Design Autonomous Drone Control For Monitoring Tea Plantation Using Dynamic Programming and Kruskal Algorithm

Indonesia is the country with the largest plantation in the world, especially tea. With the vast area of land from plantations, it makes the planters have difficulty in monitoring the land, so that the impact on the harvest is obtained. At present, research on plantations, especially tea plantations, is still very little, as technology develops researchers are more interested in researching technology that is intended for urban areas, while technology intended for plantations and agriculture is still very rare. Unmanned Aerial Vehicle (UAV) or drone is proposed to be a solution to overcome this problem. But drones have limitations in power consumption, the drones can only last 30 minutes in the air, so an optimum path is needed to save energy efficiency in shooting in the air. Design Autonomous drone control for monitoring tea plantation is a technique for monitoring tea plantations in the air. Drones used will be a solution for planters to be able to better monitor the land. The purpose of this research is to create a control system for drones to be able to monitor tea plantations in the air. In addition, the optimum path of the drone is needed to determine energy efficiency and good image results. The energy efficiency that is obtained must be able to be received with the results of the images obtained. The algorithm proposed in this study is Dynammic Programming to create a graph and look for possible paths encountered and the kruskal algorithm to calculate the paths generated from Dynammic Programming. This algorithm is used for the network , This algorithm will be implemented for finding the optimum path for taking picture. Drone for this research will be used for taking the information data, information from this research is tea leaves. In previous research, the Drone only included sensing data in the form of a landing pad to land the drone automatically, which could still be developed again. Therefore, in this research we use drones that can work well to overcome the need for monitoring tea gardens. From the initial data, there are 3 different data, including height, path and image. From these data, 2 heights were taken which met the requirements for calculation, namely 100m and 80m. The results obtained from the height of 100m, obtained the optimum path for shooting with the assumption that the image error is 4 %, 2 % smaller than the previous data which is 6 %. In addition, using both algorithms has an impact on better power consumption. At a height of 100m there is a power consumption of 29 % or 25Wh each iteration, this result is better 26 % or 23Wh compared to the initial 100m data which has an average power of 55 % or 49 Wh each iteration, but the image results from 100m are different with the initial data so that the 100m height is not recommended. The 80m height is obtained by the optimum path for shooting assuming the image error is 3 %, this value is 1 % better than the previous one which had a 4 % error the results of the images obtained are still the same as the initial data. the average power consumption is 29 % or 25.8 Wh each iteration, this result is better 17 % or 15Wh than the initial 80m data which has an average power of 45.1 % or 40 Wh each iteration. So from the results of research that has been obtained in the field, one height is appropriate for monitoring tea gardens at the Research Institute for Tea and Cinchona (PPTK) with image accuracy rates above 95 %, which is 80m high and the optimum path that has been found to make the value of power consumption is more efficient than before. The results that have been obtained will be a solution where tea planters can use drones to monitor gardens in PPTK gambung, ciwidey

Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security

AUTOMATIC VEHICLE COUNTER SYSTEM BASED BLOB DETECTION FOR HIGHWAY SURVEILLANCE

The number of vehicles that increase every year has a major impact on the occurrence of congestion and accidents and causes a significant increase in the volume of vehicles, especially on the highway. With this increase, many officers find it difficult to be able to anticipate or supervise vehicles directly. The research that we made, entitled Automatic Vehicle Counter System Based on Blob Detection for Highway Surveillance Using OpenCV, is a solution to this problem because by utilizing image transformation it makes it easier for the system to be able to detect vehicles and identify the number of vehicles entering the lane. The results obtained show an accuracy value of 97.11% based on testing with 10 video samples, with a total of 1329 vehicles detected out of a total of 1362, meaning that the total error is only 3.02%

Perancangan Sistem Pembayaran Non Tunai Berbasis NFC, Raspberry dan Arduino

Near Field Communication (NFC) merupakan teknologi yang baru mulai masuk di Indonesia dan terkait erat dengan operator telekomunikasi. Di era globalisasi ini pembayaran dengan menggunakan NFC sudah banyak diterapkan di Asia dan Eropa yang dapat membantu operator telekomunikasi dan bank untuk memberikan kemudahan bagi pelanggannya dalam melakukan transaksi pembayaran non tunai dengan lebih efisien dan lebih mudah dalam melakukan transaksi baik pembayaran atau penarikan secara cepat dan mudah. Metode penelitian yang digunakan pada perancangan ini adalah membuat suatu sistem pembayaran non tunai dengan menggunakan NFC sebagai alat pembayarannya. Ada berberapa alat yang di gunakan dalam perancangan ini seperti Raspberry pi, PN532, Arduino unoR3 yang memiliki fungsi yang berbeda beda Hasil perancangan selanjutnya diuji untuk mengetahui sebagaimana data pembayaran bisa di gunakan dalam proses transaksi pada umunya. Perancangan telah terpenuhi apabila alat bisa membaca dan memproses data dari kartu maupun smartphone. Hasil dari perancangan Sistem Pembayaran non tunai berbasis NFC, Raspberry dan Arduino, ini dimana sebuah kartu yang memancarkan sebuah gelombang dan akan dibaca oleh alat kemudian alat tersebut akan memproses data yang di terima dari kartu tersebut sehingga mendapatkan hasil berupa data pembayaran non tunai. NFC PN532 dapat mendeteksi sinyal dari Kartu, Tag maupun Smartphone dengan jarak lebih kurang 1- 4 cm dengan menggunakan penggaris dari sensor yang terdapat pada module NFC PN532 tersebut. Panjang data dan penyimpanan terhadap reader Tag baik kartu maupun Smartphone sebesar 7678Bytes dengan frekuensi 13,56MHz. Proses pengiriman data atau pertukaran informasi dari NFC dan Tag dengan cara menempelkan Tag Reader ke NFC modul

Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security

Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security.</jats:p

Enhancement of images compression using channel attention and post-filtering based on deep autoencoder

Image compression has recently become a crucial research topic, especially in data transformation and storage processes. Conventional methods have long been used to significantly reduce image file sizes; however, they are insufficient to meet current transmission and storage needs due to various issues often encountered in the received image size and quality. Currently, deep learning-based approaches using autoencoders are considered one of the best solutions. This research aims to explore the potential of artificial neural networks to achieve more optimal data compression with better image reconstruction results by leveraging hyperparameters in the compression process and channel attention. Furthermore, it introduces a novel image compression architecture utilizing convolutional autoencoders to replace traditional transformation roles and the use of post-filtering to enhance the quality of the reconstructed images. Experimental results using two datasets, CLIC for training and KODAK for testing, demonstrate that this method outperforms existing conventional methods and some previous studies. Finally, with an average PSNR improvement of 34% and an MS-SSIM improvement of 8%, the model in this research significantly enhances the rate distortion (RD) performance compared to previous approaches