This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

The Paiton Steam Power Plant (PLTU) is one of the main sources of electrical energy in East Java, which plays a vital role in maintaining a sustainable electricity supply. The reliability of generator units is a key element in maintaining stable energy distribution. However, the high frequency of sudden generator failures poses serious challenges, such as increased downtime and increased maintenance costs. To address these challenges, this study aims to design a generator maintenance prediction model based on the Naive Bayes algorithm with a predictive maintenance approach. This study uses historical maintenance data and key sensor parameters such as temperature, oil pressure, and vibration as input. The data is analyzed through several stages, namely data preprocessing, selection of relevant features, and labeling generator conditions into three categories: Normal, Warning, and Critical. The Naive Bayes model is trained to classify the data probabilistically to generate predictions of future generator conditions. Model evaluation using accuracy metrics and a confusion matrix shows that the model successfully achieved an accuracy rate of 89% and was able to provide early warnings of potential failures up to 3 days before failure occurs. The implementation of this system is expected to support the shift in maintenance strategies from reactive and scheduled systems to data-driven predictive systems. Implementing failure predictions allows the technical team at the Paiton PLTU to conduct planned maintenance, avoid sudden disruptions, and extend equipment lifespan. Thus, this model has the potential to reduce operational downtime by up to 25%, while providing significant savings in operational and logistics costs. This research also shows that integrating machine learning technology into energy facility management can improve the efficiency and resilience of the overall electric power system.

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.

Extreme climate change in Indonesia, caused by air pollution from the use of fossil energy, has encouraged the development of renewable energy technology, including Wind Power Plants (PLTB). This technology is increasingly important for providing clean energy, especially in the maritime industry. The aim of this applied scientific work is to provide a battery charging solution when the emergency battery charger charging system on commercial ships experiences problems. To make it easier to monitor the power produced by wind power plants and also battery capacity via a smartphone application in real time. The method used in this applied scientific research research is experimental. The results of calculating the average power during testing at night show an average power of 10.22 Watt/min and an average current of 0.78 A. So it can be concluded that battery charging takes 11.53 hours. Meanwhile, the results of calculating the average power during testing during the day show an average power of 13.41 Watt/min and an average current of 1.02 A. So it can be concluded that charging the battery lasts for 8.82 hours. From the two data obtained, it can be concluded that charging is optimal during the day because the efficient charging time is 8.82 hours.

The increasing reliance on information technology (IT) across various sectors has led to the growing importance of managing IT risks effectively. Information technology systems are prone to a variety of risks, including cybersecurity threats, system failures, and human errors. These risks can have severe consequences, such as financial loss, data breaches, and operational disruptions. To address these challenges, organizations must adopt comprehensive risk management strategies. This literature-based study explores various approaches and frameworks for managing IT risks, such as ISO 27001, NIST, and OCTAVE, by reviewing existing research and methodologies. The study aims to identify best practices, challenges, and trends in the field of IT risk management, offering insights into how organizations can improve their risk management strategies to enhance the resilience of their IT systems. Through this literature review, the paper highlights the importance of a systematic approach to IT risk management and provides recommendations for organizations seeking to mitigate risks and safeguard their technological.

The aim of this study is to design a battery charging system using solar panels, integrated with an IoT-based monitoring system to improve efficiency and ease of supervision. The system utilizes the photovoltaic effect to convert solar energy into electrical energy, which is then stored in a battery through the regulation of a solar charge controller. The main components in this design include a solar panel, lead-acid battery, ESP32 microcontroller, current and voltage sensors, and the Blynk application for remote monitoring. The research method involves several stages, including the design of hardware such as solar panels, current and voltage sensors, and a step-down regulator, as well as software development using the Blynk platform and ESP32 programming. Test results show that the system is capable of achieving charging efficiencies between 89.45% and 99.57%, with optimal performance under clear weather and maximum sunlight conditions. The IoT system performs well up to a distance of 10–15 meters, with an average data transmission delay of less than 1.5 seconds. Therefore, this device proves to be effective as an independent battery charging and energy monitoring solution in locations without permanent electricity access.

Worldwide trade is conducted 80% through maritime routes, including the export and import of coal. Coal is a non-renewable energy source, often used as fuel for power plants. As technology advances, detecting metal contaminants in coal cargo becomes crucial to prevent contamination of coal with metal materials. This study aims to (1) understand the operation of a conveyor in transporting coal, and (2) assess how the Proximity inductive sensor detects metal presence. The methods used in this research include real data analysis and literature review, conducted aboard the ship. The researcher designed and developed a prototype system for this purpose. The Proximity inductive sensor is utilized to detect metal contaminants, such as aluminum, iron, and stainless steel, among the coal cargo. The research was carried out during sea practice (prola) aboard the WHS ISKANDAR 1. The findings indicate that the conveyor is driven by a DC motor, and the Proximity inductive sensor effectively detects metals to separate them from the coal cargo, thus purifying the coal from metal contaminants.

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.

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 research with the title Application of Fire and Gas Detection Systems takes place at the North Field Expansion Project (NFXP). The methods used are direct observation, interviews, simulations and literature studies. The objectives of the study were to determine the application of fire and gas detection systems, and  to determine the challenges in the application of fire and gas detection systems in the North Field Expansion Project (NFXP). The results obtained  show that the Fire and Gas Detection System implemented at NFXP is in compliance with international safety standards, and the system is able to detect fire threats and gas leaks with high efficiency through advanced detector technology, automatic response, and integration with other safety systems such as IPS and HVAC. The effectiveness of the system is reflected in the ability to detect threats in less than 5 seconds, with detection coverage that meets the target of 90% by one detector and 85% by two or more detectors. The main challenges in the implementation of fire and gas detection systems include extreme environmental conditions, such as corrosion due to exposure to seawater and high temperatures, and false alarms that reach around 15%. Another obstacle is the lack of technical training for personnel in operating and maintaining the system.