Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Jurnal Manajemen & Supervisi Pendidikan Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Teknologi
Armin, Edmund Ucok
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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

Found 4 Documents
Search

 1 
MD PROX Alat Pendeteksi Benda Logam pada Cucian Laundry Menggunakan Arduino Uno dan Sensor Proximity Khan, Aziz; Armin, Edmund Ucok
Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Vol 10, No 1 (2024): April
Publisher : universitas teuku umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jmkn.v10i1.9343

Abstract

Besarnya pertumbuhan penduduk dan semakin meningkatnya kesibukan di kalangan pekerja menyebabkan semakin sedikitnya waktu yang dimiliki untuk melakukan berbagai pekerjaan rumah termasuk dalam mencuci pakaian. Oleh karena itu saat ini pada wilayah perkotaan yang mayoritas masyarakatnya bekerja sebagai pegawai buruh maupun kantoran lebih memilih untuk mencucikan pakaiannya nya pada jasa laundry untuk mempermudah mereka dalam menyeleesaikan pekerjaan rumah dengan praktis tanpa membutuhkan waktu seperti melakukan aktivitas mencuci pakaian sendiri. Hal ini menyebabkan usaha laundry mendapatkan banyak permintaan jasa mencuci pakaian dari para konsumen, yang mayoritas merupakan pekerja atau orang-orang sibuk yang tidak punya banyak waktu dalam melakukan pekerjaan rumah seperti mencuci pakaian. Selain itu juga para ibu-ibu rumah tangga yang merangkap profesi selain bekerja juga harus mengurus rumah tangga. Tentu saja hal itu membuat mereka, mencari cara yang lebih praktis. Karena selain dari kesibukan pekerjaan mereka juga harus mengurus keluarga yang merupakan prioritas utama.
Evaluating the Usability of Moodle-based Learning Management System Application in Faculty of Engineering UNSIKA Using USE Questionnaire Pambudi, Teguh; Umam, Hilman Imadul; Armin, Edmund Ucok
JMSP (Jurnal Manajemen dan Supervisi Pendidikan) Vol 7, No 3 (2023): Vol. 7 No. 3 Juli 2023
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um025v7i32023p131

Abstract

A Moodle-based LMS has been used by the Faculty of Engineering Universitas Singaperbangsa Karawang to support blended learning. Therefore, it needs to be evaluated in terms of user satisfaction and usability. The USE Questionnaire was used in this study to evaluate Moodle-based LMS usability. The usability was evaluated by analyzing the data from the USE Questionnaire, which was personally filled out by 167 respondents, both students and lecturers. The result showed that USE Questionnaire used in this study possessed excellent validity and reliability with coefficient values above 0.700 and 0.981 respectively. In addition, the percentage of each usability parameter for Moodle-based LMS application was as follows: 70,1% of usefulness, 68,9% ease of use, 73,7% ease of learning, and 67,9% of satisfaction. It can be concluded that this application is worthy to use.
Performa Model YOLOv8 untuk Deteksi Kondisi Mengantuk pada pengendara mobil Armin, Edmund Ucok; Edra, Anggun Purnama; Alifin, Fakhri Ikhwanul; Sadidan, Ikhwanussafa; Sary, Indri Purwita; Latifa, Ulinnuha
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.279

Abstract

Driving while drowsy is identified as a significant risk factor in traffic accidents, yet awareness of this risk is often lower compared to other hazards. Phenomena such as microsleep have been shown to increase the risk of inattention and accidents on the road. This study proposes a novel approach utilizing Deep Learning, specifically YOLOv8, to detect and address the risk of driver drowsiness. To train the model, the researchers employed a secondary dataset consisting of 3708 images, partitioned into 80% for model training and 20% for validation. Multiple models were compared during the training process, and the results indicated that the YOLOv8 model outperformed previous models, achieving a recall value of 0.95261, precision of 0.94655, F1-SCORE of 0.9496, and mAP of 0.98055. This research contributes to the development of more effective drowsiness detection systems using Deep Learning approaches, with promising evaluation results.
Evaluasi Kinerja YOLOv11 pada Deteksi Penyakit Tanaman Cabai: Studi Komparatif dengan YOLOv8, YOLOv5, dan SSD Permatasari, Jelita; Armin, Edmund Ucok; Sunardi, Egi; Laili, Maria Bestarina; Putri, Salsanabila Mariestiara
Jurnal Teknologi Vol 25, No 3 (2025): Desember 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i3.8400

Abstract

Early and accurate detection of chili plant diseases is essential to support precision agriculture and minimize crop losses. Conventional visual inspection performed by farmers is often subjective and inconsistent, particularly under varying lighting conditions and complex field environments. Recent developments in deep learning, especially object detection models, enable the automation of disease identification with higher reliability. This study evaluates the performance of the YOLOv11 architecture for detecting three classes related to chili plant conditions—anthracnose, fruit fly, and healthy fruit—using a primary dataset of 1,062 field images collected in Karawang, Indonesia. The model was trained using a standardized configuration and compared with three widely used object detection models: YOLOv8, YOLOv5, and SSD. The training process was conducted for 100 epochs, with evaluation metrics including precision, recall, mAP50, mAP50–95, and inference time. Experimental results show that YOLOv11 achieved the highest detection performance, with an mAP50 of 86.94%, outperforming YOLOv8 by 3.8%, YOLOv5 by 6.8%, and SSD by 12.7%. The model also demonstrated the fastest inference speed at 10.9 ms, making it suitable for real-time field applications. Training analysis indicated stable convergence at the 61st epoch, supported by balanced precision (0.82391) and recall (0.77967) values as well as consistent reductions in both training and validation losses. These findings demonstrate that YOLOv11 provides more accurate and efficient detection of chili plant diseases compared with previous YOLO variants and SSD, and it offers strong potential for implementation in practical agricultural environments.
Co-Authors Aziz Khan Edra, Anggun Purnama Fakhri Ikhwanul Alifin Ikhwanussafa Sadidan, Ikhwanussafa Laili, Maria Bestarina Latifa, Ulinnuha Permatasari, Jelita Putri, Salsanabila Mariestiara Sary, Indri Purwita Sunardi, Egi Teguh Pambudi