This study aims to investigate the effect of the percentage of reinforcement and hot compaction pressure on the density value of Aluminum Matrix Composite brake pads, reinforced with Boiler-Fly-Ash and silica sand. The Powder Metallurgy method was used in this study, with variations in the weight fraction of reinforcement being 6%, 10%, and 14%. Mixing of the matrix powder and reinforcement was performed using a Ball Mill Machine, with a Ball Powder weight Ratio of 10:1, within 6 hours of mechanical alloying at a speed of 90 rpm. The moldingprocess used a hot compaction system in the form of a Hydraulic Jack machine with an upper press and a lower press, with a hot pressing temperature of 350 ºC, a holding time of 10 minutes, at a pressure variation of 5200 Psi, 5600 Psi, and 6000 Psi. The sintering process used a temperature of 600 ºC at a holding temperature of 10 minutes. The density test of the specimen uses the ASTM B962-17 standard with the Archimedes theory approach, while the hardness test uses the Brinnel Hardness, referring to ASTM E110-14. The result, the highest average density value is 2.10 g/cm³ with a reinforcement percentage variation of 10%, while the lowest density value is 1.90 g/cm³ with a reinforcement percentage of 14%. The highest hardness value is 42.93 HB with a percentage of 10%, while the lowest hardness value is 41.4 HB with a reinforcement percentage of 14% compaction of 6000 Psi.

The rotary dryer is a machine designed to simplify the cocopeat drying process. The current process still relies on sunlight; this drying process has the drawback of longer drying times and uncontrolled moisture content. The maximum moisture content of thecocopeat must be 15%. This research method is an experiment using several parameters: a drum speed of 6 rpm, heating temperatures of 600°C, 900°C, and 1300°C, with varying drying times of 120 and 150 minutes. The drying process at a temperature of 600 °C resulted ina decrease in humidity efficiency of up to 56.7% with a water content value of 23.8% in 150 minutes and a mass of 3.7 kg. Meanwhile, for drying at a temperature of 1300 °C, the best reduction in efficiency was obtained compared to the previous temperature, a decrease in water content of up to 84% with a water content value of 8.55 and a mass of 2.7 kg using a time of 150 minutes

Heart disease is the leading cause of death globally and is often not detected early due to limited awareness and the high cost of medical diagnosis. This study aims to develop an accurate and efficient prediction model for heart disease using the Linear Discriminant Analysis (LDA)algorithm. The dataset, obtained from Kaggle, contains 1,024 patient records with 14 clinical attributes, including age, blood pressure, cholesterol, and ECG results. The preprocessing steps include handling outliers, duplicates, class imbalance using SMOTE, and featurestandardization. The model was evaluated using cross-validation and learning curve analysis. Results show that the optimized LDA model, tuned with GridSearchCV, achieved an accuracy of 82.54%, a recall of 88.91%,a precision of 79.03%, and an F1-scoreof 83.54%. The model demonstrates balanced and stable performance, although some misclassification in the positive class remains. This study highlights LDA as a promising method for the early detection of heart disease based on structured clinical data.

Tire pressure inspection is a crucial procedure at PT HMMI based on the Part Inspection Standard (PIS), with a recommended pressure of 105-125 Psi for buses. Currently, inspections are still performed manually, deemed inefficient and prone to human error. To addressthis and meet inspection standards, this research aims to develop an Internet of Things (IoT)-based bus tire pressure detection system. The Research and Development (R&D) method was applied to design and build this system. The developed system utilizes a pressure transmitter sensor integrated with an ESP32 microcontroller, equipped with LCD, LED, and buzzer outputs. Pressure measurement data is transmitted in real-time and stored in Google Spreadsheet for paperless documentation. Functional testing of the system on buses demonstrated the sensor's detection capability within 2-3 seconds with all outputs functioning optimally. Accuracy test results showed excellent performance, reaching 99.44% with an average error of only 0.56% after calibration with 30 pressure parameters. This system successfully proved its capability as an effective solution for automatically and accurately monitoring bus tire pressure, supporting the achievement of PIS standards and enhancing the efficiency of inspection processes in the automotive industry.

The bypass system in oil filters plays a crucial role in maintaining engine cleanliness and performance by allowing oil to flow through the filter when the pressure exceeds set limits. A critical component of this system is the coil spring that controls the bypass valve. In this study, an experimental approach was applied to reduce material cost while preserving performance. We redesigned the spring from four coils of 3.5 mm diameter to three coils of 3 mm diameter, using the same standard hard steel wireSW‑C. The redesigned springs were subjected to a standard impulse test of 250,000 cycles under 7 kgf/cm² pressure and a loading test with deflections from 1 to 10 mm at pressures up to 11 kgf. Results show that the new three‑coil SW‑C spring meets all performance criteria: impulse life and load‑deflection characteristics fall within standard tolerances. A direct comparison with the original design demonstrates negligible differences in functional behavior, confirming that material usage and costs can be reduced without sacrificing reliability. These findings offer valuable guidance for the cost‑efficient production of oil filter components in automotive engineering.

Indonesia has a large solar energy potential, with an average radiation of 4.5-4.8 kWh/m² every day. Politeknik Negeri Cilacap (PNC) can take advantage of this opportunity to develop a Solar Power Plant. This study examines the possibility of building an on-grid rooftop solar power plant in the Department of Electrical and Mechatronics Engineering. This study uses Homer to simulate the solar power plant design. The parameters discussed are total energy production (kWh/year), renewable fraction, net present cost/NPC (Rp) which is all costs used in construction, both in installation and operation of the solar power plant and Cost of energy/COE (Rp/kWh) which is the cost incurred to produce electrical energy per 1 kWh. The results of the study include an average solar energy at PNC of 4.66 kWh/m²/day. Based on Homer's simulation results, the total energy generated by the solar power plant is 149,056 kWh/year, with 68,599 kWh/year from photovoltaic and 80,457 kWh/year from the PLN grid. The renewable fraction is 44.8%. The NPC is Rp 1,880,000,000, and the CoE is Rp 1,000/kWh

This research successfully developed a prototypeof an IoT-based LPG gas leak detection system using an MQ-2 sensor and NodeMCU ESP8266 with digital output. The system works in binary to detect the presence or absence of gas and send notifications via Telegram. The research method includes hardware design, microcontroller programming using Arduino IDE, and system testing through 10simulated gas leaks at a distance of 5 cm. The test results show that the prototypefunctions very well, achieving perfect detection accuracy (100%) in all trials. The system canrespond to gas leaks quickly, having an average response time of 1.6 seconds and sending Telegram notifications in less than 3 seconds. Importantly, the system did not give false alarmsunder normal conditions. The prototypefeatures three layers of alerts: a buzzersound, a red LED indicator,and a Telegram notification that can be accessed from anywhere. With a relatively affordable production cost, the system offers an effective, reliable,and affordable gas leak early detection solution for household safety.

Solar energy is a new renewable energy (EBT) that can be used as an alternative energy source for electricity generation to replace fossil fuels or supplies from the National Electricity Company (PLN). One of its uses can be applied in everyday life in household appliances, namely, coconut grater machines. Coconut grater machines used in the market still use fossil fuels to crush coconut meat, so solar energy is implemented as an alternative energy to operate the coconut grater machine. The use of solar panels in this study is highly dependent on sunlight exposure. In addition, the tilt position of the solar panel can also determine the power generated by the solar panel. The tilt position of the solar panel can be manually adjusted according to the direction of sunlight at certain times. Around midday, sunlight can be captured optimally. At that time, the accumulator/battery will quickly charge, and the coconut grater machine can be used at low or high speeds. The purpose of this study is to implement a PWM (Pulse Width Modulation) system-based control as a motor speed regulator on a coconut grater machine. PWM technology is installed to obtain optimal rotation results and has the potential to save electrical energy. The research results showed that the installed solar panels could produce an average of 4.86 watts of electrical power at 8:00 a.m. WIB and a maximum of 5 watts of electrical power at 12:00 p.m. WIB. Under no-load operating conditions, the current was 0.38 A and the motor speed was 3,724 Rpm. When the engine was tested under load, the speed was 2,926 Rpm.

In 2022, a total of139,258 traffic accidents occurred in Indonesia, encompassing all types of incidents, from minor to fatal. The high number of accidents, particularly those caused by speeding and unsafe following distances, highlights the need for a system capable of automatically monitoring and intervening in driver behavior. This study aims to develop an IoT-based control and monitoring system that not only detects violations such as overspeeding and insufficient following distance but also provides direct intervention in vehicle speed. The system utilizes an Arduino Mega and ESP32, equipped with GPS and LiDAR sensors, along with a DAC output to limit the accelerator pedal voltage when repeated violations are detected. Testing was conducted to evaluate sensor accuracy, IoT performance, and the effectiveness of speed intervention. The results showed a 100% success rate in five speed intervention tests, good sensor accuracy, and IoT notifications successfully delivered with an average delay of 5.9 seconds. The system proved to be effective and feasible as a technology-based solution to enhance driving safety.