Jurnal Riset Teknologi Pencegahan Pencemaran Industri

Assessing the Challenges and Opportunities of Collaborative Governance in Multi-Stakeholder Policy Environments

Abdurrahman Kader, Ririn Fahri — Thu, 15 May 2025 00:00:00 +0000

This article explores the challenges and opportunities associated with collaborative governance in multi-stakeholder policy environments. Utilizing a qualitative approach, we conducted a comprehensive literature review to identify key themes and insights regarding collaborative governance practices. The findings reveal that while collaborative governance offers significant potential for enhancing stakeholder engagement and fostering innovative solutions to complex policy issues, it also presents notable challenges. These challenges include power imbalances among stakeholders, difficulties in communication and coordination, and the risk of diluted accountability. Conversely, the opportunities identified include the potential for shared knowledge creation, increased resource mobilization, and improved policy outcomes through collective decision-making. By synthesizing existing research, this study provides a nuanced understanding of the dynamics at play in collaborative governance frameworks and emphasizes the importance of fostering inclusive participation and effective communication strategies. The insights gained from this study can inform policymakers and practitioners seeking to enhance collaborative governance efforts in various sectors. Ultimately, this article contributes to the growing body of literature on collaborative governance by highlighting the intricate balance between challenges and opportunities, thereby offering practical recommendations for improving multi-stakeholder collaboration in policy environments

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

Sartika Sartika, Syarifah Aqla, Firman Firman, Yosa Megasukma — Fri, 23 May 2025 00:00:00 +0000

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, Dimas Frananta Simatupang — Thu, 15 May 2025 00:00:00 +0000

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.

Human-Centered Ergonomic Design in Industry 5.0: Enhancing Productivity and Worker Wellbeing

Idzani Muttaqin — Fri, 30 May 2025 00:00:00 +0000

Industry 5.0 represents a paradigm shift from technology-driven automation to a human-centered approach, emphasizing the integration of advanced technologies with human creativity and well-being at its core. This article employs a qualitative literature review to explore the role of ergonomic design as a foundational pillar within Industry 5.0, focusing on its impact on productivity and worker well-being. The review synthesizes findings from recent studies and bibliometric analyses, highlighting how ergonomic interventions—spanning physical, cognitive, and social domains—optimize work environments by aligning them with human capabilities and limitations. Differentiating from existing literature, this research emphasizes the importance of a holistic ergonomic approach in designing work environments that not only enhance safety and comfort but also foster collaboration between humans and intelligent machines. In Industry 5.0, ergonomic design reduces physical strain and cognitive overload, while simultaneously promoting innovation and job satisfaction. The integration of real-time monitoring technologies and adaptive workstations allows for dynamic adjustments, ensuring sustained worker health and organizational efficiency. However, despite these advancements, challenges remain in the widespread adoption of ergonomic principles, particularly in harmonizing technological innovation with the holistic needs of workers. The findings underscore the necessity of a comprehensive, multidisciplinary approach—bridging policy, technology, and human factors—to fully realize the potential of human-centered ergonomic design in Industry 5.0. This study offers actionable insights for researchers and practitioners aiming to create productive, sustainable, and worker-friendly industrial ecosystems, with an added focus on how ergonomic design in Industry 5.0 fosters more adaptive and dynamic human-technology interactions compared to previous approaches.

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

Titin Agustin Nengsih, Indrawata Wardhana, M. Nazori M. Nazori Madjid — Wed, 28 May 2025 00:00:00 +0000

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.

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

Novan Akhiriyanto, Setiawan Listianto, Naufal Basmana — Wed, 28 May 2025 00:00:00 +0000

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.