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Time Performance Analysis Using Earned Value on the Construction of the Aranaway Kiram Bridge in Banjar Baru City
No full textWhen implementing a project in the field, it is fairly uncommon for numerous projects to be delayed or even halted. This also has an impact on project cost escalation, resulting in project losses. Earned value analysis can help manage construction projects efficiently and effectively. The results value analysis was done to estimate the extent to which the project was carried out according to the work plan. This assessment was performed on the Aranaway Kiram Bridge Construction project in Banjarbaru City. The project was aimed to determine weekly time performance and project completion time. The method employed was earned value analysis. According to the results of the analysis method (earned value analysis), the results from the 3rd to the 18th week experienced delays, as evidenced by the performance of the Schedule Variance (SV) which was Negative (-) and the Schedule Performance Index (SPI) which was less than 1, but work accelerated in the 19th week of the Banjarbaru City Aranaway Kiram Bridge Construction Project, as evidenced by the SV performance which was Positive (+) and SPI greater than 1. The research results estimated the project's completion time as 253 days
Secure Cloud Storage with a Sanitizable Access Control System Again Malicious Data Publisher
No full textA novel encryption mechanism known as Ciphertext Policy of topic has been developed. Attribute Based Encryption (CPABE) was developed as an alternative to password-based systems to address the challenges associated with secure data sharing, where users are required to know the password for each file they need. This research work proposes a CPABE-based approach where just one secret key is required per user. A strategy has been implemented to establish precise document access control in a typical academic environment. Only users with the specified attribute similar to public key cryptography, can be encrypted many times to satisfy the access structure defined and allowing different users to decode the contents for secret retrieval. CP-ABE only necessitates a single encryption for each document due to its encoding. The idea of CPABE, which stands for Ciphertext-Policy Attribute-Based Encryption and analyses it in relation to other types of ABE, which stands for attribute-based encryption is developed here
Optimizing Age Estimation in Facial Images with Advanced Multi-Class Classification Techniques
No full textAutomatic age and gender prediction from facial images is increasingly crucial for applications in
security, marketing, and social media. Existing systems often face challenges related to accuracy,
demographic generalization, and bias. This study addresses these issues by developing a deep
learning-based system utilizing Convolutional Neural Networks (CNNs) for enhanced
classification of age and gender. The key research gaps include limited accuracy, insufficient
handling of diverse data, and model bias. The proposed approach encompasses data acquisition,
preprocessing, and the design of a CNN architecture within a multi-class classification framework.
Various CNN models are evaluated, incorporating transfer learning, hyperparameter optimization,
and regularization techniques to improve performance. The system's effectiveness is assessed
through metrics such as classification accuracy, precision, recall, and robustness across different
demographic groups. Results indicate significant advancements in prediction accuracy and model
generalization compared to existing methods. The technology holds practical applications in
security, personalized marketing, and social networking. Challenges such as model bias and the
need for diverse datasets are addressed, with future research aimed at further refining the model
and expanding its applicability. This work highlights the substantial improvements deep learning
offers to facial recognition technologies
A Survey on Balancing Data Loss Prevention (DLP) with User Privacy in a Data-Driven World
No full textNowadays, data breaches are a major concern for industries and governments, Data Loss
Prevention (DLP) solutions have become essential tools to protect sensitive information and
uphold data integrity. This study examines the ever-changing field of DLP methods, highlighting
the importance of maintaining a balance between protecting data and respecting user privacy in
the midst of widespread data circulation. The study discusses the difficult obstacles organizations
encounter when merging DLP with privacy protection through analyzing real-life examples,
research findings, laws, and technology developments. The results offer useful suggestions for
matching DLP projects with privacy principles to improve organizational ability to withstand data
breaches while also protecting individual privacy in a connected digital environment
The Impact of Treadmill Retro Walking on Hamstring Flexibility and Speed in Distance Runners
No full textBackground: Distance runners frequently encounter constraints in terms of hamstring flexibility
and running speed improvement. It is possible that existing training approaches may not
sufficiently address these concerns. Analyzing the effect of retro-walking on a treadmill may
provide valuable insights into an innovative strategy for enhancing hamstring flexibility and speed
in distance runners. Objective: The study aims to investigate the effect of retro-walking on a
treadmill on hamstring flexibility and speed in distance runners. Methodology: A total of 30 male
distance runners were chosen for this quasi-experimental study. The hamstring flexibility and
speed were evaluated by conducting a sit-and-reach test and a 35-meter sprint test before and after
the intervention. The research intervention was administered 3 days a week over a duration of 12
weeks. The pre- and post-mean difference within the group was analyzed using a paired t-test.
Result: The result showed that there was a significant improvement (mean±SD, p<0.05) in
hamstring flexibility and speed within the group. Conclusion: In conclusion, incorporating retro
walk into the training protocol may improve the hamstring flexibility and speed in distance
runners
Application of Virtual Reality Technology Combining Sustainable Development and History Education
No full textUsing VR to improve students' historical and environmental education is an important issue in the
current educational and environmental challenges. This study explores the application of virtual
reality technology in history education, especially how to educate students about environmental
changes in historical events through immersive experiences. This study evaluates the impact of
VR technology on enhancing student environmental awareness and promoting sustainable
behavior by simulating environmental scenarios in different historical periods. Preliminary results
showed that through this innovative teaching method, students not only improved their
understanding of historical environmental events, but also showed positive changes in
environmental behavior. The findings of this study provide valuable insights into the application
of emerging technologies in education, particularly in promoting sustainability
The Role of Green Society in Society 5.0: Tango Diamond in a Collective Intelligence (Hybrid) Ecosystem Founded on Human-Centricity and Sustainability
No full textThe research composition sets out to extend and deepen the extant theoretical fund with a holistic
perspective of the role of green society within the framework of Society 5.0. The purpose of the
article is to move forward with tango diamond in a Collective Intelligence (Hybrid) Ecosystem
concept and model, founded on human-centricity, sustainability and long-term prosperity and wellbeing.
The study opted for a research method that is grounded on a systematic literature review
approach, by utilizing a five-stage review process, taking into account the conceptual nature of the
article. The research results signify that the role of green society goes beyond the traditional
understanding of society and requires a notable transformation towards the desired effects of
human-centric society doctrines. The innovative green society within Society 5.0 is substantial and
multifaceted. The holistic sight of the contribution of green society goals and principles of a
human-centric society is systematically depicted in the study, by pointing out that the framework
is far more than a pattern and goes beyond conventional prospects, towards the development of a
highly smart and healthy society and world. The research can make significant contributions to
advancing understanding of green society, and the transition to a more equitable, healthy,
sustainable and resilient future. The work may influence academic discourse, policy debates, and
practical initiatives aimed at promoting sustainability and addressing global "green" and humanity
challenges. The paper may contribute to all stakeholders interested in developing and
implementing a healthy green society in a healthy human-centric society by approaching inclusive
collaboration in a connected world and encouraging open innovation in managing change
Revolutionizing Construction through Enhanced Project Management and Sustainability with Industry 4.0 Technologies
No full textWith limited resources and a focus on sustainability, building industry staff face increasing
pressure to innovate. This research explores best practices in construction to understand how
Industry 4.0 technologies (AI, Robotics, AR/VR, Digital Twins) can transform project
management. Through a bibliometric study and literature review, it identifies current technology
adoption and barriers. The solution is a program for implementing these technologies to streamline
operations, reduce waste, and boost participation. Key to unlocking Industry 4.0's benefits lies in
addressing challenges in training, investment, and interoperability, supported by our findings
Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost
No full textAir quality prediction, particularly in estimating PM10 particle concentration, is a
significant challenge in major cities like Jakarta, which experience high levels of air pollution.
This study aims to develop an air quality prediction model using an innovative stacking
framework that combines several machine learning algorithms, namely ConvLSTM, CatBoost,
SVR, and XGBoost. The methodology employed in this research is an experimental approach,
where each model is trained and tested individually before being integrated into the stacking
framework. The dataset used was sourced from the Kaggle platform, containing historical air
quality data in Jakarta. Performance evaluation was conducted by measuring the Root Mean
Squared Error (RMSE) for each model. The results of the study showed that the ConvLSTM
model produced an RMSE of 13.5168, CatBoost with an RMSE of 13.4113, and SVR with an
RMSE of 14.2725. To improve prediction accuracy, the researchers employed a stacking
approach of the four models (ConvLSTM, CatBoost, SVR, and XGBoost), which yielded a
much lower RMSE of 0.8093. Thus, this stacking framework has proven to significantly
enhance air quality prediction performance, particularly in predicting PM10 concentrations in
Jakarta
Poverty Classification in Indonesia Using BiGRU, BPNN, and Stacking AdaBoost Frameworks
No full textThis research addresses the persistent global challenge of poverty, with a specific focus on
Indonesia, a nation with a population exceeding 270 million. The primary objective is to enhance
the precision and reliability of poverty classification using advanced machine learning
technologies. We employed a combination of Bidirectional Gated Recurrent Unit (BiGRU),
Backpropagation Neural Network (BPNN), and stacking techniques with AdaBoost to develop an
innovative classification model. The methodology involved training each technique separately and
then integrating them into a stacked model to leverage their individual strengths. The results were
promising, demonstrating a substantial improvement in model performance with precision, recall,
and F1 scores reaching as high as 0.98, and an overall accuracy of 98.06%. The use of visual
analytics, including pie charts and bar graphs, provided a comprehensive distribution analysis of
poverty levels, confirming the balanced nature of the dataset. These findings underscore the critical
role of machine learning in formulating effective policies for poverty alleviation and suggest that
integrating multiple machine learning algorithm can significantly enhance decision-making
processes. The novelty of this research lies in the successful application of a stacked machine
learning model combining BiGRU, BPNN, and AdaBoost, which establishes a new benchmark for
poverty classification in large-scale social datasets. This study not only contributes to the academic
discourse but also paves the way for practical implementations that can drive inclusive and
sustainable development