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All Journal Emitor: Jurnal Teknik Elektro Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Science and Advanced Engineering
Vidya Ikawati, Vidya
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Physics

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DESAIN DAN IMPLEMENTASI MODEL PEMBELAJARAN E-LEARNING DI PROGRAM STUDI TEKNIK ELEKTRO UNIVERSITAS 17 AGUSTUS 1945 CIREBON DENGAN MODULAR OBJECT ORIENTED DYNAMIC LEARNING ENVIRONMENT Ikawati, Vidya
Emitor Vol.15 No.01 Maret 2015
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Meningkatnya jumlah jaringan internet membuat dampak yang cukup berarti untuk bidang pendidikan. Dimana pada dasarnya pendidikan adalah proses komunikasi antara pendidik kepada peserta didik. Seiring dengan perkembangan zaman, jumlah kehadiran mahasiswa semakin sedikit dibandingkan dengan proses pendidikan berpuluh tahun yang lalu. Oleh karena itulah perlu dibuat alternatif media pembelajaran sebagai komplemen pembelajaran di kelas. Program Studi Teknik Elektro UNTAGCirebonsebagai salah satu penyelenggara pendidikan diIndonesiaberupaya mengatasi masalah ini dengan membuat media pembelajaran berupa e-learning yang proses pembuatannya berbasiskan Moodle dan MySQL.
Desain dan Implementasi Model Pembelajaran E-Learning di Program Studi Teknik Elektro Universitas 17 Agustus 1945 Cirebon dengan Modular Object Oriented Dynamic Learning Environment Ikawati, Vidya
Emitor: Jurnal Teknik Elektro Vol 15, No 1: Maret 2015
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v15i1.1754

Abstract

Meningkatnya jumlah jaringan internet membuat dampak yang cukup berarti untuk bidang pendidikan. Dimana pada dasarnya pendidikan adalah proses komunikasi antara pendidik kepada peserta didik. Seiring dengan perkembangan zaman, jumlah kehadiran mahasiswa semakin sedikit dibandingkan dengan proses pendidikan berpuluh tahun yang lalu. Oleh karena itulah perlu dibuat alternatif media pembelajaran sebagai komplemen pembelajaran di kelas. Program Studi Teknik Elektro UNTAGCirebonsebagai salah satu penyelenggara pendidikan diIndonesiaberupaya mengatasi masalah ini dengan membuat media pembelajaran berupa e-learning yang proses pembuatannya berbasiskan Moodle dan MySQL.
Face Recognition-Based Door Lock Security System Using TensorFlow Lite Septyanlie, Vrazsa Viantyezar; Ikawati, Vidya; Subiyanta, Erfan; Lestari, Nina
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 2 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i2.9557

Abstract

A door security system utilizing face recognition technology based on TensorFlow Lite has been developed to enhance access security and convenience. This research aims to design a system capable of accurately recognizing faces in real time, integrating it with door lock devices, and ensuring user data security. Employing the waterfall method, the system was implemented using an ESP32-CAM microcontroller and deep learning algorithms. Testing results demonstrated a face recognition accuracy of 91% in identifying and processing commands from 200 trials with ten facial variations. Successful integration with door lock devices was achieved through serial communication. The system also features activity log recording for monitoring purposes. This solution offers greater practicality and security than RFID systems as it eliminates the need for physical cards. This research contributes to developing more sophisticated and user-friendly home security systems, with the potential for further enhancements in recognition capabilities under various lighting conditions and integration with other biometric technologies
Prediction of Covid-19 Disease Using X-Ray Images with Deep Learning Algorithm Ikawati, Vidya; Yoeni, Indrasary; Prihatmanto, Ary Setijadi
Journal of Applied Science and Advanced Engineering Vol. 1 No. 1 (2023): JASAE: March 2023
Publisher : Master Program in Mechanical Engineering, Gunadarma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59097/jasae.v1i1.11

Abstract

The capacity of Indonesian medical personnel, especially pulmonary and radiology specialists, is still far from the proportionate ratio of Indonesia's population. This limitation is one of Indonesia's main issues in realizing adequate health services for lung sufferers. Furthermore, the diagnosis process is one of the keys to obtaining appropriate and fast treatment procedures for sufferers. This paper will review the research conducted by the PPTIK ITB team in developing a tool for diagnosing lung disease with the help of Deep Learning. In this study, deep learning models play a role in classifying diseases based on an X-Ray image of the lungs. At this stage, the performance of three deep learning models, ResNet50, ResNet101, and VGG19, will be compared in classifying COVID-19, Pneumonia, and tuberculosis. The performance metrics to be compared include accuracy, precision, recall, and F1 score. The test results show that, on average, the VGG19 model gives the best results on the four performance metrics compared to the other two models.
Co-Authors Ary Setijadi Prihatmanto Erfan Subiyanta, Erfan Lestari, Nina Septyanlie, Vrazsa Viantyezar Yoeni, Indrasary