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All Journal InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Bumigora Information Technology (BITe)
Hani Harafani
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Indonesia Perancangan Jaringan Komputer untuk Sekolah Dasar Dengan Sistem Manajemen Bandwidth Hierarchical Token Bucket Jesika Marsaulina Simbolon; Hani Harafani; Rachmawati Darma Astuti
Jurnal Bumigora Information Technology (BITe) Vol 3 No 1 (2021): Juni 2021
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v3i1.966

Abstract

In the development of internet service technology, it has become a major requirement as a means of communication and exchanging information. Especially in the field of education such as elementary schols, internet facilities are very much needed where the users are administrative staff, teachers, students via the internet can access various knowledge needed quickly, so that it can simplify the learning process. Therefore, the bandwidth sharing system or it can be called bandwidth management must be in accordance with the conditions of the network service to be applied and bandwidth management is indispensable for multi-service networks, because the more and more varied applications that can be served by a network have an effect on link usage. In the network. The method in this research uses library research and the implementation uses the Hierarchical Token Bucket method, which is an application that function to regulate the distribution of bandwidth. The purpose of this analysis is to provide a bandwidth management system to make a fair distribution for users connected to the network. The results showed that the Hierarchicak Token Bucket method can regulate bandwidth usage for each client when downloading, uploading, streaming and browsing. Keyword: Bandwidth, HTB, Design, Network, Computer.
Perbandingan Metode Deep Learning dalam Mengklasifikasi Citra Scan MRI Penyakit Otak Parkinson Waeisul Bismi; Hani Harafani
InComTech : Jurnal Telekomunikasi dan Komputer Vol 12, No 3 (2022)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v12i3.15068

Abstract

Penyakit Parkinson merupakan gangguan neurodegenerative yang bersifat progresif dan relative umum pada system saraf pusat yang menyebabkan kesulitan dalam bergerak. Biasanya penyakit ini sering terjadi pada individu berusia lebih dari 60 tahun dipengaruhi oleh factor genetic dan lingkungan. Deteksi dini pada penyakit Parkinson dapat mencegah gejala hingga usia tertentu sehingga meningkatkan harapan hidup. Dalam penelitian ini bertujuan untuk menggunakan gambar otak dari Magnetic Resonace Imaging (MRI) untuk mengetahui bagaimana penyakit tersebut menyebar, dengan menggunakan metode deep learning menggunakan model atau arsitektur InceptionV3, VGG16, VGG19, NasnetMobile, dan MobileNet dengan melalui proses Input data - augmentasi - preprocessing - Classification (model a b c d ) - result dan pembelajaran mesin pada kumpulan data klinis dan paraklinis untuk mendiagnosis secara akurat meggunakan dataset yang berasal dari Parkinsons Brain MRI sebanyak 2 kelas yaitu kelas normal dan Parkinson. Hasil dari penelitian menggunakan deep learning berdasarkan kelima algoritma yang digunakan tersebut diperoleh nilai akurasi terbaik dari seluruh model arsitektur adalah arsitektur MobileNet sebesar 99,75% dengan kappa score 99,30% dengan total durasi komputasi selama 2 jam satu menit
Co-Authors Jesika Marsaulina Simbolon Rachmawati Darma Astuti Waeisul Bismi