Jurnal Riset Teknologi Pencegahan Pencemaran Industri

Implementation of Integrating PV System Production Forecasting Using Recurrent Neural Networks in Local Weather Station Prototype

Novan Akhiriyanto — 2025-05-28

This study explores the crucial role of weather stations in measuring, collecting, and reporting weather data, as well as the implementation of modern technologies such as Long Range (LoRa) radio wave modulation technology for real-time data monitoring. Equipped with components like temperature, humidity, solar radiation, and wind sensors, the weather station ensures accurate and efficient data collection. Testing of LoRa technology at the PEM Akamigas Campus demonstrated an effective range of approximately ±85 meters, ensuring optimal connectivity between the Subroto Building and the Energy Laboratory Building. Data consistency from the Message Queue Telemetry (MQTT) protocol server and Haiwell Human-Machine Interface (HMI) confirms the reliability of weather monitoring. Additionally, this research focuses on weather and energy production predictions for the PV system at the Subroto Building, using an Recurrent Neural Network (RNN) deep learning model to enhance the accuracy of solar panel energy production forecasts. Data evaluation from April 1, 2024, to April 22, 2024, highlights the potential. Based on the real-time sensor data installed in the field on a combination of 3 series solar panels, resulted in production forecasting with Root Mean Square Error (RMSE) values of approximately 4.9965 for voltage, and 0.0081 for current. This indicates fairly satisfactory results. For power testing, the RMSE results are still unsatisfactory, highlighting an opportunity for future model improvements. The combination of LoRa technology and the RNN model is expected to provide valuable insights into reliable weather monitoring and energy production at the PEM Akamigas Campus, with improvements to the model parameters for power data, which is inherently derived from the multiplication of voltage and current parameters.

Utilization of Industrial Waste (Bottom Ash) as an Alternative Material in Paving Block Manufacturing

Sartika Sartika — 2025-05-23

Coal is a heterogeneous, combustible material composed of various components with differing properties. The combustion process of coal in coal-fired power plants (CFPPs) generates waste in the form of bottom ash residue. If not properly utilized, bottom ash has The ability to trigger adverse environmental impacts. One alternative for its utilization is as a mixture component in the production of paving blocks. This study aims to evaluate the water absorption, compressive strength, and quality classification of paving blocks with the addition of bottom ash, where the test results will be compared with the Indonesian National Standard (SNI) to determine conformity to quality standards. According to the study, it can be seen that increasing the percentage of bottom ash leads to a higher water absorption rate and a decrease in compressive strength. This trend is attributed to the reduction in bulk density of the paving blocks as the proportion of bottom ash increases. Based on the test results, paving blocks without bottom ash (Sample A) fall into Class C; mixtures with 10% and 20% bottom ash (Samples B and C) fall into Class B; the 30% mixture (Sample D) belongs to Class C; and the 40% mixture (Sample E) is categorized as Class D. All composition variations meet the quality classification criteria stipulated in the applicable Indonesian National Standard (SNI). Based on compressive strength and water absorption parameters, the optimal bottom ash composition ranges between 10% and 20%.

Analysis of Neutron Radiation Absorption Capacity of Coir Fiber Composite Board as A Neutron Radiation Shield

Evi Christiani Sitepu — 2025-05-15

Research has been conducted on the radiation shielding capability of coir fiber composite boards to determine the extent of neutron radiation absorption as it passes through the created radiation shield. This study aims to ascertain whether coir fiber can be used as a filler in the production of radiation shields. Initial analysis was conducted using SEM-EDX, FTIR, and XRD testing. The results indicated that the primary component of coir fiber is carbon at 70.68%, which is structured in chemical bonds of cellulose, hemicellulose, and lignin. Additionally, coir fiber retains a crystalline region observed at the peak of 2θ=22.4°, with a crystallinity degree of 35.46%, suggesting its potential for neutron radiation absorption. After fabricating the composite board, it was tested using the Neutron Activation Analysis method to evaluate its neutron radiation absorption capability. The analysis results showed that the absorption capacity of the composite board at a fiber mass fraction of 2.0 g ranged from 59.4 to 97.8%; at 3.0 g from 64.3 to 98.3%; and at 4.0 g from 73.5 to 99.3%. The linear attenuation coefficients (µ) for each coir fiber fraction were found to be 3.84; 4.13; and 4.80/cm, with half-value layers of 0.18; 0.17; and 0.14 cm, respectively, demonstrating that coir fiber can be utilized as a filler for neutron radiation shielding.

Addressing Missing Data in Environmental Technologies: Optimizing Air Quality Monitoring with Random Forest and MissForest

Titin Agustin Nengsih — 2025-05-28

Air quality monitoring often encounters missing data issues due to technical glitches, equipment malfunctions, or other causes. This study employs PM2.5 and PM10 datasets from station 6, calculating multiple weighted probabilities for imputation. With missing values introduced at rates of 10, 40, and 70 percents through different amputation methods, the Random Forest and missForest techniques are utilized for imputation. Notably, missForest consistently outperforms Random Forest across all scenarios, yielding accuracy exceeding 96% even with high missing data levels. MissForest achieves remarkable accuracy above 96% for PM2.5 and PM10 across left, middle, and right multiple weight probabilities amputations. Overall, missForest attains the highest accuracy (over 97%) for Air Quality Index at lower and middle missing value proportions.