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All Journal Denta: Jurnal Kedokteran Gigi Jurnal Indonesia Sosial Teknologi INOVTEK Polbeng - Seri Informatika
Yoannita
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Computer Science & IT Dentistry Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Klasifikasi Jenis Bunga Menggunakan Metode Svm Berdasarkan Citra Dengan Fitur Hsv Mudita Chandra, Millenia; Yoannita
Jurnal Indonesia Sosial Teknologi Vol. 4 No. 02 (2023): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v4i02.585

Abstract

Bunga mempunyai banyak jenis yang beraneka ragam, dari warna, bentuk, ukuran, makna, dan lain – lain. Adapun bunga yang terkadang terlihat mirip sehingga sulit dibedakan, contohnya saja bunga matahari dan bunga daisy. Keduanya memiliki kelopak bunga berwarna kuning cerah, inti bunga yang mirip, memiliki kelopak bunga yang banya. berbentuk mekar, dan batang tumbuhan yang berwarna hijau. Dengan ciri – ciri yang sulit dibedakan tersebut, maka dilakukanlah penelitian dengan melakukan resize sebesar 320x240 piksel, kemudian citra akan di ekstraksi dengan fitur HSV. Algoritma Support Vector Machine yang digunakan dalam klasifikasi ini memiliki akurasi keseluruhan sebesar 63.66%.
Pengaruh Paparan Radiasi Telepon Genggam Terhadap Aktivitas Enzim Katalase Kelenjar Parotis Rattus norvegicus Strain Wistar Yoannita; Isidora Karsini; Syamsulina Revianti
Denta Journal Kedokteran Gigi Vol 10 No 2 (2016): Agustus
Publisher : Fakultas Kedokteran Gigi Universitas Hang Tuah

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Abstract

Background: Handheld mobile phones have become a cultural accessory device. Nevertheless, the use mobile phones has given rise to great concern because of possible adverse health effects from exposure to the radiofrequency radiation (RFR) emitted by the device. There have been plenty of researches reportedly establishing the damaging effect of radiofrequency radiation emitted from cell phone on the biological tissues in the body, but only several researches report the effect of handheld mobile phone radiation in oral cavity.Purpose: The aim of this experiment was to examine the difference effect of rat parotid gland catalase activity cause by low and high radiation handheld mobile phone. Material and Methods: This study was true experimental study with post test only control group design. 72male Wistar rats, 3 months old, with body weight 200 grams were used in this experiment, devided into 3 major group. Group 1 was negative contol group, group 2 was radiated by handheld mobile phone with low radiation, and group 3 was radiated by handheld mobile phone with high radiation 1 hour per day until 7 days, 14 days, and 21 days. Parotid gland catalase activity was measured by spectrophotometer λ 240. Data were analyzed by KruskallWallis and Mann-Withney test. Result: Low radiation handheld mobile phone was not altered catalase activity until 7, 14 and 21 days. High radiation handheld mobile phone was not altered catalase activity until 7and 14, but catalase activity was significantly increase at 21 days. Conclusion: Radiation emitted from the high radiadion handheld mobile phone until 21days was able to rise rat parotid gland catalase activity .
Implementation of MobileNetV4 and Efficient Channel Attention in Anti-Spoofing Face Attack Detection Rayvin Suhartoyo; Yoannita
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/kth2nc32

Abstract

Face Anti-Spoofing (FAS) is essential for preventing presentation attacks in biometric systems, yet deploying robust models on mobile devices remains a challenge due to computational constraints. This study proposes a lightweight FAS model integrating the MobileNetV4 architecture with an Efficient Channel Attention (ECA) module. The ECA mechanism is designed to enhance the network’s ability to detect subtle spoofing artifacts, such as texture anomalies, with negligible computational overhead. The model was evaluated using a dataset of 6,400 images, comprising both bona fide and attack presentations. Experimental results demonstrate robust performance, achieving an overall accuracy of 99.69%, 100% precision, and an Average Classification Error Rate (ACER) of 0.25%. Crucially, the model yielded a Bona Fide Presentation Classification Error Rate (BPCER) of 0.00%, ensuring that no genuine users are falsely rejected. While the baseline architecture provided a strong benchmark, the proposed attention-enhanced framework offers a viable trade-off between security and usability, providing a computationally efficient solution suitable for real-time mobile authentication.
Co-Authors Isidora Karsini Mudita Chandra, Millenia Rayvin Suhartoyo Syamsulina Revianti