Mobile ad-hoc network is a wireless network with variable topology and communication routes. In a MANET network, nodes have the role of finding and handling routes to each node in the network. There are several challenges such as bandwidth, power, and direction of movement of nodes. A routing protocol is needed to determine the route and ensure that the packets sent successfully arrive at their destination to solve this problem,. In large-scale networks, the routing structure requires redundant route information, resulting in frequent overhead. We investigated the use of the LEACH protocol as a hierarchical protocol to solve the network overhead. The proposed research also adds a PSO algorithm to optimize the performance of the LEACH protocol. The research method uses simulation methods and evaluation of routing models using Network Simulator. Evaluation is prioritized on the use of traffic TCP and UDP. The results show that TCP traffic has better delay, packet loss, and PDR values compared to UDP traffic when using the LEACH protocol. When the PSO algorithm is added, the delay value in TCP traffic decreases by 47%, the packet loss value decreases by 78%, and the PDR value also increases by 0.1%.

Indonesia is one of the countries that has a very large potential for disaster. If a disaster occurs then all activities will stop because the infrastructure is damaged. One of the example is digital payment activitiy. It will stop because there are failure because the existing system is loss. The proposed research is to conduct modeling using cellular ad hoc networks (MANET). An Ad-Hoc network is a standalone configuration for configuring the network infrastructure of devices that can self-configure wirelessly connected devices. In its development, Mobile ad hoc networks can be used in many ways. One of the example is in disaster management. In this study, we recommend restructuring the network by using a mobile ad hoc network to replace network infrastructure damaged by disasters. We are trying to design a replacement network infrastructure to replace the damaged network infrastructure so that payment transactions can be carried out continuously until the restoration of the new network infrastructure can be built. The protocol used in this study is the DSR (Dynamic Source Routing) protocol, this protocol has the advantage of finding routes when there is damage to the links between nodes. From the simulation results using NS-2, by increasing the number of nodes in the simulation area, it shows that there is a decrease in packet loss to 0%. There is an increase in delivery success of almost 98% when the number of nodes is increased. And there is a decrease in delay with the addition of the number of nodes

Indonesia is one of the largest countries in Asia, with a very dense population. According to data from The World Bank, the human population indicator in Indonesia in 2019 increased by 270,625.57 people. It shows that population density in Indonesia is related to world problems related to household waste. The household sector contributes as the top waste producer in Indonesia. Adipura data in 2019 stated that at least household waste contributed 36%. This figure is higher than the heap of waste generated from the traditional market, which reaches 24%. The accumulation of waste that occurs without any sorting of waste makes the waste more difficult to decompose and difficult to recycle. Apart from that, the current landfill causes air pollution. Therefore, it is necessary to increase public awareness regarding waste sorting and processing to overcome these problems. We propose to make a device that can help sort organic and inorganic waste with Artificial Intelligence technology based on Computer Vision using the Eigenface method and the Internet of Things. Eigenface is a method that has the working principle of using an XML file to perform face recognition. 1. Implementation of a Computer Vision-Based Garbage Counting System Using the Eigenface Method can run well, where when the system detects an organic object, the door of the counting machine can be opened, and vice versa if it detects inorganic, the machine door is closed. Accuracy results for organic are 70% and for inorganic 75%. It is due to the lack of variation in the dataset and changes in the object's physical condition.

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