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All Journal Jurnal Sinergitas PkM & CSR Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Technology
Rangga Winantyo, Rangga
Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu 432-8011, Japan
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Improvement in Performance of ZnO based DSC Prepared by Spraying Method Winantyo, Rangga; Liyanage, Devinda; Murakami, Kenji
Makara Journal of Technology Vol. 17, No. 2
Publisher : UI Scholars Hub

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Abstract

This paper reports the effect of TiCl4 on the performance of ZnO based DSC. ZnO was used due to its stability against photo-corrosion and photochemical properties similar to TiO2. Thin films of nanocrystalline ZnO were deposited on transparent conducting oxide glass using spray method. The ZnO films were treated using TiCl4. The cell’s efficiency was found to be 2.5% with TiCl4 post-treatment and 1.9% without TiCl4 post-treatment.
ALAT PENGUKUR KESEHATAN UNTUK DETEKSI AWAL GEJALA COVID-19 DI PUSKESMAS KELAPA DUA Pura, Megantara; Muharrom, Ahmad Syahril; Nugraha, M.B; Winantyo, Rangga; Tampubolon, Marojahan; Tanex, Kevin Nicholas; Kristi, David
Jurnal Sinergitas PKM & CSR Vol. 6 No. 2 (2022): OCTOBER
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/jspc.v6i2.6151

Abstract

Sebagai bentuk kepedulian terhadap pencegahan penyebaran virus COVID-19, dibuat alat pengukur kesehatan yang akan diaplikasikan di puskesmas. Alat yang dibuat dapat mengukur temperatur tubuh, detak jantung, dan kadar oksigen dalam darah menggunakan teknik photoplethysmography. Alat ini juga berfungsi untuk menghubungi petugas puskesmas bila kondisi kesehatan pasien menurun menggunakan sistem short messaging services (SMS). Mikrokontroller Arduino Mega 2560 digunakan sebagai penerima data dan kendali sistem. Sensor yang digunakan adalah sensor temperatur MLX90614 dan sensor kadar oksigen MAX30102. Sistem pengiriman SMS menggunakan board SIM808. Alat ini beroperasi menggunakan sumber listrik AC, dengan baterai Li-ion sebagai cadangan catu daya. Pengujian alat dilakukan di pintu masuk Laboratorium Teknik Elektro Universitas Multimedia Nusantara. Setiap mahasiswa yang datang ke laboratorium tersebut wajib melakukan cek kesehatan menggunakan alat tersebut. Selama masa pengujian, alat dapat mendeteksi temperatur dan kadar oksigen mahasiswa dengan tepat, serta alat mampu bertahan selama satu hari penuh hanya dengan menggunakan baterai. Alat ini akan diletakkan dan diaplikasikan di Puskesmas Kelapa Dua Tangerang.
Deepfake detection using convolutional neural networks: a deep learning approach for digital security Twince Tobing, Fenina Adline; Kusnadi, Adhi; Pane, Ivransa Zuhdi; Winantyo, Rangga
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1092-1099

Abstract

The development of artificial intelligence technology, especially deep learning, has facilitated the emergence of increasingly sophisticated deepfake technology. Deepfakes utilize generative adversarial networks (GANs) to manipulate images or videos, making it appear as if someone said or did things that never actually happened. As a result, deepfake detection has become a critical challenge, particularly in the context of the spread of false information and digital crime. The purpose of this research is to create a method for detecting deepfakes using a convolutional neural network (CNN) approach, which has been proven effective in visual pattern recognition. Through training with a dataset of original facial images and deepfakes, the CNN model achieved an accuracy of 81.3% in detecting deepfakes. The evaluation results for metrics such as precision, recall, and F1-score indicated good performance overall, although there is still room for improvement. This study is expected to make a significant contribution to enhancing digital security, especially in detecting visual manipulations based on deepfakes.
Co-Authors Devinda Liyanage, Devinda Kenji Murakami Kristi, David kusnadi, adhi Marojahan Tampubolon Muharrom, Ahmad Syahril Nugraha, M.B Pane, Ivransa Zuhdi Pura, Megantara Tanex, Kevin Nicholas Twince Tobing, Fenina Adline