Amidst the rapid advancements in the digital landscape, the convergence of digitization and cyber threats presents new challenges for organizational security. This article presents a comprehensive framework that aims to shape the future of cyber security. This framework responds to the complexities of modern cyber threats and provides guidance to organizations to enhance their resilience. The primary focus lies in the integration of capabilities with resilience. By combining these elements into cyber security practices, organizations can improve their ability to predict, mitigate, respond to, and recover from cyber disasters. This article emphasizes the importance of organizational leadership, accountability, and innovation in achieving cyber resilience. As cyber threat challenges continue to evolve, this framework offers strategic guidance to address the intricate dynamics between digitization and cyber security, moving towards a safer and more robust digital environment in the future.

This paper proposes the design of real-time weather monitoring system based on a mobile application using Automatic Weather Station (AWS). The system connects to the AWS equipped with several sensors for collecting data and storing the data to the web server. Data from weather sensor is taken from the AWS-Davis Instrument using the WeatherLink software. The data is transmitted through the data logger using serial communication, uploaded via FTP and stored on a webserver. The Android application reads the files and displays the information provided by the web server in real-time. The system has successfully show real-time monitoring of weather through the mobile application with a flexibility in the parameters and the need of user interface (UI) design compared to the other solution.

Human-Robot Interaction (HRI) requires media for communication which can be both understood by robot and easily done by human. Usually, human using oral language to communicate but there are some situations that require performing non-verbal activities such as deaf people, patient, and old people, therefore gesture recognition as communication media is needed to give order to Robot. This paper discusses hand gesture recognition as input command for Bioloid Premium Robot using two methods, K-Means clustering and Support Vector Machine (SVM) with directed acyclic graph (DAG) decision. Four gestures (forward, right, left and stop) were recognized using Kinect v2. The testing was done 6 peoples for three distances (2m, 3m, and 4m) and three slopes position (45, 0, -45). The SVM required 10ms recognition time with accuracy reached 95.15%, while K-Means needed 4.45ms recognition time with 77.42% accuracy. This study resulted in Multiclass SVM with DAG decision performs better than K-Means clustering method.

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis.

The increased occurrence and severity of cyber-attacks on critical infrastructure have underscored the need to embrace systematic and prospective approaches to resilience. The current research takes as its hypothesis that the InfraGuard Cybersecurity Framework—a capability model that measures the maturity of cyber resilience through three functional pillars, Cyber as a Shield, Cyber as a Space, and Cyber as a Sword—is an implementable and understandable means to proceed with. The model treats the significant aspects of situational awareness, active defense, risk management, and recovery from incidents and is measured using globally standardized maturity models like ISO/IEC 15504, NIST CSF, and COBIT. The contributions include multidimensional measurements of resilience, a scored scale of capability (0–5), and domain-based classification enabling organizations to assess and enhance their cybersecurity situation in a formalized manner. The framework’s applicability is illustrated in three exploratory settings of power grids, healthcare systems, and airports, each constituting various levels of maturity in resilience. This study provides down-to-earth recommendations to policymakers through the translation of the attributes of resilience into concrete assessment indicators, promoting policymaking, investment planning, and global cyber defense collaboration.

Soil nutrition assessment is a critical factor in improving both the quantity and quality of agricultural yields. The integration of Internet of Things (IoT) technology represents a groundbreaking advancement, allowing for real‐time measurements in smart farming. This article focuses on investigating and evaluating the current challenges in nutrient management within smart farming and explores the future potential of utilizing IoT for nutrient management. To gather data, bibliometric analysis and utilization of the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flowchart were conducted, searching on the Publish or Perish and Science direct search engines using the keywords “soil nutrient monitoring smart farming internet of things” without quotation marks. A total of 395 articles were initially identified, 20 of which were included in the analysis after verification with Covidence assessment tools. Our review of existing studies revealed that many nutritional management approaches are still in prototype and experimental design, without the use of experimental design agriculture standards, without validation through plant testing, and mainly focus on monitoring systems. This highlights the need for additional research into large‐scale studies, considering data security and the application of this technology in agricultural experimental design across a wide range of agricultural practices. Furthermore, future research on NPK IoT‐based sensors is expected to provide numerous benefits and accessibility to all farmers, ensuring agricultural profitability.