This study presents a LoRaWAN-based IoT framework for robust data aggregation in Sargassum sp. cultivation, integrating multi-sensor monitoring and Kalman filter-based data enhancement. The system employs water quality sensors—including temperature, salinity, light intensity, dissolved oxygen, total dissolved solids, and pH—deployed in 6 out of 14 cultivation containers. Sensor data are transmitted via LoRaWAN to The Things Network (TTN) and processed through an MQTT-based pipeline in Node-RED before visualization in ThingSpeak. The Kalman filter is applied to improve data accuracy and detect faulty sensor readings, ensuring reliable aggregation of environmental parameters. Experimental results demonstrate that this approach effectively maintains optimal cultivation conditions, reducing ecological risks such as eutrophication and improving Sargassum sp. growth monitoring. Findings indicate that balanced light intensity plays a crucial role in photosynthesis, with optimally exposed containers exhibiting the highest survival rates and biomass. However, nutrient supplementation showed limited impact due to uneven distribution, highlighting the need for improved delivery systems. By combining real-time monitoring with advanced data processing, this framework enhances decision-making in sustainable aquaculture, demonstrating the potential of LoRaWAN and Kalman filter-based methodologies for environmental monitoring and resource management.

VANET (Vehicular Ad-hoc Network) is an emerging technology that has taken great attention in the recent years. Due to its changing topology and frequent disconnection naturally, it must have a flexible and optimum routing protocol to accommodate its behaviour. Hybrid Wireless Mesh Protocol (HWMP) is a routing protocol that has both reactive and proactive components that can be applied to the VANET and possibly gives the optimum results. In this paper, we evaluate the performance of the HWMP in the VANET based on Manhattan and Freeway mobility model. The performance evaluation is performed through a simulation under Network Simulator 2 (NS-2), using an output from VanetMobiSim to model the mobility pattern of Manhattan Model. Three performance metrics are used for the evaluation i.e. Packet Delivery Ratio, delay, and throughput. Simulation results show that the HWMP is able to prove the best performance of TCP in terms of packet delivery ratio and throughput. On the other hand, in the CBR, the HWMP only gives better result in terms of delay.

One of many important activities in the Wireless Sensor Network is the localization for tracked devices. Received Signal Strength (RSS) is a parameter of the power level that being received by the radio which can be used to track the location of the devices. This paper evaluates the localization of ZigBee devices which uses RSS fingerprinting by artificial neural networks. The RSS data processing by neural networks uses two types of training algorithms, i.e. Levenberg-Marquardt algorithm and Resilient Backpropagation algorithm. The performance of these two algorithms is evaluated. The experiment result shows that the RSS fingerprinting method is able to accurately predict the location of tracked ZigBee devices. The Levenberg-Marquardt algorithm has accuracy 96.41%, which is slightly better than Resilient Backpropagation algorithm with 94.52% accuracy.

In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.

Desa sukaraja merupakan desa dengan curah hujan yang cukup rendah. Hal tersebut menyebabkan timbulnya berbagai permasalahan dalam bidang pertanian. Permasalahan yang umumnya dihadapi adalah sulitnya pengairan lahan pertanian, dan kurangnya keragaman jenis tanaman yang bisa dibudidayakan disebabkan oleh kurangnya pengetahuan dari masyarakat desa tentang jenis tanaman bisa dibudidayakan di lahan kering. Kegiatan ini bertujuan untuk menemukan alternatif tanaman berupa varietas kelapa genjah yang cepat beradaptasi dengan kondisi lahan kering. Kegiatan ini dilaksanakan dengan tahapan pemilihan bibit kelapa genjah, persiapan lahan tanam, penanaman bibit kelapa genjah, serta penyiraman dan pemupukan tanaman kelapa genjah. Hasil yang diperoleh dari kegiatan ini adalah terpilihnya beberapa varietas bibit kelapa genjah yang unggul, pemanfaatan lahan untuk penanaman bibit kelapa genjah siap dan sesuai dengan metode pelaksanaan, setelah tersedianya bibit dan lahan kemudian dilakukan penanaman bibit kelapa genjah, selanjutnya perawatan dilakukan dengan melakukan penyiraman serta pemupukan secara rutin. Berdasarkan hasil penelitian yang penulis lakukan selama kegiatan Kuliah Kerja Nyata di Desa Sukaraja bahwa tanaman kelapa genjah dapat menjadi tanaman alternatif yang cocok di tanam pada lahan kering.

LoRa Technology is one of the devices used on Low-Power Wide Area Networks (LPWAN), which is a viable alternative wireless communication technology for the Internet of Things (IoT). The LoRa device is meant to traverse a large distance while using minimal power. However, because it uses single-hop communication, the Gateway's farthest nodes will die prematurely as a result of the increased energy usage. This research attempts to improve the range of LoRa networks utilizing multi-hop uplink communication while also reducing energy consumption by adjusting the LoRa transmission power to the lowest possible dBm in each hop. As a network model, a star topology-based tree made of some rings with a gateway as the central point is chosen. The performance of two forms of uplink communication, single-hop and multi-hop models was tested in terms of energy consumption and coverage. The results show that the network structure in the multi-hop uplink model can extend coverage over a greater distance while using less energy than the single-hop uplink model. This model can be used as a supplement to the LPWAN's uplink communication in the IoT to enhance the network's coverage range and lifetime.