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All Journal Petir Kilat TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri
Rosida Nur Aziza
Informatika - Institut Teknologi PLN
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Mechanical Engineering

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Klasifikasi Sinyal EEG Menggunakan Model K-Nearest Neighbor Untuk Pengenalan Kata Yang Dibayangkan Abdul Rauf; Efy Yosrita; Rosida Nur Aziza
PETIR Vol 15 No 1 (2022): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v15i1.1335

Abstract

Locked in syndrome (LIS) is a condition of complete paralysis in which people with LIS are conscious but unable to move or communicate verbally except to move their eyes or blink. One way that can help LIS sufferers to communicate and interact is through recording brain signals called Electroencephalogram (EEG). In this study, the data from the recording of the EEG signal has gone through the extraction stage. The extracted data is preprocessed and classified using the K-Nearest Neighbor (K-NN) algorithm to be visualized using a web-based application. The results of the classification using the K-Nearest Neighbor algorithm with a value of K = 1 resulted in 82% accuracy, 82% precision and 82% recall. Keywords: LIS, EEG, K-Nearest Neighbor.
Teknologi Content Management System (CMS) Dinamis untuk Pengembangan Aplikasi Penerimaan Siswa Baru (PSB) SDIT Yasir Cipondoh Rahma Farah Ningrum; Rosida Nur Aziza; Puji Catur Siswipraptini; Abdul Haris; Karina Djunaidi; Riki Ruli A. Siregar; Efy Yosrita
Terang Vol 4 No 2 (2022): TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/terang.v4i2.1466

Abstract

Penerimaan Siswa Baru adalah kegiatan tahunan yang diselenggarakan oleh semua sekolah pada setiap tahun ajaran baru, tidak terkecuali SDIT Yasir yang terletak di Cipondoh Tangerang. Untuk menjangkau para calon siswa yang berdomisili diluar Cipondoh, diperlukan suatu pengembangan aplikasi Penerimaan Siswa Baru yang berbasis web dengan semua fitur standar yang diperlukan untuk memudahkan calon siswa baru dan pihak sekolah. Terdapat beberapa fitur menu diantaranya registrasi, verifikasi pembayaran, jadwal tes dan laporan pendaftaran siswa baru.
Implementasi Algoritma Convolutional Neural Network Dalam Mengklasifikasi Kesegaran Buah Berdasarkan Citra Buah Femil Paraijun; Rosida Nur Aziza; Dwina Kuswardani
KILAT Vol 11 No 1 (2022): KILAT
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/kilat.v10i2.1458

Abstract

The development of Information Technology today, which continues to grow, can help overcome various problems because matters relating to the advancement of Information Technology have spread to almost all levels of Indonesian society. Along with the development of Information Technology, it is also marked by Artificial Intelligence which can simulate human intelligence and help handle tasks in the real world. By utilizing Information Technology, one of them can be used in terms of the classification of fruit freshness. Where this classification will be very useful and help farmers and fruit consumers. This study describes the use of the Convolutional Neural Network to classify the freshness of the following fruits: apples, oranges, and bananas. And also using six classes, namely fresh apples, fresh oranges, fresh bananas, unfresh apples, unfresh oranges, and unfresh bananas. The first thing to do is Convolutional Neuronal Network training using an image dataset as input using data sources from Kaggle.com, published by "Student at Stony Brook University, New York, United States". To determine the performance of the various models produced, the following Confusion Matrix is used: accuracy, precision, and recall. The best average obtained is 93%.
Co-Authors Abdul Rauf Djunaidi, Karina Dwina Kuswardani Efy Yosrita Efy Yosrita Femil Paraijun Haris, Abdul Ningrum, Rahma Farah Puji Catur Siswipraptini Riki Ruli A. Siregar, Riki Ruli A.