Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Pelayanan Hubungan Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yusup Miftahuddin
Institut Teknologi Nasional
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Perbandingan Metode EfficientNetB3 dan MobileNetV2 Untuk Identifikasi Jenis Buah-buahan Menggunakan Fitur Daun: Metode EfficientNetB3 dan MobileNetv2 fiqry zaelani; Yusup Miftahuddin
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 9 No. 1 (2022)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jitter.vol9.iss1.2022.911

Abstract

Today's technology is growing rapidly in various fields. One of the benefits of technological developments are helping human to work in various fields, for instance, in the field of plantations, development for the quality of fruits and even in the field of education. One of them is identifying the types of fruits that are needed by citizen even children. They can easily distinguish the types of fruits by looking at the shape of the leaves, so that it can help to increase their knowledge about fruits. For ordinary people, it must be quite difficult to know what kind of fruit on its leaf. Therefore, this study proposes a Convolution Neural Network proposal by comparing the architecture of EfficientNet-B3 and MobileNet-V2 by setting several parameters to get the best accuracy value in detecting fruit types using leaf features. EfficientNet-B3 and MobileNet-V2 are pre-trained models from CNN that tell a fairly large dataset, namely ImageNet. The results obtained from this study are applied several parameters such as the use of epoch, optimizer Adam, optimizer Adamax, optimizer sgd, bathsize. For EfficientNet-B3 epoch 20 optimizer sgd produces an accuracy of 0.2370 or 23%, while EfficientNet-B3 epoch 50 optimizer Adamax produces an accuracy of 0.3051 or 30%. In addition, research on the MobileNet-V2 epoch 20 optimizer Adam resulted in an accuracy of 0.9914 or 99%, while the MobileNet-V2 epoch 50 optimizer Adamax resulted in an accuracy of 0.9860 or 98%. Keywords: Leaf, Convolution Neural Network, EfficientNet-B3, MobileNet-V2
Aplikasi Pengenalan Alat Musik Pada Anak Usia Dini Yusup Miftahuddin; Marisa Premitasari; Daniel Barus; Fakhrudin Rizky Husaini; Noer Fadillah
Jurnal Pelayanan Hubungan Masyarakat Vol. 1 No. 2 (2023): Juni : Jurnal Pelayanan Hubungan Masyarakat
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jphm-widyakarya.v1i2.735

Abstract

The development of music from the past until now continues to grow, from traditional musical instruments to modern musical instruments that are currently widely used. However, today's children or adolescents are more interested in modern musical instruments such as guitars, saxophones, violins and other modern musical instruments than traditional musical instruments such as suling, gendang and other traditional musical instruments. So it is necessary to teach from an early age by introducing both traditional and modern musical instruments. By taking advantage of technological developments, especially in multimedia technology, we need a system that can facilitate teaching through computers or commonly called CAI (Computer Assisted Instruction). This was done because the Mekar Arum Kindergarten children were still not interested in musical instruments. So we need an application to introduce musical instruments that can be used in learning while playing to attract children's attention. With this application, it can make it easier for teachers to teach introduction to musical instruments for young children without having to meet children at kindergartens and make it easier for children to recognize musical instruments and be more interested in traditional and musical instruments modern.
Implementasi Algoritma Speeded Up Robust Features (SURF) Pada Pengenalan Rambu – Rambu Lalu Lintas Firma Firmansyah Adi; Muhammad Ichwan; Yusup Miftahuddin
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 3 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i3.697

Abstract

One factor of increased violations on the highway is a violation of traffic signs, because the signs are not visible to the driver. In addition to this the conditions on road signs attached to the road have shortcomings such as twisting signs, imperfect beacons and non-standard beacons. So to be able to reduce the violation of traffic signs required a system that can recognize traffic. To be able to recognize traffic signs can be done visually and must be fast in recognizing. The image to be recognized can use the camera to retrieve information from signposts then the image is extracted with features of Speeded Up Robust Features (SURF) algorithm consisting of three stages: interest point detection, scale space, feature description and feature matching so that the system can recognize traffic signs. The research that has been done has resulted that the SURF algorithm in recognizing traffic signs is good enough to be the algorithm of introduction of traffic signs with the need to be fast and accurate. In addition, this algorithm is invariant to scale and invariant to rotation, so that the difference of slope and scale difference can still be recognized by using SURF algorithm
Co-Authors Daniel Barus Fakhrudin Rizky Husaini fiqry zaelani Firma Firmansyah Adi Marisa Premitasari Muhammad Ichwan Noer Fadillah