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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Aiti: Jurnal Teknologi Informasi
Rissal Efendi
Universitas Kristen Satya Wacana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Implementasi Sistem Monitoring Jaringan Menggunakan Zabbix Berbasis SNMP Arya Pradana; Indrastanti R. Widiasari; Rissal Efendi
AITI Vol 19 No 2 (2022)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v19i2.248-262

Abstract

Monitoring jaringan diperlukan oleh administrator jaringan dalam mengelola sebuah jaringan. Perkembangan teknologi memungkinkan monitoring jaringan menggunakan protokol SNMP yang dipadukan dengan sistem monitoring menggunakan Zabbix. Penelitian ini dirancang untuk melihat serta memanfaatkan router untuk menampilkan informasi mengenai jaringan. Permasalahan yang sering terjadi adalah kurangnya tenaga administrator serta sistem monitoring jaringan yang belum mewadahi. Dengan ini tujuan penelitian terus dikembangkan sehingga administrator jaringan terbantu serta lebih cepat memecahkan persoalan untuk memonitoring jaringan yang ada. Penelitian ini menggunakan Zabbix sebagai sistem monitoring yang bersifat open source. Aplikasi ini mampu menampilkan informasi dari perangkat jaringan seperti: Traffic Data, Bandwidth Usage, Network Interface. Hasil uji coba yang telah dilakukan terbukti mampu untuk memonitoring jaringan menggunakan Zabbix berbasis SNMP dengan menampilkan hasil yang diharapkan. Hal ini ditunjukkan dengan berjalannya sistem monitoring pada aplikasi Zabbix. Pengembangan sistem monitoring jaringan menggunakan Zabbix-server diharapkan dapat membantu mempercepat masalah yang terjadi serta menjadi tolak ukur sistem yang sedang dikembangkan.
Integrasi Blockchain dan Keamanan Data untuk Meningkatkan Efisiensi dan Transparansi Sistem Logistik Global Cheysha Restu Nabila; Rissal Efendi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3277

Abstract

The global logistics system currently faces complex challenges related to efficiency and transparency. To address these issues, integrating blockchain and data security has emerged as a promising solution. Blockchain, the foundational technology behind cryptocurrencies, provides the capability to record, verify, and secure transactions in a decentralized and transparent manner. This study aims to analyze the potential of integrating blockchain and data security to enhance efficiency and transparency in global logistics systems. The research employs a literature review approach, examining relevant studies on blockchain and data security applications within the global logistics context. The findings indicate that blockchain integration in global logistics can facilitate a more efficient supply chain by enabling transparency, real-time validation, and enhanced monitoring of logistics activities. Additionally, encrypted data security can mitigate security risks and reduce fraud, providing better protection against cyberattacks and data manipulation.
Optimizing a Hybrid Deep Learning Model for DDoS Detection Using DBSCAN and PSO Indrastanti Ratna Widiasari; Rissal Efendi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6383

Abstract

This study proposes a hybrid deep learning approach that combines Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs) for Distributed Denial of Service (DDoS) cyberattack detection. The model, called DBSCAN–GRU–CNN, uses density-based clustering (DBSCAN) to select relevant features and reduce execution time. The dataset for this study was obtained from live penetration testing, where a series of simulated attacks was performed on a monitored network. To evaluate the performance of the proposed model, several comparison models were used, including DBSCAN–GRU–CNN (Single Hidden Layer), DBSCAN–GRU–CNN (Double Hidden Layers), DBSCAN–GRU–CNN (With Regularization), DBSCAN–GRU–CNN–PSO, GRU–CNN, GRU–CNN (With Hyperparameter Tuning), and Random Forest (Tuned Model). Variations of the model tested were made by adding hidden layers, regularization, optimization with Particle Swarm Optimization (PSO), and hyperparameter tuning. Experimental results show that the DBSCAN–GRU–CNN–PSO model provided optimal performance with a 99.3% accuracy, a 99% precision, a 98.9% recall, and a 99% F1-score, while the model with hyperparameter tuning achieved a 99% accuracy. By adding PSO, the model achieved optimized weights, better generalization, and excellent accuracy in DDoS detection.
Hybrid Deep Reinforcement Learning and Particle Swarm Optimization for Accelerated Multipath Routing in Congested SDN Environments Rissal Efendi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7324

Abstract

Network congestion remains a critical challenge in dynamic communication environments, often degrading data delivery performance and Quality of Service (QoS). This study proposes a Hybrid Deep Reinforcement Learning with Particle Swarm Optimization (DRL–PSO) model to adaptively optimize routing paths and mitigate congestion in Software Defined Networking (SDN). The proposed approach integrates the exploration capability of Deep Reinforcement Learning with the fast convergence characteristics of Particle Swarm Optimization to select optimal routes based on real-time network conditions. Simulations were conducted using realistic network topologies under three traffic scenarios: normal, moderate, and congested conditions. The proposed model was compared with several baseline methods, including Pure DRL, DDPG, PPO, DQN, Multi-Agent DRL (MARL), PSO-only routing, Shortest Path First (SPF), and Equal Cost Multi-Path (ECMP). The results show that Hybrid DRL–PSO achieves the lowest latency values of 15.2 ms, 34.8 ms, and 55.3 ms, as well as the highest throughput values of 9.45 Mbps, 6.34 Mbps, and 4.27 Mbps across the three scenarios. In addition, the model maintains low packet loss rates of 0.05%, 1.2%, and 8.5%, and jitter values of 4.3 ms, 9.2 ms, and 16.6 ms, respectively. The main novelty of this work lies in integrating PSO as a pre-selection mechanism to generate K-best candidate paths, reducing the DRL action space and accelerating learning convergence for QoS-aware multipath routing. This hybrid approach also demonstrates the practical potential of combining learning-based intelligence and optimization techniques for adaptive traffic management in real-world SDN infrastructures.
Co-Authors Arya Pradana Cheysha Restu Nabila Indrastanti Ratna Widiasari