Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Sinergi EMITTER International Journal of Engineering Technology INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) ACPES Journal of Physical Education, Sport, and Health
Tjahyaningtijas, Hapsari Peni Agustin
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
QR Code Technology Based Laboratory User Attendance to Improve Study Program Governance Tjahyaningtijas, Hapsari Peni Agustin
INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Vol. 7 No. 1 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/inajeee.v7n1.p1-5

Abstract

QR codes are commonly employed in today's technologies. Surabaya State University's Department of Electrical Engineering uses the QR Code to track the presence of Electrical Engineering Laboratory users. hence far, attendance has been taken through manual scanning, hence laboratory members frequently do not take attendance. The usage of the QR Code for attendance has been carried out and tested by scanning the QR code. The QR code direct to google sheet link whereas the Laboratory used to send it to the head of the faculty. The Experiment of using QR Code where held in Telecommunication Laboratory with homogenous area of illumination and scanned at 50 cm, 100 cm, and 150 cm distances. The results demonstrate that QR codes sized 13.5 cm2 can still be scanned at a distance of 100 m but fail at distances greater than 100 cm by using smart phone. The use of QR codes for attendance in the Electrical Engineering Department has numerous advantages, including being efficient in order to establish good governance, efficient, paperless and contributing to a green environment
Indonesia rupiah currency detection for visually impaired people using transfer learning VGG-19 Alfatikarani, Raissa; Suciningtyas, Laras; Bimasakti, Genta Garuda; Mardhatillah, Faqisna Putra; Paragas, Jessie R.; Tjahyaningtijas, Hapsari Peni Agustin
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.022

Abstract

People with visual impairments often face difficulties in determining the authenticity of paper money, which is a crucial skill to avoid fraud. The limitations of traditional methods, like blind codes for visually impaired people, require a more advanced and efficient solution. Previous methods of currency detection using Convolutional Neural Network (CNN) techniques, including the VGG-19 architecture, have often encountered challenges, particularly the long training times required. Therefore, we propose using transfer learning techniques and modifying the top layers of the VGG-19 model, known as fully connected layers, within a mobile application with audio feedback built using Android Studio. These modifications involve substituting the three fully connected layers with dense and flattened layers. We also implemented hyperparameter tuning, including adjusting the batch sizes and setting the number of epochs. The datasets used Indonesian Rupiah paper currency from the 2022 emission year, specifically Rp 50,000 and Rp 100,000 denominations. The best transfer learning VGG-19 model achieved a batch size of 32 and an epoch of 50, resulting in a high accuracy of 88%. Response speed testing with performance profiling on Android Studio showed an overall average response time of 458 ms. The main advantage of using transfer learning with the VGG-19 model is that it significantly reduces training time while still achieving high accuracy, differentiating this work from previous studies that relied on training from scratch, which is more time-consuming and resource-intensive. Therefore, this mobile app can be categorized as having a fast response time.
Correlation Between Limb Length and Muscle Mass of The Lower Extremity Firmansyah, Awang; Tjahyaningtijas, Hapsari Peni Agustin; Rasy, Ahmad Hafizh Ainur; Budijono, Agung Prijo; Putro, Andika Bayu; Putra, I Wayan Valentino Eka
ACPES Journal of Physical Education, Sport, and Health (AJPESH) Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Negeri Semarang (UNNES) in cooperation with ACPES (ASEAN Council of Physical Education and Sport)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ajpesh.v4i2.21461

Abstract

Introduction: Muscle mass has a contribution to body strength. One third of the human body is part of the lower extremity. The limbs are part of the lower extremity. This study aims to investigate the length of the limb with muscle mass. Methods: Cross-sectional study with the subjects of this study were 39 students (12 females and 27 males) with male characteristics (age 20.03±0.7, height 168±5.46, weight 61.92±10.98, BMI 22±4.24) while in women (age 20.16±0.83, height 158.83.3±5.13, weight 54.7±12.02, BMI 21.65±4.51). Pearson correlation from SPSS version 26 with a significance level of P-value < 0.05. Findings: Based on the data, there was a signifi correlation between the length of the leg and muscle mass on both the right (P-value: 0.000, r: 0.634) and the left (P-value: 0.000, r: 0.629). Conclusion: This study proves that the longer the limbs a person has, the greater the muscle mass he has.
Automatic Segmentation on Glioblastoma Brain Tumor Magnetic Resonance Imaging Using Modified U-Net Tjahyaningtijas, Hapsari Peni Agustin; Nugroho, Andi Kurniawan; Angkoso, Cucun Very; Purnama, I Ketut Edy; Purnomo, Mauridhi Hery
EMITTER International Journal of Engineering Technology Vol 8 No 1 (2020)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v8i1.505

Abstract

Glioblastoma is listed as a malignant brain tumor. Due to its heterogeneous composition in one area of the tumor, the area of tumor is difficult to segment from healthy tissue. On the other side, the segmentation of brain tumor MRI imaging is also erroneous and takes time because of the large MRI image data. An automated segmentation approach based on fully convolutional architecture was developed to overcome the problem. One of fully convolutional network that used is U-Net framework. U-Net architecture is evaluated base on the number of epochs and drop-out values to achieve the most suitable architecture for the automatic segmentation of glioblastoma brain tumors. Through experimental findings, the most fitting architectural model is mU-Net architecture with an epoch number of 90 and a drop out layer value of 0.5. The results of the segmentation performance are shown by a dice value of 0.909 which is greater than that of the previous research.
Co-Authors Agung Prijo Budijono Alfatikarani, Raissa Andi Kurniawan Nugroho Angkoso, Cucun Very Bimasakti, Genta Garuda Firmansyah, Awang Mardhatillah, Faqisna Putra Mauridhi Hery Purnomo Paragas, Jessie R. Purnama, I Ketut Edy Putra, I Wayan Valentino Eka Putro, Andika Bayu Rasy, Ahmad Hafizh Ainur Suciningtyas, Laras