving student learning outcomes. The research method used was classroom action research with 32 students from class XI TITL as subjects. Data were collected through pre-tests, post-tests, and observations of student activities during learning. The results of the study indicate that the implementation of STAD successfully improved student learning outcomes significantly, with an average post-test score of 82.19 and an N-Gain Score of 0.58 (moderate category). Academic achievement also increased from 15.6% to 75%. Further analysis revealed that collaborative interactions within groups, the reward system, and active student participation were key factors contributing to the success of this method. However, challenges such as time management and low student engagement require attention. It is concluded that the STAD model is effective in improving students' learning outcomes in the subject of Electrical Lighting Installation, both in terms of cognitive aspects and social skills. Therefore, it is recommended that teachers implement STAD with proper syntax and consider developing supporting media such as digital simulations to optimize the learning process.

A conveyor is a tool with a large rubber band arranged transversely between two pulleys at a constant speed as a transporter of solid material in this case coal with the provisions of the permitted amount. Damage to the conveyor tool has a major impact on the smooth running of a coal loading and unloading process, so a coal monitoring tool was designed using an ESP32 microcontroller-based data logger. The purpose of this study is to find out how to design a coal load monitoring tool based on a data logger using the Research and Development (R&D) method type ADDIE. Using the following types of testing: static and dynamic. The research results show that the load monitoring system based on a load cell and ESP32 microcontroller demonstrates high accuracy, with a reading deviation of less than 2% from the actual weight. The system is also considered effective, as it can detect loads, adjust the hatch door angle, and transmit data to Google Spreadsheet in real-time without disruptions. Its ability to adapt to varying loads indicates that the prototype operates stably, responsively, and in accordance with the design objectives.

This research aims to design a Solar Power System (PLTS) for households that can meet an energy demand of 4.5 kWh per day. The system design takes into account the efficiency of solar panels and the average solar radiation potential in Semarang, which is around 5 hours per day. The method used in this research involves calculating the required solar panel capacity based on the daily energy needs of the household, panel efficiency, and solar radiation hours. The calculation results show that the required PLTS capacity is 1125 Wp, which is then designed using 4 solar panels with a capacity of 300 Wp each. This system is expected to allow households to meet their energy needs independently and sustainably. The implication of this research is to provide an alternative solution for households in reducing their dependence on conventional energy sources, while contributing to the effort to reduce greenhouse gas emissions.

Digital transformation through Internet of Things (IoT) in warehouse management represents a paradigmatic revolution in contemporary logistics systems. This research comprehensively explores the complexity of IoT implementation, analyzing critical factors influencing digital transformation in storage systems. Utilizing a systematic literature review methodology, the research unveils multidimensional dynamics involving complex interactions between advanced technology, organizational structures, and human resources. Findings indicate that successful IoT implementation requires a holistic approach that transcends mere technological adoption, demanding a fundamental reconstruction of business ecosystems. Key factors include top management support, sustainable infrastructure investment, human resource competency development, and innovation culture creation. The research affirms that digital transformation is not simply a technological project, but a complex strategic journey requiring long-term commitment and sustainable adaptive capabilities. Research implications provide profound insights for academics and practitioners in designing effective digital transformation strategies in warehouse management.

This study aims to design and develop a GPS tracking system for ships integrated with Internet of Things (IoT) technology. The system enables real-time ship position monitoring via the Telegram application by utilizing ESP32, SIM800L, and GPS Neo 6M modules. The research employs an experimental method with both static and dynamic testing of hardware components such as GPS modules, SD cards, and battery performance. The test results demonstrate that the system successfully transmits accurate coordinate data to Telegram and logs the location on the SD card module. This system is expected to serve as an alternative solution to improve ship tracking security and efficiency, particularly in emergency situations or AIS signal loss.

The determination of outstanding students is based on different criteria, depending on the type of achievement that is to be measured. At SMK Migas Cepu, this assessment is typically based on the highest academic score from the class promotion exam. However, this method is considered less accurate and problematic in terms of grouping students. To address this issue, a clustering method using the K-Means algorithm can be applied. The purpose of this research is to build a K-Means model to determine outstanding students. The data used in this study comes from the report card ledger of class XI Machine A and B for the year 2022, which includes 71 students at SMK Migas Cepu. The RapidMiner tool was used to build the K-Means model and cluster the data based on student characteristics. The first test conducted using Excel resulted in two clusters: 35 outstanding students and 36 non-outstanding students. Meanwhile, the second test using the RapidMiner model produced two clusters with a distribution of 26 outstanding students and 45 non-outstanding students.

In global competition, companies must be able to set competitive selling prices. Production cost components such as raw material costs, direct labor costs, and factory overhead costs are all included in the cost of goods manufactured. This study analyzes the COGS with Job Order Costing at PT. XYZ, a make-to-order company. The study was conducted by comparing COGS with Job Order Costing between Box Roller Conveyor and Bag Stop & Go Belt Conveyor as well as material usage and outsourcing. The results of the study indicate that Job Order Costing at PT.XYZ accurately traces the contribution of material costs and outsourcing to total production costs, and evaluates efficiency and cost control on each project with different characteristics. The use of Job Order Costing shows that Bag Stop & Go Belt Conveyor has a higher COGS of Rp299,655,813 than the COGS of Box Roller Conveyor of only Rp209,740,142. The cost structure shows that outsourcing is the most dominant component, reaching more than 98% of HPP on Bag Stop & Go Belt Conveyor, while raw material costs contribute less than 3%, so it can be concluded that the outsourcing vendor management strategy greatly affects the efficiency of production costs at PT. XYZ.

Methane gas detection is crucial in the oil and gas sector to enhance safety and operational efficiency. This study examines the impact of three types of gas detectors—catalytic, infrared, and ultrasonic sensors—on accuracy and response time. The research was conducted at PT PHM's onshore and offshore sites to evaluate sensor performance in operational environments. A quantitative approach with direct field observation was used. Data were collected by measuring methane gas concentrations indicated by detectors, which were then compared to standard gas concentrations. Response times were recorded when the detectors were exposed to methane concentrations of 2.5% LEL until the alarm triggered at 40% of full scale. Data analysis was performed using descriptive statistics, homogeneity test, normality test, ANOVA, and post hoc tests. The results show that the infrared detector had a response time of 2.87 seconds with an accuracy of 0.218%. The catalytic detector had a response time of 8.91 seconds and accuracy of 0.489% and the ultrasonic detector had a response time of 6.15 seconds and accuracy of 0.842%. Overall, the infrared detector demonstrated the best performance in response time and is recommended for use at PT PHM.

The detection of hazardous gases, such as carbon monoxide (CO), is crucial for ensuring human safety and health, especially in enclosed environments or areas with a high potential for exposure to toxic gases. Carbon monoxide is a colorless, odorless gas that is extremely dangerous when inhaled in large amounts. Therefore, a CO detection system that can provide early warnings is essential. This research aims to design a carbon monoxide (CO) detector using the MQ-7 sensor, which can detect CO concentrations in the air. The detection system is developed based on Internet of Things (IoT) technology, enabling real-time CO detection data to be transmitted to a cloud platform or user devices via an internet connection. This allows users to monitor CO levels in their environment anytime and anywhere. The system consists of the MQ-7 sensor to detect CO levels, a microcontroller as the controller, and a communication module to send data to the internet. The main advantage of this system is its ability to notify users if CO levels exceed a safe threshold. Thus, this detector can function as a preventive tool against CO exposure accidents. The use of IoT in this system also facilitates remote monitoring, improving the effectiveness and efficiency of managing a safe environment.

This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.