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The threat of Distributed Denial of Service (DDoS) is increasing develop along with increasing use of the Internet of Things (IoT) and Software-Defined Networking (SDN) architecture . Although SDN provides convenience in management network , properties its centralized control make it prone to to flooding attacks that can paralyze controller performance . Detection method conventional , such as approach statistics and machine learning, still own limitations in matter accuracy , high false positive rate , and dependence on extracted features manually . To overcome problem said , research This propose a hybrid deep learning based DDoS detection and mitigation model that combines Convolutional Neural Network (CNN) to extraction feature spatial from RGB and Gated Recurrent Unit (GRU) images for understand temporal correlation between traffic data network . System tested through network test-bed Mininet based with Ryu/Floodlight controller, using simulation DDoS attacks (Hping3, LOIC) and normal traffic (video streaming, HTTP server). Traffic data cross recorded in PCAP format, processed become RGB image measuring 200×200 pixels, and labeled based on type traffic . Evaluation results with metric accuracy , precision, recall, F1-score, and MCC show that the CNN–GRU model has performance more superior compared to baseline approaches such as CNN-only, GRU-only, as well as classical ML methods such as SVM and Random Forest. In addition , the system capable apply mitigation adaptive through automatic flow rule creation on edge switches. Findings This confirm that effective deep learning- based spatial -temporal hybrid approach in increase detection early and response DDoS attacks on SDN networks adaptive and real-time.  

Urban farming is a modern form of adaptation that is responsive to food security challenges, especially in areas with limited space such as Banten Province. This study aims to examine the dynamics of food security in the agrarian region of Banten and offer innovative solutions through urban farming practices. The global food crisis, the shrinking of agricultural land due to development and land conversion, and the decreasing public interest in farming are crucial issues that threaten local food security. In Banten, land conversion has resulted in the loss of more than 9,869.61 hectares of productive rice fields, which has a direct impact on local production capacity and increased dependence on external supplies. As a solution, this study advocates urban farming innovations such as hydroponics, vertical farming, and the utilization of idle land and home gardens. This approach not only utilizes limited land efficiently but also aligns with the principles of wise food planning as exemplified in the story of the Prophet Joseph. Qualitative methods with literature studies and content analysis were used to gather in-depth information. To create sustainable food security, this study identified three main strategies: digitalization of agricultural spatial planning using geographic information systems (GIS) to prevent illegal land conversion; Integrating circular agriculture and urban farming to manage organic waste; and engaging the younger generation as drivers of innovation through technologies such as the Internet of Things (IoT) and digital marketing. These efforts are expected to create high-quality and sustainable food security in Banten Province by combining social intelligence, technology, and environmental sustainability.

The quality of life in North Sumatra currently still needs improvement, where poverty rates and human development are still lagging. These problems can be reflected in the community welfare indicator, namely the Human Development Index (IPM). One of these problems can be solved by knowing the factors that determine the Human Development Index in North Sumatra. The Human Development Index is suspected to contain elements of spatial dependency, therefore in this study the spatial regression method will be used with a spatially dependent effect model. The results of the analysis and discussion obtained are that the SAR model is the appropriate model in the case of the Human Development Index in North Sumatra. Predictor variables that significantly influence the Human Development Index in North Sumatra are Pure Enrollment Rate (APM) at Senior High School level , Poverty Rate , Open Unemployment Rate , and PDRB on the basis of price .