Dampak Polusi Udara terhadap Kesehatan dan Kesiapan Pertahanan Negara di Provinsi DKI Jakarta

Air pollution in DKI Jakarta Province has reached alarming levels, threatening the health and quality of life of millions of residents and damaging the integrity of national defense preparedness. As the capital and center of Indonesian government, Jakarta plays an important role in the country's defense and security structure. This research was conducted to understand and evaluate the complex impacts of air pollution, which relate not only to public health but also to defense readiness. Through empirical data analysis, interviews with stakeholders, and the application of geospatial intelligence from satellite imagery, this research found various variables that correlate between air pollution, public health, and defense readiness. One of the main findings is that high levels of air pollution have a significant impact on public health conditions, which in turn can disrupt the country's defense readiness. Therefore, these results demonstrate the need for comprehensive and coordinated mitigation actions between various parties, including the provincial government, central government, and the military. These policies and actions should focus on protecting public health, preserving critical military infrastructure, and preparing the country to face additional challenges that may arise, such as climate change. In an increasingly interconnected and complex global context, this research highlights the urgency for a more holistic approach to addressing the problem of air pollution and its multifaceted impact

Disaster Risk Analysis and Modeling for Strategic Decision Making in DKI Jakarta

DKI Jakarta as an important national center, faces significant challenges in disaster risk management. This research aims to develop a model that can predict and reduce the impact of disasters in Jakarta, thereby making an important contribution to strengthening the city's resilience and the safety of its residents. This research uses an empirical quantitative approach, with main data from the Central Statistics Agency (BPS) and the National Disaster Management Agency (BNPB). The main focus is on correlation analysis and multiple linear regression analysis to explore the relationship between various variables such as infrastructure vulnerability, population density, and disaster frequency, as well as their combined impact on disaster risk. The research results show that Total Casualties, Inflation, Gini Index, and Number of Affected Villages are significantly correlated with disaster risk. Regression analysis reveals that Total Casualties have the most dominant influence. Economic stability and a localized approach to risk management were also found to be important factors. This research confirms that disaster risk management in Jakarta requires a comprehensive and integrated approach. Total Casualties as the dominant variable emphasizes the need for effective disaster response strategies. Meanwhile, economic and social factors such as inflation and the Gini Index also play a key role in determining vulnerability to disasters. A segmented approach based on the Number of Affected Villages is important for an efficient mitigation strategy

Youtube Analytics Channel Visualization Results Using Google Data Studio and Klipfolio

Demonstrate the power of Big Data Analytics using Google Analytics as a platform work flow. First open the YouTube channel, then start recording of the channel analytics is done here automatically by Google. This data is exported from YouTube Analytics to Google sheets and then is fed to Google Analytics. After analyzes the data, it is now integrated with Google Data Studio and Klipfolio. Google Data Studio makes use of AI (Artificial Intelligence) insights techniques that can generate artificial intelligence and prediction-based report graphs which can be analyzed by the end user. In the future, not only YouTube, but any Google products or Google service data can be fed to Google Analytics and integrated in Google Data Studio for artificial intelligence based on Big Data Analytics

Analysis of SPBE and SWCSF measurement instruments using Flesch Reading Ease for state security

The world has transitioned into a digital era where both individuals and governments require technology and the internet. The number of cybercrimes perpetrated online is impacted by the rising usage of computers and the internet. A measurement instrument that can stop cybercrime is necessary. The Six-Ware Cyber Security Framework (SWCSF) and the Electronic-Based Government System (SPBE) are two measurement tools that are expected to be able to stop cybercrime from happening in an agency or organization. But are all people able to use these two instruments? This research was conducted to answer this question by analyzing readability on the SPBE and SWCSF instruments using the Flesch Reading Ease method. The results show that the two instruments were extremely difficult for respondents of all grade levels to comprehend, with the exception of those at the university level or individuals who worked with computers, the internet, and other technologies

Modified Of Evaluating Shallow And Deep Neural Networks For Network Intrusion Detection Systems In Cyber Security

Abstract—Intrusion Detection Systems (IDS) have developed into a crucial layer in all contemporary Information and Communication Technology (ICT) systems as a result of a demand for cyber safety in real-world situations. IDS advises integrating Deep Neural Networks (DNN) because, among other things, it might be challenging to identify certain types of assaults and advanced cyberattacks are complex (DNNs). DNNs were employed in this study to anticipate Network Intrusion Detection System attacks (N-IDS). The network has been trained and benchmarked using the KDDCup-'99 dataset, and a DNN with a learning rate of 0.001 is used, running for 10 epochs for using the activation model experiment and 8 epochs for using the TensorFlow experiment. Keywords—Intrusion detection system, deep neural networks, machine learning, deep learnin