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All Journal TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal Informatika Global MULTINETICS Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Bagus Aditya
Universitas Telkom
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Wireless Last Mile Study in Rural Areas Bagus Aditya; Galih Nugraha Nurkahfi; Christoporus Ivan Samuels
TELKA - Jurnal Telekomunikasi, Elektronika, Komputasi dan Kontrol Vol 7, No 2 (2021): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v7n2.100-107

Abstract

Internet access has become a primary human need. During the COVID 19 pandemics, almost all activities such as studying and working online. However, several rural areas still did not have internet access due to weak cellular signals. Internet access in rural areas can have various alternatives, such as VSAT (satellite). It needs an inexpensive and precise way to complete the internet access coverage because many customers were concentrated in areas far from the VSAT terminal. WLAN 802.11n was an alternative way to expand the range of internet coverage with low cost and easy implementation. Our case study uses the 5GHz frequency with a sectoral antenna as the multi-hop point-to-point backhaul network's frequency to avoid the risk of channel interference on the backhaul. On the other hand, 2.4GHz frequency with an omnidirectional antenna serves smartphone customers. On the test results, the network latency between multihop access point, has a maximum value of 19,894 ms. This result means that the latency obtained can be categorized as preferred VoIP services based on ITUT G.1010 in the local network. Then for the customer on the fourth hop, 3968 meters from the VSAT terminal, the UDP data rate of 1.04Mbps was stable, and the TCP data rate decreased to 1.26Mbps. This paper emphasizes the use of multihop 5Ghz WLAN 802.11n as a backhaul to expand internet access coverage from VSAT in rural areas, where the concentration of customers was far apart and there were many buffalo horn barriers in traditional homes and buildings.
AUTO SCALING DATABASE SERVICE WITH MICRO KUBERNETES CLUSTER Anita Rosdina Nasution; Favian Dewanta; Bagus Aditya
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.484

Abstract

Data storage media, or what is often referred to as a database is something that is quite vital for technological developments. As the amount of data increases, it allows database services to experience downtime. For this reason, it is necessary to build an infrastructure that can replicate itself, so that it will avoid downtime. This infrastructure can be built using a container orchestration tool called Kubernetes which has high availability and autoscaler features, so it can replicate and guarantee service availability, to avoid downtime. This research builds a MongoDB NoSQL database service. This service is built using micro Kubernetes clusters from several different data centers. This service also implements a horizontal pod autoscaler feature that is capable of replicating pods, to increase high availability and avoid downtime. The autoscaling process will be tested by providing a load request for the service. Testing is done several times on each server. This study will compare the MongoDB service that was built monolithically with a micro Kubernetes cluster, and with HPA features and without HPA features by paying attention to several things. Based on Response Time, Response Code per Seconds, and CPU Usage, the results obtained are that the service built on a micro Kubernetes cluster with HPA features is the best, with a constant response time value below 100 ms, Response Code per Seconds reaches 500 threads per second. seconds, and CPU Usage in the range of 30 – 55%.
ANALISIS PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK SISTEM DETEKSI KATARAK Akmal Rusdy Prasetyo; Sussi; Bagus Aditya
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol 3 No 1 (2023): Maret, Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Sekolah Tinggi Ilmu Ekonomi Trianandra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juritek.v3i1.604

Abstract

One of the most vital senses for humans is sight. Humans utilize their eyes to take in visual information that is used for a variety of tasks, but vision problems are prevalent, ranging from minor disorders to serious disorders that can result in blindness. Cataracts are one of the factors contributing to this vision loss. In Indonesia, cataracts alone account for 81 percent of cases of blindness, and 40 percent of those affected don't even realize they have them. A technique for early cataract identification using digital photographs is one way to lower the incidence of cataract-related blindness. We used Support Vector Machine (SVM) and Convolutional Neural Network (CNN) techniques to create a cataract detection system. These two techniques are used to categorize normal eye classes, immature cataracts, and mature cataracts. A digital image that has been downsized to 64x64 pixels in Joint Photographic Group (JPG) format that was taken from earlier study serves as the input data. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) methods used in the cataract detection process provide optimal results. The Support Vector Machine (SVM) method itself produces an accuracy value of 96.67%, while the Convolutional Neural Network method produces a better accuracy value of 98.89%. Keywords: Cataract, Digital Image, Machine Learning, Support Vector Machine, Convolutional Neural Network
Implementasi Deteksi Citra Termal untuk protokol pencegahan Covid19 di Desa Sukapura Aji Gautama Putra; Doan Perdana; Bagus Aditya; Sidik Prabowo
Prosiding COSECANT : Community Service and Engagement Seminar Vol 1, No 2 (2021)
Publisher : Universitas telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (427.43 KB) | DOI: 10.25124/cosecant.v1i2.17516

Abstract

Kantor Desa Sukapura adalah pusat administrasi dan kepemimpinan Desa Sukapura yang beralamat di Jl. Sukapura No.54, Sukapura, Kec. Dayeuhkolot, Bandung, Jawa Barat 40267. Kepala Desa Sukapura adalah H.Ganjar Sukma Wibawa, A.Md. Jarak Kantor Desa Sukapura dari Kampus Telkom University adalah 500 meter. Di masa Pandemic COVID-19, Kantor Desa Sukapura sudah menjalankan beberapa protokol di antara nya adalah; menjaga jarak, mencuci tangan, menggunakan masker, dan WFH Sebagian Sudah dilakukan pengukuran suhu tubuh sebagai salah satu Protokol Pencegahan COVID-19, di mana setiap pegawai atau pengunjung yang datang ke Kantor Desa Sukapura harus mempunyai suhu tubuh normal atau di bawah 37,5oC. Namun akurasi pengukuran suhu masih bisa ditingkatkan dan bisa diterapkan pengukur suhu yang multi-person. Selain itu pegawai di Kantor Desa Sukapura juga perlu upgrading skill dan pengetahuan terkait penggunaan teknolologi untuk pengukuran suhu. Oleh karena itu pada Pengabdian kepada Masyarakat (PkM) Community Service Engagement (CSE) ini diusulkan untuk dilakukan pendampingan desa berupa perancangan dan implementasi Pemeriksaan Suhu Badan dengan Citra Termal Sesuai Protokol COVID-19 di Kantor Desa Sukapura. Selain itu agar pegawai dapat menggunakan teknologi tersebut dilakukan penyuluhan terkait penggunaan pemeriksaan suhu badan dengan citra termal sesuai protokol COVID-19 di Kantor Desa Sukapura.
The Average Value Algorithm from The Distance Matrix for Traveling Salesman Problem Candra Setiawan; Bagus Aditya; Agustina Heryati
Jurnal Ilmiah Informatika Global Vol. 14 No. 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i1.3014

Abstract

The Traveling Salesman Problem (TSP) is a popular problem, but until now there is no algorithm that has the same search results as brute force with a fast search time. Many algorithms have been made previously related to solving this problem with the aim of finding the shortest route through a number of nodes to finally return to the initial node. The purpose of this research is to create an algorithm that can optimize the search for the shortest route with a fast search time. The approach taken is to find the average value of the distance matrix and look for routes with links that have values below the average value. Each route that has been passed will be marked and compared so that it can facilitate the search with a shorter processing time. In this paper the best and effective routes are limited to 12 nodes. The results obtained show that the Average Score Algorithm provides a relatively stable processing time from node 4 to node 12. The proposed algorithm has a tendency of decreasing processing capacity with increasing number of nodes.
Analisis Perbandingan Algoritma Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) untuk Sistem Deteksi Katarak Akmal Rusdy Prasetyo; Sussi Sussi; Bagus Aditya
eProceedings of Engineering Vol 10, No 4 (2023): Agustus 2023
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mata adalah salah satu indera terpenting bagi manusia. Melalui mata, manusia menyerap informasi visual yang digunakan untuk melakukan berbagai aktivitas, tetapi gangguan terhadap penglihatan banyak terjadi, dari gangguan ringan hingga gangguan serius yang dapat menyebabkan kebutaan, kebutaan. Salah satu penyebab gangguan penglihatan ini adalah katarak. Katarak sendiri menjadi penyebab tertinggi kebutaan di Indonesia (81%) dengan 40% penderitanya tidak mengetahui bahwa dirinya menderita katarak. Salah satu solusi untuk mengurangi prevalensi kebutaan yang disebabkan oleh penyakit katarak yaitu dengan sistem deteksi dini penyakit katarak dengan memanfaatkan citra digital. Pada Tugas Akhir ini, dirancang sistem deteksi katarak dengan metode Support Vector Machine (SVM) dan Convolutional Neural Network (CNN). Kedua metode ini digunakan sebagai metode klasifikasi atas kelas mata normal, katarak imatur dan katarak matur. Data masukan berupa citra digital yang sudah di resize menjadi 64x64 pixel berformat Joint Photographic Group (JPG) yang diperoleh dari penelitian sebelumnya. Metode Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) yang digunakan dalam proses deteksi penyakit katarak memberikan hasil yang optimal. Metode Support Vector Machine (SVM) sendiri menghasilkan nilai accuracy sebesar 96.67%, Sedangkan untuk Metode Convolutional Neural Network menghasilkan nilai accuracy yang lebih baik sebesar 98.89%.Kata kunci—katarak, citra digital, machine learning, support vector machine, convolutional neural network
Deteksi Saturasi Oksigen dalam Darah Menggunakan Sensor MAX30100 Berbasis ESP8266 Adam Fauzan Ahmad; Bambang Setia Nugroho; Bagus Aditya
eProceedings of Engineering Vol 10, No 4 (2023): Agustus 2023
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pada era modern saat ini, Internet of Things berkembang sangat pesat dalam berbagai bidang, termasuk dalam bidang kesehatan. Kemajuan teknologi menyebabkan beberapa dampak untuk kesehatan, salah satunya adalah membuat tubuh kekurangan oksigen dan menurunnya kesehatan jantung. Alat yang digunakan untuk mendiagnosa tubuh seseorang kekurangan oksigen dan juga detak jantung adalah oximeter. Pada penilitian ini digunakan sensor MAX30100 untuk mengukur saturasi oksigen dalam tubuh dan ESP8266 untuk menginterfacekan pada smartphone berbasis android menggunakan ESP8266. Jika data yang terdeteksi tidak normal dan butuh tindakan maka SIM800L akan langsung mengirimkan pesan pada nomor darurat yang terdaftar. Pada saat pengujian sensor MAX30100 metode PPG reflectance terbukti akurat pada deteksi saturasi oksigen . Yang kedua performansi QoS dipagi hari mendapat nilai rata-rata delay sebesar 33,971894 ms, throughput 1841,4 bps dan packet loss 0%. Sedangkan nilai rata-rata performansi QoS disore hari yang kurang baik yaitu mendapatkan hasil nilai rata-rata delay 47,85524 ms, throughput 1669,9 bps dan packet loss 0%.Kata kunci— ESP8266, MAX30100, SIM800L, oximeter, QoS.
IMPLEMENTASI DAN ANALISIS MIGRASI DATA LMS PADA KLASTER KUBERNETES ANTAR-PUBLIC CLOUD MENGGUNAKAN BACKUP DAN RESTORE Adimas Fachri Ranunegoro; Favian Dewanta; Bagus Aditya
MULTINETICS Vol. 9 No. 1 (2023): MULTINETICS Mei (2023)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v9i1.5556

Abstract

Moodle is one of the media for learning management systems that are widely used today because non-face-to-face learning is unavoidable. Fluctuating user traffic makes moodle suitable for deployment to a public cloud. Public clouds are easier to scale, especially when combined with a container orchestrator such as Kubernetes. However, there are times when it is necessary to migrate data on a Kubernetes cluster from a public cloud to another public cloud to mitigate disasters in a specific region in the public cloud. Moodle applications can be routed to different regions, but this will cause high latency. This problem can be solved by migrating the Kubernetes cluster on Google Cloud to the same region on Microsoft Azure as an alternative public cloud. This final project will discuss the migration of a kubernetes cluster along with persistent volume data between public clouds from Google Cloud Platform to Microsoft Azure and vice versa using backup and restore methods. Velero is used as a backup and restore tool, then the restic plugin is added so that Velero can also backup and restore persistent volumes located outside the Kubernetes cluster. The test results show that Velero with the restic plugin can backup and restore persistent volumes outside the cluster. The larger the data size, the longer the backup, restore, and migration time will be. Backup and restore time for each incremental size of approximately 500 MB will increase the backup and restore time by approximately 10 seconds. Meanwhile, on the utility side, the amount of CPU usage during restore consumes more resources than when backup. At the time of backup, the maximum CPU spike was 3.5% at 3 GB data size in both public cloud clusters. Meanwhile, at the time of restore, the maximum CPU spike is 5% at 3 GB of data size.
Co-Authors Adam Fauzan Ahmad Adimas Fachri Ranunegoro Agustina Heryati Aji Gautama Putra Akmal Rusdy Prasetyo Anita Rosdina Nasution Bambang Setia Nugroho Candra Setiawan Christoporus Ivan Samuels Doan Perdana Favian Dewanta Galih Nugraha Nurkahfi Sidik Prabowo Sussi