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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL INTEGRASI SKANIKA: Sistem Komputer dan Teknik Informatika
Medi Taruk
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Computer Science & IT Control & Systems Engineering Engineering Other

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Analisis Throughput Varian TCP Pada Model Jaringan WiMAX Medi Taruk; Ahmad Ashari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 2 (2016): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.15529

Abstract

 Transmission Control Protocol (TCP) is a protocol that works at the transport layer of the OSI model. TCP was originally designed more destined for a wired network. However, to meet the need for the development of a very fast network technology based on the needs of the use by the user, it needs further development to the use of TCP on wireless devices. One implementation of a wireless network based on Worldwide Interoperability for Microwave Access (WiMAX) network is a model that offers a variety advantage, particularly in terms of access speed.In this case, use NS-2 to see throughput at TCP variants tested, namely TCP-Tahoe, TCP-Reno, TCP-Vegas, and TCP-SACK over WiMAX network model, with few observations scenarios. The first is a look at each of these variants throughput of TCP when only one particular variant of the work in the network. Second observe all variants of TCP throughput at the same time and have the equivalent QoS, but with the possibility of a small congestion based on the capacity of the link is made sufficient. Third observed throughput with multi congestion.In WiMAX network has scheduling services are UGS, rtPS and ertPS using UDP protocol and nrtPS and BE using the TCP Protocol. By using the software network simulator (NS-2) to obtain performance comparison TCP protocol-based services on the WiMAX network with QoS parameters are throughput, packet loss, fairness and time delay.
Augmented Reality Pengenalan Alat Musik Tradisional Sape’ Muhammad Bambang Firdaus; Ade Chrisvitandy; Medi Taruk; Masna Wati; Andi Tejawati; Fadli Suandi
JURNAL INTEGRASI Vol 14 No 2 (2022): Jurnal Integrasi - Oktober 2022
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v14i2.4041

Abstract

Indonesia adalah negara yang kaya akan budaya, seni, dan tradisi warisan leluhur; setiap daerah memiliki budaya, kerajinan, dan adat istiadatnya sendiri yang merupakan warisan penting dan mapan; alat musik tradisional adalah salah satu contohnya. Sape' adalah alat musik asli Dayak yang paling langsung dikenali dari kemiripannya dengan gitar. Penulis melakukan penelitian ini dengan tujuan untuk mengembangkan aplikasi Augmented Reality berbasis Android yang dapat memperkenalkan alat musik tradisional Sape. Itu dibuat menggunakan model proses pengembangan perangkat lunak Siklus Hidup Pengembangan Multimedia dan berjalan dengan lancar di Android versi 6.0 (Marshmallow) minimum dan semua lapisan rasio aspek. Sementara itu, terlepas dari rasio aspek perangkat, kualitas model 3D akan tetap konstan.
Implementasi Arsitektur Recurrent Neural Network Pada Analisis Sentimen Clash of Champions Arif Hidayat; Anindita Septiarini; Medi Taruk
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3586

Abstract

Clash of Champions is an educational program by Ruangguru on YouTube that has received mixed responses. This study aims to perform sentiment analysis using three Recurrent Neural Network (RNN) architectures: Vanilla Recurrent Neural Network (Vanilla RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data consists of 2,100 training samples, 300 validation samples, and 600 testing samples collected from YouTube and enriched with data augmentation using GPT-4 technology. Additionally, 35 comments from a survey conducted via Google Form are used for generalization testing. Comments are classified into three sentiments: Pro, Neutral, and Contra. The analysis involves preprocessing, model training, and evaluation using standard metrics. GRU demonstrated the best performance with an accuracy of 99.2% and the highest F1 score. LSTM achieved an accuracy of 99.0% and a recall of 100% for the Pro class, while Vanilla RNN was less stable. On real-world data, GRU correctly predicted 16 comments, outperforming LSTM with 14 correct predictions and RNN with 13 correct predictions. GRU excels in accuracy, stability, and adaptability to the data.
Co-Authors Ade Chrisvitandy Ahmad Ashari Anindita Septiarini, Anindita ARIF HIDAYAT Fadli Suandi Muhammad Bambang Firdaus Tejawati, Andi Wati, Masna