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Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification Nurwan Reza Fachrurrozi; Andri Agustav Wirabudi; Seandy Arandiant Rozano
SINERGI Vol 27, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023.1.013

Abstract

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.
Perancangan Sistem Pembayaran Non Tunai Berbasis NFC, Raspberry dan Arduino Andri Agustav Wirabudi; Asep Najjmurokhman
Jurnal FUSE-Teknik Elektro Vol 1, No 2 (2021): Jurnal FUSE-Teknik Elektro
Publisher : Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (867.007 KB) | DOI: 10.52434/jft.v1i2.1509

Abstract

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 Autonomous Drone Control For Monitoring Tea Plantation Using Dynamic Programming and Kruskal Algorithm Andri Agustav Wirabudi; Rendy Munadi; Angga Rusdinar; Dadan Rohdiana; Dong Ho Lee
eProceedings of Engineering Vol 6, No 1 (2019): April 2019
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

AbstractIndonesia is a country with the largest tea producers in the world, with a very large area needed tools to be able to help monitor the area of tea plantations as a whole. Unmanned Aerial Vehicle (UAV) wash chosen as a solution for the monitoring proses. Optimum flight path calculation is needed in order to produce good quality images, and also it influence to power consumption. The algorithm proposed in this study is Dynamic Programming and Kruskal Algorithm. Implementing these two network algorithms is expected to find the optimal path in aerial photography. The experimental results showed that the algorithm produced the optimum path , and more efficient power consumption than conventional lines. Image data obtained during tea plantation monitoring produced high-quality images, with the accuracy of each map above 90% and the assumption of errors below 5%. Keywords—Unmanned Aerial Vehicle UAV, Monitoring, Dynamic Programming, Kruskal, Mapping.
Classification of tea plantation using orthomosaics stitching maps from aerial images based on CNN Andri Agustav Wirabudi; Nurwan Reza Fachrurrozi
JURNAL INFOTEL Vol 15 No 1 (2023): February 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i1.871

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.
AUTOMATIC VEHICLE COUNTER SYSTEM BASED BLOB DETECTION FOR HIGHWAY SURVEILLANCE Andri Agustav Wirabudi; Nurwan Reza Fachrur Rozi; Heeji Han
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 9 No 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4142

Abstract

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%.