Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor JUITA : Jurnal Informatika EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Applied Technology and Computing Science Journal Indonesian Journal of Electrical Engineering and Computer Science Prosiding SNAST Prosiding Seminar Nasional Pengabdian Kepada Masyarakat
Sritrusta Sukaridhoto
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : jurnal ilmiah kursor

 1 
Analisys and Implementation Cloud-based Biometricauthentication in Mobile Platform agostinho marques ximenes; Sritrusta Sukaridhoto; Amang Sudarsono; Hasan Basri
Jurnal Ilmiah Kursor Vol 10 No 2 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i2.200

Abstract

Based on the Indonesian Central of Statistics the level of poverty people in September 2018 was 25.95 million, based on data, the government allocation care fund the reduce poverty people, the fund are given through the bank. However, banks cannot allocation funds because the cost for build infrastructure is expensive, such as making an ATM. about that, the banks need to find a new solution to allocation care fund to the poverty people, Mobile Platform Biometric Cloud Authentication is one solution. In this study, the experimentationn of the biometric face recognized( face data enrypt and decript by algoritma AES 256 bit) to secure online payment mobile application based on the QR Code scan and face recognition[8,10]. The concentration of this study lies in the experimentationn of biometric face recognize and QR Code scan on biometric payment based face recognition and QR Code scan mobile applications that play a role in data communication security. The test results on this mobile application show that scanning a QR Code and biometric face recognize can be implemented at an online merchant transaction with an accuracy of 95% and takes 53, 21 seconds in transactions. Keyword: biometric, cloud server, Cryptography, QR Code.
THE IMPLEMENTATION OF HEART RATE SENSOR AND MOTION SENSORS BASED ON INTERNET OF THINGS FOR ATLETE PERFORMANCE MONITORING Sritrusta Sukaridhoto; Muhammad Aksa Hidayat; Achmad Basuki; Riyadh Arridha; Andi Roy; Titing Magfirah; Agus Prasetyo; Udin Harun Al Rasyid
Jurnal Ilmiah Kursor Vol 10 No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v10i1.208

Abstract

Indonesian achievements in the ASEAN Games continued to decline in achievement starting in 1962 with the acquisition of 51 medals and up to 2014 with the acquisition of 20 medals. The decline in achievement was due to the lack of athletic resources due to the absence of media that could record athletes' abilities in the field. Can record the athlete's performance before running, running and after running using the Heart Rate sensor and Motion Capture sensor. The results of the sensor recording will be stored in the database. This system applies the Internet of Things (IoT) concept, using raspberry pi, Arduino microcontroller, T34 polar heart rate sensor to capture and send heartbeat to receivers, gyro-based motion-capture sensors that named wear notch where this sensor serves to capture the movement of athletes, sensors communicate with the system using 4G connectivity, use MQTT as edge computing which acts as a communication medium from sensors to databases, Maria DB and influx DB as accumulation which plays a role in storing heart rate and athlete's movements that have been recorded by sensors, athlete performance monitoring platform with a heart rate sensor and athlete's motion capture is a web-based application that collaborates all processes from the sensor to the system. Sensor heart rate recording results are categorized good because the error margin is only 0.4%. Wearnotch sensor data can be stored in the database, and athletic data can be recorded before sports, while sports, and after sports in real-time
INTELLIGENT SYSTEM FOR AUTOMATIC CLASSIFICATION OF FRUIT DEFECT USING FASTER REGION-BASED CONVOLUTIONAL NEURAL NETWORK (FASTER R-CNN) Hasan Basri; Iwan Syarif; Sritrusta Sukaridhoto; Muhammad Fajrul Falah
Jurnal Ilmiah Kursor Vol 10 No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v10i1.187

Abstract

In 2018, the Indonesian fruit exports increased by 24% from the previous year. The surge in demand for tropical fruits from non-tropical countries is one of the contributing factors for this trend. Some of these countries have strict quality requirements – the poor level quality control of fruit is an obstacle in achieving greater export yield. This is because some exporters still use manual sorting processes performed by workers, hence the quality standard varies depending on the individual perception of the workers. Therefore, we need an intelligent system that is capable of automatic sorting according to the standard set. In this research, we propose a system that can classify fruit defects automatically. Faster R-CNN (FRCNN) architecture proposed as a solution to detect the level of defect on the surface of the fruit. There are three types of fruit that we research, its mangoes (sweet fragrant), lime, and pitaya fruit. Each fruit divided into three categories (i) Super, (ii) middle, (iii) and fruit defects. We exploit join detection and video tracking to calculate and determine the quality fruit in real-time. The datasets are taken in the field, then trained using the FRCNN Framework using the Tensorflow platform. We demonstrated that this system can classify fruit with an accuracy level of 88% (mango), 83% (lime), and 99% (pitaya), with an average computation cost of 0.0131 m/s. We can track and calculate fruit sequentially without using additional sensors and check the defect rate on fruit using the video streaming camera more accurately and with greater ease.
Co-Authors Achmad Basuki Adnan Rachmat Anom Besari agostinho marques ximenes Agus Prasetyo Agus Prayudi Ahmad Rifai Amang Sudarsono, Amang Andi Roy Arther Wilem Cornelius Tirie Budiarti, Rizqi Putri Nourma Dadet Pramadihanto Devaldi Akbar Dwi Kurnia Basuki Endang Sulistyani Erik Tridianto Funabiki Nobuo Hasan Basri Hasan Basri Heri Yulianus Herman Yuliandoko, Herman iwan Syarif Jordan Maulana M Udin Harun Al Rasyid, M Udin Harun M. Cahyo Kriswantoro Maulana, Jordan Muhammad Agus Zainuddin Muhammad Agus Zainuddin Muhammad Aksa Hidayat Muhammad Fajrul Falah Muhammad Iskandar Dzulqornain Nobuo Funabiki, Nobuo Oktafian Sultan Hakim Purwoko, Agus Putra Asmara Danu Rahardhita Widyatra Sudibyo Riyadh Arridha Satria, Bayu Dwiyan Septyan Diaz Anggoro Son Kuswadi Subhan Khalilullah, Achmad Tamara Maharani Titing Magfirah Vivien Arief Wardhany Yohanes Yohanie Fridelin Panduman