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PENGUKURAN PSNR PADA TRANSMISI VIDEO DI KANAL TERAHERTZ MENGGUNAKAN QAM MODULATION Tamara Maharani; Muhammad Agus Zainuddin; Sritrusta Sukaridhoto
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 2 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i2.319

Abstract

In the current era of communication has various challenges that include the intensity of information exchange more often, the amount of information carried and the speed in exchanging information. Communication is not only in the form of text and sound but also in the form of pictures and videos. This study tries to use digital data in the form of video with the aim of providing a view of the PSNR measurement simulation. The method used is modulation of QAM 64, 256, 1024 and 4096 through terahertz channels (0.1-10 THz). Simulation results show that in QAM 64 the PSNR value is 35.2 dB to 36.6 dB. The PSNR value decreases as the M-ary increases. PSNR at 256 QAM ranges from 25.9 to 26.5 dB. PSNR in QAM 1024 is stable at magnitude 16.3 to 16.5. Whereas PSNR in QAM 4096 ranged from 15.0 to 15.25. From this study shows the greater the value of PSNR, the quality of information sent is increasingly similar. In addition, the higher the M-ary, the data carried will also be large so as to speed up the transmission time.Keywords: Terahertz, QAM, PSNR, Video, Simulation Di era saat ini komunikasi memiliki berbagai tantangan yang meliputi intesitas pertukaran informasi yang lebih sering, besarnya informasi yang dibawa dan kecepatan dalam bertukar informasi. Komunikasi tidak hanya berupa text dan suara namun juga berupa gambar dan video. Penelitian ini mencoba menggunakan data digital berupa video dengan tujuan memberikan pandangan tentang simulasi pengukuran PSNR. Metode digunakan yaitu modulasi QAM 64, 256, 1024 dan 4096 melalui kanal terahertz (0.1-10 THz). Hasil simulasi menunjukkan pada QAM 64 nilai PSNR sebesar  35.2 dB hingga 36.6 dB. Nilai PSNR menurun seiring bertambahnya M-ary. PSNR pada QAM 256 di rentang 25.9 hingga 26.5 dB. PSNR pada QAM 1024 stabil di besaran 16.3 sampai 16.5. Sedangkan PSNR pada QAM 4096 di rentang 15.0 hingga 15.25. Dari penelitian ini menunjukkan semakin besar nilai PSNR maka kualitas informasi yang dikirimkan semakin mirip. Selain itu semakin tinggi M-ary maka data yang dibawa pun juga ikut besar sehingga mempercepat waktu transimisi. Kata kunci: Terahertz, QAM,PSNR, Video, Simulasi
PENGEMBANGAN SOLAR PHOTOVOLTAIC SYSTEM DENGAN MPPT UNTUK TEKNOLOGI IOT- SEMAR Putra Asmara Danu; Erik Tridianto; Sritrusta Sukaridhoto
PROSIDING SNAST Prosiding SNAST 2018
Publisher : IST AKPRIND Yogyakarta

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

Abstract

Photovoltaic system is one of renewable energy applied to industrial equipments and public facilities. The case study in this study was demonstrated by the design of photovoltaic systems on the Internet of Things (IoT) technology of Smart Environment Monitoring and Real Time System Analytics (SEMAR). IoT - SEMAR is designed on a ship prototype consisting of motor drives, servo motors, and raspberry pi. Photovoltaic system design process is done by considering location, electric energy consumption per day, and solar radiation with the aim of obtaining efficient design. From the calculation obtained solar photovoltaic capacity of 50 Wp / 18 V, with a storage battery of 17 Ah / 12 V. The results obtained by considering the accumulation of power consumption per day multiplied by a correction factor of 1.3. Based on the measurement, the driving force requires energy of 51 Wh, a servo motor of 1.5 Wh, and raspberry pi of 52 Wh so that the energy requirement per day is obtained by 105 Wh. For more effective electrical energy absorption, solar charge is used with Maximum Power Point Tracking (MPPT) with 5 A capacity. The test result through direct hardware application shows that the design with the addition of MPPT solar charge can produce 140 Wh / day, which is enough to operate IoT - SEMAR non-stop. This procedure is compatible for designing off-grid photovoltaic applications, particularly those located in remote locations.
Monitoring Detak Jantung untuk Atlet Lari 100 Meter Berbasis Internet of Things Muhammad Aksa Hidayat; Sritrusta Sukaridhoto; Achmad Basuki; Muhammad Fajrul Falah
INTEK: Jurnal Penelitian Vol 6, No 2 (2019): October 2019
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (569.539 KB) | DOI: 10.31963/intek.v6i2.1563

Abstract

Abstract— At the 2018 ASEAN Games, Indonesia won 98 medals, the number of medals exceeded the expected target, this achievement could not be separated from the role of coaches who always monitor the condition of their athletes while training on the field. One of the conditions of monitoring athletes can be done by monitoring the heartbeat activity of each athlete during exercise. In this study the authors made a heart rate monitoring tool for IoT 100 meter runners that can be used in the field and send data in real time. Polar Heart Rate heart rate sensor is good to use because the data reading error is 0.4%. 2. heart rate monitoring can only communicate up to 70 meters more. Data entered and read well into the server.
Mobile Platform Biometric Cloud Authentication Agostinho Marques Ximenes; Sritrusta Sukaridhoto; Amang Sudarsono; Hasan Basri
INTEK: Jurnal Penelitian Vol 6, No 2 (2019): October 2019
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (488.38 KB) | DOI: 10.31963/intek.v6i2.1525

Abstract

Berdasarkan data Pusat Statistik Indonesia, tingkat kemiskinan pada bulan September 2018 adalah 25,95 juta, berdasrkan data tingkat kemiskinan masyarakat  tersebut  pemeritnah menyalurkan dana bantuan mengatasi tingkat kemiskinan masyarakat melalui Bank. Namun, Bank tidak dapat mengalokasikan dana karena biaya untuk membangun infrastruktur mahal, seperti membuat ATM.Berbagai kendala tersebut, Bank perlu menemukan solusi baru agar dapat mengalokasikan dana kepada masyrakat dengan biaya yang murah, Mobile Platform Biometric Cloud Authentication adalah salah satu solusi. Dalam penelitian ini, eksperimen yang dilakukan melakukan autentikasi dengan QR Code Scan dan face recognize (data face dienkripsi dan didekripsi dengan kritografi algoritma AES 256 bit). Konsentrasi penelitian ini terletak pada  eksperimen terhadap komunikasi keamanan data transaksi payment  merchant onlie degan QR Code scan dan Face recognize yang berbasis mobile android dan serta spesfikasi android versi 23. Hasil pengujian pada aplikasi Mobile ini menunjukkan bahwa QR Code scan dan face recognize dapat diimplementasikan pada transaksi payment merchant online dengan akurasi 95% dan membutuhkan 53, 21 detik per transaksi.
High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease Evianita Dewi Fajrianti; Afis Asryullah Pratama; Jamal Abdul Nasyir; Alfandino Rasyid; Idris Winarno; Sritrusta Sukaridhoto
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.793

Abstract

In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.
Comparison of cloud computing providers for development of big data and internet of things application Muhammad Fajrul Falah; Yohanes Yohanie Fridelin Panduman; Sritrusta Sukaridhoto; Arther Wilem Cornelius Tirie; M. Cahyo Kriswantoro; Bayu Dwiyan Satria; Saifudin Usman
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1723-1730

Abstract

The improved technology of big data and the internet of things (IoT) increases the number of developments in the application of smart city and Industry 4.0. Thus, the need for high-performance cloud computing is increasing. However, the increase in cloud computing service providers causes difficulties in determining the chosen service provider. Therefore, the purpose of this study is to make comparisons to determine the criteria for selecting cloud computing services following the system architecture and services needed to develop IoT and big data applications. We have analyzed several parameters such as technology specifications, model services, data center location, big data service, internet of things, microservices architecture, cloud computing management, and machine learning. We use these parameters to compare several cloud computing service providers. The results present that the parameters able to use as a reference for choosing cloud computing for the implementation of IoT and big data technology.