Yayasan Riset dan Pengembangan Intelektual (YRPI) Journal
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Optimalisasi Pengelolaan Dan Pemanfaatan Tempat Pengolahan Sampah Reduce-Reuse-Recycle (TPS3R) Desa Rajik, Kec. Simpangrimba, Kabupaten Bangka Selatan
No full textThis community service program aims to optimize the management and utilization of the Reduce-Reuse-Recycle Waste Processing Site (TPS3R) in Rajik Village, Simpang Rimba District, South Bangka Regency. The main problems identified are the low operational effectiveness of the TPS3R facility and the limited community participation in sorting and managing waste. This condition has led to the accumulation of household waste, the decline of environmental quality, and the absence of a well-structured waste management system at the village level. The program was carried out through a scientific study and participatory action using a mixed-methods approach, combining quantitative surveys and qualitative interviews to obtain a comprehensive understanding of waste management conditions in Rajik Village. Through this approach, socialization, training, and mediation activities were conducted, involving community members, village officials, and TPS3R managers. The results indicate a significant improvement in community awareness and participation in waste management following a series of outreach and training sessions. Residents have begun to understand the importance of the 3R-based waste management system and have shown commitment to practicing waste sorting and recycling at the household level. Moreover, the program encouraged the development of creative economic awareness based on waste management, where the community started to recognize the economic potential of recyclable products. Overall, this program successfully strengthened collaboration among the village government, TPS3R management, and the local community in realizing a self-reliant, participatory, and sustainable waste management system, while making a tangible contribution to building a cleaner and more productive village environment
Blockchain Framework for Secure IoT Operations in Military Applications: Integrating LoRaWAN and Helium Network
No full textThe traditional IoT is typically based on centralized systems that are susceptible to multiple cyberattacks and a single point of failure. Modern industries regularly embrace block chain technology due to its decentralization and security. This study suggests a block chain-based system that guarantees reliable and secure operations. They suggest a secure compact block chain for handling access to precious information through instruments and controllers. Based on realistic military applications, the current investigation makes evidence for the benefits of merging LoRaWAN and Helium Network technology, and also demonstrates how deliberate research and analysis can bridge the block chain gap for military cyber defense. To improve the proposed system's computing efficiency, the block chain network has devised a rapid and power-saving decision technique for proof of authentication. The suggested framework for smart industrial environments has survived extensive testing and study to be sustainable. Use the suggested configuration to convert a standard processing system into an intelligent and secure industrial platform. This article aims towards assessing the practicality of Proof of Authority in the block chains network as a consensus algorithm. There are numerous techniques available for creating a consensus among the nodes
A Comprehensive Review of Deep Learning Techniques for Intrusion Detection in the Internet of Medical Things
No full textThe work revisits the security issues of Internet of Medical Things (IoMT) platforms and provides a list of deep learning models used for intrusion detection. The study fills the salient gap in early detection of actual IoMT system intrusions for enhanced medical device and data security. A wide-ranging and systematic investigation of deep learning models, such as CNNs, LSTMs, and hybrid ones (GNNs and BiLSTMs) recently introduced was carried out. These were then analyzed against well-known benchmark datasets, such as ToN-IoT and IoT-Healthcare Security and WUSTL-EHMS-2020, to consider the quality of their detection work on cybersecurity threats for IoMT systems. The results indicated high accuracy in cyber threat detection, reaching even 100% accuracy. But the challenges are still how to decrease false positives and improve the real-time performance of the model on robustness and generalization when making real-world applications. The research is literature-based and aimed to provide some further updates on a secure IoMT framework by identifying recent studies in the security of the IoMT ecosystem and shedding light on future work using hybrid methods, blockchain technology, or federated learning approaches that can contribute to the detection of IDSs. And all can help pave the way for a more secure, privacy-protecting IoMT that safeguards extremely sensitive medical data. The research also enhances the model: utilizing 15+ deep-learning models to propose an IoMT-resistant architecture. This can promote participation in the theoretical research and practical security protocols in the IoMT context, thus drawing attention and comprehension from researchers and practitioners to enhance security protocols
Enhanced CSMA/CA Protocol-Based Optimal Robust Dynamic Query-Driven Clustering for Improved QoS in Heterogeneous WSNs
No full textHeterogeneous Wireless Sensor Networks (HWSN) are basically decentralized and distributed systems that playing a crucial role in numerous Internet of Things (IoT) applications, enabling efficient monitoring and data collection. However, these networks often suffer from high latency, routing overheads, and energy consumption. To meet these challenges effectively, This article proposes an enhanced CSMA/CA protocol based on an Optimal Robust Dynamic Query-Driven Clustering Protocol (ECODQC) model. The enhanced model includes two key components: the improved CSMA/CA protocol, which reduces network collisions, lowering delay and overhead during communication, and the Optimal Robust Dynamic Query-Driven Clustering (ODQC) protocol, which efficiently reduces energy consumption among sensors. In the first phase, the modified CSMA/CA protocol focuses on analyzing communication delays, defining dynamic data transmission, and evaluating data delivery beyond predefined times. In the second phase, the ODQC protocol addresses optimal load balancing and the dynamic process of cluster head selection, aiming to reduce energy consumption during sensor communication. The proposed techniques demonstrate superiority over conventional protocols and are recommended for enhancing the overall quality of service in decentralized, distributed HWSN-based IoT networks. The ECODQC model is compared against existing methods using the NS2 simulation platform in two scenarios: the varying numbers of nodes and varying speeds. The performance parameters of this proposed model are analyzed in terms of energy efficiency, cluster head efficiency, data success rate, computational delay, and node throughput. The Results demonstrate that ECODQC proves to be superior compared to existing techniques in terms of energy efficiency of 432.23 J, low latency of 85.23 ms, and increased throughput of 813.77 Kbits/s. With these observations, the possibility of using ECODQC with a high level of applicability in real-time IoT scenarios is eviden
CNN-Based SIBI Sign Language Recognition Alphabet: Exploring the Impact of Hardware on Model Training
No full textThe recognition of Sign Language Alphabets (SLA) plays a vital role in human-computer interaction, especially for individuals with auditory disabilities. This study aims to evaluate the impact of different hardware configurations—specifically CPU, GPU, and memory setups—on the training efficiency and recognition performance of a Convolutional Neural Network (CNN)-based model for SLA using the SIBI dataset. The novelty of this research lies in its focus on hardware-aware deep learning optimization for Indonesian sign language (SIBI), an underexplored area. The model was trained on 3,468 labeled hand gesture images representing 24 SIBI alphabet signs. Experiments were conducted on CPU (Intel Xeon 2.00 GHz) and GPU (Nvidia Tesla T4) platforms using a consistent CNN architecture. The training time was significantly reduced by 45.5%, from 1 hour 39 minutes to just 54 minutes, while the accuracy remained consistent at 96.7%, showing no significant change between the two setups. These results demonstrate the significance of parallel processing and memory bandwidth in enhancing model convergence and generalization. The findings are relevant for real-time SLA deployment with hardware constraints on embedded or mobile platforms. Overall, the study underscores the importance of hardware optimization in accelerating CNN training and improving performance in sign language recognition systems
Generating Accurate Topographic Map by Integrating Drone Imagery and GNSS Data
No full textWhen operating in big or hard-to-reach areas, traditional topographic survey methods can be costly, difficult to organize, and time-consuming. Some of these technologies use total stations and GPS on the ground and aerial photogrammetry done by planes or helicopters. We need a better and cheaper approach to collect geographic data fast. This article discusses employing unmanned aerial vehicles (UAVs) for topographic surveying, mapping, and updating data as one option. A DJI Mavic 2 Pro quadcopter drone with a 20-megapixel digital camera took photographs of the Wasit University campus from 125 meters above the ground. The pictures indicated a space of around 0.43 km², with 80% of the front and 70% of the sides overlapping. The research area was turned into an orthomosaic by Agisoft PhotoScan Professional. This was then loaded into ArcMap so that features may be taken out. By comparing the coordinates of fourteen Ground Control Points (GCPs) that we got using the Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) mechanism, we were able to get a reference positional precision of 0.050 m RMSE. The results of this study demonstrate that geospatial data obtained from UAVs, when augmented by GCPs, can produce and update comprehensive maps with accuracy comparable to RTK GNSS and Total Station methodologies. Many individuals use these methods for surveys of land, buildings, and engineering
Swarm Intelligence Optimisation Vs Deep Learning: Energy-Aware Strategy for Disaster Communication Networks
No full textIn disaster-prone environments, communication networks must sustain operation under severe constraints such as limited energy, damaged infrastructure, and uncertain topology. This study compares Deep Learning (DL) and Swarm Intelligence Optimisation (SIO) as energy-aware strategies for disaster communication. While DL excels in data-rich prediction and situational analysis, its reliance on high-performance hardware and stable connectivity restricts its feasibility during real-time emergencies. In contrast, SIO provides decentralised coordination, lightweight computation, and adaptive routing, making it better suited to infrastructure-independent device-to-device (D2D) networks when central control collapses. A comparative conceptual framework was developed to evaluate both paradigms across five criteria: energy efficiency, adaptability, computational demand, response time, and scalability, based on recent literature between 2023 and 2025. Findings show that SIO demonstrates superior suitability for energy-limited and time-critical operations, while DL remains valuable for pre-disaster prediction and post-event analysis. Hybrid DL–SIO frameworks bridge both paradigms, enabling predictive–adaptive synergy across the disaster lifecycle. The study contributes a context-aware guideline for algorithm selection, shifting the focus from technology-centric performance toward environment-centric deployment in future energy-efficient, resilient, and adaptive disaster communication systems
Career Development as a Mediator of the Relationship Between Education Level, Competence, and Employee Performance
No full textThis study aims to determine the effect of education level and competence on employee performance with career development as an intervening variable at Perumda Air Minum Tirta Hita Buleleng. This study uses a quantitative approach, and the sample used is 77 employees. The sample collection method uses proportional random sampling techniques. Data collected used questionnaires. The data analysis technique used SEM PLS. The results showed that education level affected career development, competence affected career development, education level affected employee performance, competence affected employee performance, career development affected employee performance, education level affected employee performance through career development, and competence affected employee performance through career development at Perumda Air Minum Tirta Hita Buleleng  
Resolving Conflicts, Sustaining Livelihoods: A Fisheries Resource Management Model for Enhancing Fishermen’s Welfare
No full textThis study aims to analyse the effect of capture fisheries resources and conflict resolution on fishermen's welfare in the coastal area of Sibolga City, as well as to explore the moderating role of conflict resolution in the relationship between capture fisheries resources and fishermen's welfare. The study used a quantitative approach with data collection through questionnaires administered to active fishermen in coastal areas, and analysed using linear regression and moderation tests. The results showed that capture fisheries resources and conflict resolution had a significant effect on fishermen's welfare, but conflict resolution did not act as a moderating variable between capture fisheries resources and fishermen's welfare. These findings emphasise the importance of effective fisheries resource management and conflict resolution strategies as efforts to improve fishermen's welfare. This study has implications for the government and related institutions to strengthen fisheries resource management, improve conflict resolution capacity, and design sustainable development programmes that support the welfare of coastal communities.
 
The Moderating Effect of Firm Size on The Influence of Ownership Structure on Earnings Management Practices
No full textEarnings management refers to the choice of accounting policies or real actions taken by manager to influence earnings in order to achieve specific reported earnings numbers. This study aims to examine the effect of ownership structure on earnings management practices and to analyze the role of firm size as a moderating variable. The theory employed in this study is agency theory. The research was conducted on all manufacturing companies in the consumer goods sector listed on the Indonesia Stock Exchange for the period 2021-2023. The sample was selected using a purposive sampling method, resulting in 52 companies that met the criteria, with a total of 156 observations. The data analysis technique used in this study is moderated regression analysis. The results show that institutional ownership, foreign ownership, and public ownership have negative effects on earnings management. Firm size weakens the negative effect of institutional ownership and public ownership on earnings management. However, firm size does not moderate the relationship between foreign ownership and earnings management. The findings of this study highlight the importance of ownership structure as an effective monitoring mechanism especially for larger firms. These results can serve as a consideration for stakeholders in enhancing corporate governance and monitoring effectiveness