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Analisa Performa VLAN Dengan Link Aggregation Control Protocol (LACP) Layer 3 Pada Kasus Penentuan Topologi Tommi Alfian Armawan Sandi; Firmansyah Firmansyah; Mugi Raharjo; Sri Watmah; Jordy Lasmana Putra
INSANtek Vol. 6 No. 1 (2025): Mei 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/insantek.v6i1.8751

Abstract

Dalam era digital saat ini, kebutuhan akan jaringan komputer yang cepat, handal, dan efisien menjadi sangat penting bagi organisasi dan perusahaan. Jaringan komputer yang baik harus mampu menangani beban lalu lintas data yang tinggi, mendukung berbagai aplikasi kritis, dan memastikan ketersediaan serta keandalan yang tinggi. Untuk mengimbangi kebutuhan bandwidth yang besar diperlukan jalur serta topologi jaringan yang mumpuni supaya layanan data bisa optimal. Oleh karena itu dalam metode untuk meningkatkan performa dan keandalan jaringan adalah dengan menerapkan Virtual Local Area Network (VLAN) dan Link Aggregation Control Protocol (LACP), pengujian yang dilakukan dengan menguji performa topologi A dan B serta konfigurasi LACP yang terinstall. sebagai perbandingan VLAN dengan menggunakan Bonding, pengiriman file dengan besar 1 GB dapat menghemat 1 Menit 13 Detik, file 8 GB dapat menghemat 2 Menit 37 Detik, serta file 16 GB dapat menghemat 3 Menit 9 Detik. Bedanya pada topologi B Router dapat menghemat port yang digunakan serta beban yang terdapat di router tidak terlalu besar. Pada prinsip sederhana bonding bisa bekerja dengan menggunakan VLAN untuk meningkatkan performa jaringan.
Implementation of Local Network Access Restriction security on L2TP VPN with Firewall Filter Method Tommi Alfian Armawan Sandi; Firmansyah; Eka Kusuma Pratama; Rian Septian Anwar
Jurnal Infortech Vol. 8 No. 1 (2026): June 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v8i1.12698

Abstract

The use of L2TP/IPSec-based Virtual Private Networks (VPNs) has become a common solution for providing remote access to local networks. However, VPN implementations without adequate access restrictions can potentially pose security risks, such as unauthorized access to internal resources, including shared directories on servers. This study aims to implement and analyze a local network access restriction strategy using a firewall filter with an accept-few-drop-any approach on an L2TP VPN network. The research method used is an experiment with the PPDIOO (Prepare, Plan, Design, Implement, Operate, Optimize) model approach. Testing was conducted in two scenarios: before and after the firewall filter implementation. The parameters analyzed included security aspects (access to shared directories) and network performance (latency, throughput, and packet loss). The results showed that before the firewall filter implementation, VPN users could access shared directories without restrictions. After the whitelisting strategy was implemented, access to file sharing services was effectively blocked, while other network services continued to run normally. In terms of performance, the firewall filter implementation did not have a significant impact on network performance.
Perbandingan Algoritma Dengan Particle Swarm Optimization Untuk Analisis Sentimen Pada Peraturan PSBB di Indonesia Mugi Raharjo; Jordy Lasmana Putra; Tommi Alfian Armawan Sandi; Musriatun Napiah
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i1.968

Abstract

The pandemic has given rise to new rules and terms in society. Various countries have their own regulations, including Indonesia with the name PSBB for that, the author tries to conduct research related to the PSBB condition in Indonesia with the intent and purpose of knowing people's sentiments towards it, the authors carry out this modeling positively and negatively. model in a tweet on Twitter. We capture information through Twitter media which then we process the data so that it is ready to be tested on the algorithm used. In data collection and processing, we use a fast miner application. In this study, Naive Bayes,KNN,and SVM were used. We also did a model comparison with Particle Swarm Optimization. model 1 tested three algorithms using a 0.7-0.8 ratio validation and 10-fold cross-validation, In Model 2 the author used a selection feature, namely Particle swarm Optimization where PSO was used as optimization. From the second model, the accuracy is 88.00%. for SVM + PSO, 88.54%% for NB + PSO and 81.58% for K -NN + PSO. And after testing the 2 methods, it turns out that Naive Bayes + PSO has the highest level of accuracy and precision