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Journal : Journal of Computer System and Informatics (JoSYC)

Topologi Redundansi Link Untuk Keamanan Serta Mitigasi Ketersediaan Jaringan Komputer Menggunakan Rapid Spanning Tree Protocol Mohammad Taufan Asri Zaen; Ahmad Tantoni
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2336

Abstract

The frequent occurrence of natural disasters requires telecommunications companies to require a backup internet backbone line if the telecommunications infrastructure is damaged/disconnected due to natural disasters. Reporting from inet.detik.com APJII revealed that the Jakarta-Singapore fiber optic network had broken, which had an impact on internet services in Indonesia. Fiber optic repair can take up to 2-3 weeks requiring network backup, the main solution for network backup with satellite, but problems will arise when configuration for satellite lines takes 1-2 days. Problems arise again when combined between fiber optic internet lines and satellite internet lines which cause the looping network to create network disturbances, to maintain interference, a spanning tree protocol method is needed to backup lines automatically and create link redundancy if one of the lines is problematic or disconnected. The purpose of this study is to design a link redundancy topology for security and mitigation of network availability using the rapid spanning tree protocol (RSTP) with router rb750gr3, switch rb260gs and switch dgs-108. In the RSTP network topology, it is designed with 3 redundant lines connected between the rb260gs switch and the dgs-108 switch, with an on-stick router rb750gr3 and is designed with 6 access mode vlan lines. In testing the inter-vlan network, RSTP terminates the redundancy link and then uses the ping command at the command prompt to determine how many minutes to backup the link. The NDLC method is used in determining how many seconds to backup this redundancy link using. The results of testing the link termination ether2-switch-mikrotik and ether1-switch-dlink using the "ping" command for 20 seconds in determining how many seconds to backup the link get the result that Request Time Out does not occur and the average time for each pc in determining the backup path for the pc-vlan20 22.9ms, pc-vlan30 35ms, pc-vlan70 23.5ms. The results of link termination testing ether3-switch-mikrotik and ether2-switch-dlink using the "ping" command for 20 seconds in determining how many seconds to backup the link get the result that there is no Request Time Out and the average time for each pc in determining the backup path for the pc-vlan20 23ms, pc-vlan30 24ms, pc-vlan70 43.7ms.
Komparasi CNN dengan ResNet Untuk Klasifikasi Paling Akurat Tingkat Keganasan Diabetes Berdasarkan Citra Retinopathy Lalu Mutawalli; Mohammad Taufan Asri Zaen; Yuliadi Yuliadi
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3248

Abstract

Diabetes becomes an infectious disease that has a very significant increase, it's increasing not only in old age or even suffered by a young age. Based on the Data and Information Center of the Ministry of Health of the Republic of Indonesia, the prevalence of diabetes is 9% in female genitals and 9.65% in male genitals and is estimated to increase to 19.9% with the addition of population age. Currently, the Machine Learning (Depp learning) approach is widely used in carrying out the calcification of medical image data. Retinopathy Diabetes image data can be used as material to build a classification model by utilizing the Convolutional Neural Network (CNN) algorithm and Residual Network (ResNet). The test results in the model developed in this study were the comparison showing the accuracy level of CNN 68.49% while the ResNet was 81.23%, in the CNN loss test 32.57% while the ResNet was 12.59%. In the Scenario of the application of Learning Rate 0.0005 also the ResNet is better than CNN, in the CNN Learning Rate Application Scenario producing an accuracy value of 68.49% while the ResNet adopts an accuracy value of 81.84% and on the CNN loss value of 20.14 while the 10.2% Resnet has a higher level of accuracy has a higher accuracy rate Compared to CNN and the application of learning rates it also affects in building a more accurate model in the case study of Retinopathy Diabetes.
Co-Authors Abdul Gofur Ahmad Farijan Ahmad Tantoni Akram, Muh. Saiful Aliy Nauval Hanafi Andikasari, Devi Anitarakhmi Handaratri Apriani, Yanti Arif Maulana, Arif Asep Yunandy Ayu Lestari Baiq Daniatan Janiah Baiq Dea Zualina Baiq Sri Damanyanti Wiradarma Baiq Sri Mardiani Chaerun Hadiyan Syah Chalvin Bebby Febrianto Damanyanti Wiradarma, Baiq Sri Dedy Sofian MZ Dian Nitami Dodi Siregar Dodi Siregar EFENDI, GUNAWAN Eri Sasmita Susanto Erlin Windia Ambarsari Fadli, Sofiansyah Fahmi, Hairul Fahri Hamdani Falah Husni Fredy Windana Haerani, Kiki Hairul Fahmi Hairul Fahmi Hairul Fahmi Hamdani, Zainal Basit Hammid Muammar Robbani Al Faruq Handayani, Yudiana Tutik Hanif Priabdul Aziz Herfandi Herfandi Hifni, Hifni Husnul Khotimah Ichwan Purwata Idham Idham Indi Febriana Suhriani Istiqlal, Alamullah Julkarnaen Julkarnaen Julkarnain, M. Jumaryadi, Yuwan Khairan marzuki Khairul Imtihan Khairunnazri Khairunnisak Khalid, Syam Basri Kiki Haerani L Dita Putra LALU GALUH FIRMAN SIHABULLAH Lalu Mutawalli Leni Sefvia M. Julkarnain M. Saiful Akram Maemun Saleh Mansur Mardi Mardi Marzuki Adami Maulana Ashari Mega Tazayyun Mesran, Mesran Mirna Hidayati, Fitri Muammar Patoni Muh Nawawi Muhamad Hariadi Muhammad Fauzi Zulkarnaen Muhammad Irwan Suryadi Mulyanto, Yudi Mustakim Mustakim Musthofa Luthfi Al Manfaluty NABILA OPER Natasya, Nenti Aulia Nawassyarif Nitami, Dian Nora Dery Sofya Novita Susiana Ikhwani Piandari, Reski Pratama, Walidi Wahyu Putra, L Dita Putri, Juliyantika Ayudita Reflan Revife Purba Rizal Mahendra Rizal, Samsu Rodianto, Rodianto Rosika, Herliana Rozi, Fatrul Rusdan Rusdan Ryadi Putra Ryan Suarantalla Saepul Bahri Safira, Azzahrah Maulya Safwan Sihab Saikin Saikin Sansul Yasmi Sari, Kadek Dwijayanti Komala Septia, Laili Setiawansyah Setiawansyah Siti Fatmah Siti Patimah Az-zaen Sofiansyah Fadli Sofya, Nora Dery Sonia Sonia Sopian Sumardi Sri Agustiani Br Siburian Suharjo, Darji Darmo Suhriani, Indi Febriana Sunaryo Sunaryo Syahadatul Intihan Tantoni, Ahmad Tedi Irawan Trisnadi, Yudi Uyunun Lusiana W, Yunanri Wafiah Murniati Wahyu Ramadhan Wijono Wijono Wire Bagye Wire Bagye Yudi Pratama Yuliadi Yuliadi Yuliadi Yuliadi Yusroaini, Yusroaini Zainal Basit Hamdani Zulkhaeri, Zulkhaeri