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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer JSAI (Journal Scientific and Applied Informatics) Abdimas Indonesian Journal
Akbar Pandu Segara
Universitas Jember
Religion Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Implementasi MQTT (Message Queuing Telemetry Transport) pada Sistem Monitoring Jaringan berbasis SNMP (Simple Network Management Protocol) Akbar Pandu Segara; Rakhmadhany Primananda; Sabriansyah Rizqika Akbar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1038.2 KB)

Abstract

The growing use of current information technology users, monitoring system is needed to facilitate network administrators to monitor the devices connected in a computer network. Currently network monitoring systems generally display information by polling device data every few minutes. This causes the administrator don't know as early as possible if something happens to the device being monitored. This study developed an SNMP based network monitoring system by implementing MQTT. MQTT uses the principle of publish-subscribe to communicating. MQTT is used because power-saving and lightweight messaging protocol. This research produces a network monitoring system which can monitored device information like CPU, load, and memory periodically and display it in graphical form and table in web interface. System development with implementing SNMP agent on monitored device and SNMP manager on NMS Server as publisher. Manager requested information to agent every 2 seconds. On the client side is implemented a subscriber to subscribe published data by manager and then processed on the web-based interface. From the test results show the processing time data by the server and client takes an average time of 2.029 seconds. For data processing time from server to client takes an average time of 1.210 seconds.
Pelatihan Teknologi Drone untuk Pemetaan Pertanian Berkelanjutan Kelompok Tani Kemiri Santoso Desa Kalibaru Manis Arief, M. Habibullah; Segara, Akbar Pandu; Kartiko, Erik Yohan; Maududie, Achmad; Auliya, Yudha Alif; El Maidah, Nova; Swasono, Dwiretno Istiyadi
Abdimas Indonesian Journal Vol. 4 No. 2 (2024)
Publisher : Civiliza Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59525/aij.v4i2.533

Abstract

This community service program aims to overcome the low efficiency of agricultural land management in Kalibaru Manis Village, Banyuwangi, by focusing on increasing farmers' knowledge in utilizing drone technology for mapping. This training provides theory and practice of drone operation and processing aerial image data using Geographic Information System (GIS) software. The implementation method includes preparation, training, and evaluation stages. Participants were trained to operate drones, retrieve image data, and analyze it to produce land maps. A collaborative approach between lecturers, students, and practitioners was applied to ensure the success of the program. As a result, participants are able to use drones independently and utilize the data for more effective land management. This program increases agricultural productivity and supports environmental sustainability through the application of modern technology.
IMPLEMENTATION OF JOHNSON'S SHORTEST PATH ALGORITHM FOR ROUTE DISCOVERY MECHANISM ON SOFTWARE DEFINED NETWORK Segara, Akbar Pandu; Ijtihadie, Royyana Muslim; Ahmad, Tohari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 1, Januari 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i1.a1011

Abstract

Software Defined Network is a network architecture with a new paradigm which consists of a control plane that is placed separately from the data plane. All forms of computer network behavior are controlled by the control plane. Meanwhile the data plane consisting of a router or switch becomes a device for packet forwarding. With a centralized control plane model, SDN is very vulnerable to congestion because of the one-to-many communication model. There are several mechanisms for congestion control on SDNs, one of which is modifying packets by reducing the size of packets sent. But this is considered less effective because the time required will be longer because the number of packets sent is less. This requires that network administrators must be able to configure a network with certain routing protocols and algorithms. Johnson's algorithm is used in determining the route for packet forwarding, with the nature of the all-pair shortest path that can be applied to SDN to determine through which route the packet will be forwarded by comparing all nodes that are on the network. The results of the Johnson algorithm's latency and throughput with the comparison algorithm show good results and the comparison of the Johnson algorithm's trial results is still superior. The response time results of the Johnson algorithm when first performing a route search are faster than the conventional OSPF algorithm due to the characteristics of the all pair shortest path algorithm which determines the shortest route by comparing all pairs of nodes on the network.
Perbandingan Performa Algoritma Random Tree, K-NN, dan A-NN untuk Deteksi Serangan DDoS pada Software Defined Network (SDN) Akbar Pandu Segara; Muhammad Andryan Wahyu Saputra; Narandha Arya Ranggianto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8387

Abstract

Software-Defined Networks (SDNs) with a centralized architecture are vulnerable to Distributed Denial of Service (DDoS) attacks, which can cause widespread network service failures. This study aims to compare the performance of three Machine Learning algorithms—K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN), and Random Tree—in detecting DDoS attacks in an SDN environment. The DDoS-SDN dataset, consisting of 104,345 rows and 23 columns, was used with a data split of 70% for training and 30% for testing. Evaluation was conducted using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that ANN achieved the best performance with an accuracy of 96.85%, precision of 94.35%, recall of 97.79%, F1-score of 96.04%, and AUC of 0.994, followed by K-NN with an accuracy of 88.89% and Random Tree with the lowest accuracy of 86.49%. The superiority of ANN is attributed to its ability to capture complex non-linear patterns, perform automatic feature extraction, and adapt to the heterogeneity of data from the 22 features used. These findings indicate that ANN is the optimal choice for implementing a real-time DDoS attack detection system in an SDN environment, providing a strong foundation for the development of intelligent and adaptive Machine Learning-based network security systems
Co-Authors Achmad Maududie Dwiretno Istiyadi Swasono El Maidah, Nova Kartiko, Erik Yohan M. Habibullah Arief Muhammad Andryan Wahyu Saputra Narandha Arya Ranggianto Rakhmadhany Primananda Royyana Muslim Ijtihadie Sabriansyah Rizqika Akbar Tohari Ahmad Yudha Alif Auliya, Yudha Alif