No abstract

In Indonesia, Tea is an important economic crop that is widely grown, and in many countries, accurate mapping of tea plantations is essential for the operation, management, and monitoring of the growth and development of the tea industry. We propose a classification of tea plantations using orthomosaics from aerial images based on the Convolutional Neural Network (CNN) which identifies the condition of the tea plantations with the parameters observed, namely the condition of the tea leaves, estimated yields achieved, and monitoring of treeless areas caused by tree death. In this study, we took a sample of 20 hectares. We classify images based on maps generated by drones in previous studies. Image segmentation is performed to maintain image objects, while an enhanced CNN model is used to extract deep image features. To get complete results, this study uses UAV (Unmanned Aerial Vehicle) imagery as the basis for the map, which is then combined or stacked into one image. The results of the images that are used as maps undergo image classification, where the information contained in the map is mapped and divided according to its type. The area of ​​the tea plantations sampled is 20 ha, and the threshold for the image captured by the UAV is 5% of the total area captured, which is around 1 ha. If the image created by the UAV has an error of more than 5%, then the image does not meet the classification requirements. We determine this margin of error based on the performance of the drone camera capture when capturing Fig. 2, and the resolution used is 4096 x 2160 for each image captured by the drone. We conclude that the proposed method for mapping tea plantations using ultra-high resolution remote sensing imagery is effective and has great potential for mapping tea plantations in areas such as the development of drone aerial photography methods for tea plantations based on image classification for forecasting. tea plantations Image stitching can be used to improve the monitoring of tea plantations and predict harvest time using a classification process. The tea garden map has 5 types of information categorized by harvest time, medium leaf tea, milled tea, tea, and old tea. The success of image recognition shows the error matrix data by testing 123 random points spread over the map, of which 113 random points were identified with an average accuracy of 91.87%, this value is of course very good and exceeds the specified threshold of 75%. When using this method, an error occurs that the colors of similar pixels cannot be distinguished, resulting in an incorrect detection. In addition, the image stitching method using the orthomosaics method has succeeded in performing image stitching and can be well applied to classification using the CNN approach.

Revolusi industri 4.0 sangat berdampak pada kurikulum pembelajaran khususnya tingkat kejuruan dan pendidikan tinggi. Mereka ditutut untuk berperan aktif dalam mensukseskan dan percepatan implementasi industry 4.0. Peguruan Tinggi menjadi semacam tempat persiapan atau pelatihan bagi sumber daya manusia (SDM) untuk memberikan keterampilan yang berkaitan langsung dengan penerapan dan pengembangan aplikasi yang mendukung percepatan industry 4.0 tersebut. Dalam rangka mendukung usaha pemerintah tersebut, maka Tim abdi masyarakat Telkom University dan Institut Teknologi Telkom Jakarta berpartisipasi aktif menjalin kerjasama dengan SMK Nagrak Purwakarta. Pengabdian masyarakat ini berfokus untuk menjembatani produktifitas antar jurusan yang ada di SMK Nagrak seperti jurusan Agribisnis Tanaman Pangan dan Hortikultura (ATPH) dan jurusan teknik komputer jaringan (TKJ) sehingga menghasilkan budidaya tepat guna yaitu aquaponik berbasis Internet of Things (IoT).  Kegiatan ini terdiri dari pembuatan modul dan video tutorial untuk siswa dan guru, perancangan sensor dan perangkat, dan pembuatan aplikasi monitoring pada smartphone untuk pemantauan tanaman aquaponik. Sensor yang dirancang tersebut terdiri dari sensor pendeteksi PH pada air dan sensor oksigen yang dipasang di kebun akuaponik sekolah. Para siswa dan guru antusias dalam mengikuti workshop mengenai materi mengenai IoT. Tidak hanya mendapatkan materi, para siswa dan guru kemudian melakukan praktik secara langsung untuk membuat aquaponik serta melakukan monitoring suhu. Berdasarkan survei terhadap hasil pelaksanaan pengabdian masyarakat ini, guru dan siswa sangat puas dan antusias terhadap dan berharap kegiatan pengabdian kepada masyarakat dapat dilakukan secara berkesinambungan.

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.