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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian Masyarakat Nasional
Rahman, Ben
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Journal : PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic

 1 
Voice Command-Based IoT on Smart Home Using NodeMCU ESP8266 Microcontroller Shakaramiro, Muhammad Ariel; Gunaryati, Aris; Rahman, Ben
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 1 (2024): March 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i1.8287

Abstract

This research focuses on developing a Smart Home prototype that integrates the Internet of Things (IoT) and voice command using the NodeMCU ESP8266 microcontroller. This system allows users to control household devices such as lights, gates, and fans with voice commands through voice-enabled devices. The prototype utilizes NodeMCU ESP8266 to connect the devices to a WiFi network. The developed voice recognition system can accurately identify voice commands and send instructions to NodeMCU ESP8266 to control the corresponding devices. The test results demonstrate the prototype's efficiency in automating household devices through voice commands. Consequently, users can enhance comfort and energy efficiency within their homes. This research opens opportunities for the development of smarter and user-friendly Smart Home systems in the future. Response testing of the Blynk application showed a 100% success rate, with an average response time of less than ten seconds. WiFi network testing was carried out with the IP Address 192.168.101.137, resulting in good functional performance even in the presence of physical obstacles, and the device can operate well up to a distance of 22 meters.
Enhancing IT Employee Placement Using SMARTER with Centroid Rank Order Weighting for Candidate Suitability Rahman, Ben; Adinda, Saskia; Handayani, Adelia Putri
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 1 (2024): March 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i1.9165

Abstract

In an era characterized by constant evolution in digital technology, the significance of Information Technology (IT) within organizations is of utmost importance. Efficient recruitment processes and appropriate placement of IT personnel are crucial for a company's success. This research aims to develop a candidate assessment system using the SMARTER (Smart Simple Multi-Attribute Rating Technique Exploiting Rank) approach combined with the Rank Order Centroid weighting method to assist HR departments in selecting suitable IT candidates. Addressing the challenges faced by HR directors lacking IT expertise, this study offers an innovative solution to enhance alignment between candidates and IT position requirements. Through the analysis of nine specific criteria, including education, work experience, and English proficiency, the system is designed to provide more accurate candidate placement recommendations. The findings of this research demonstrate significant potential in improving IT recruitment processes and contributing significantly to the literature.
Object Detection Using YOLOv5 and OpenCV Subagja , Mifta; Rahman, Ben
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10772

Abstract

Object detection is one of the main tasks in computer vision, aimed at recognizing and localizing objects in images or videos. In this study, we utilize the YOLOv5 model, which is well known for its efficiency in realtime object detection. We implement this method with the help of the OpenCV library for image processing. This research aims to evaluate the performance of YOLOv5 in detecting objects in various types of images, including landscape photos, cat photos, and traffic light images with vehicles. The model is trained using optimization methods with the Adam optimizer and assessed through metrics like accuracy, precision, recall, and IoU. The results indicate that YOLOv5 can detect objects with high accuracy and fast inference time, making it an ideal solution for various applications such as security monitoring, video analysis, and automatic recognition systems. The advantage of YOLOv5 over traditional methods such as histogram equalization and thresholding lies in its ability to perform realtime detection with optimal computational efficiency. Thus, this study demonstrates that YOLOv5 is a suitable choice for implementing deep learningbased object detection systems.
Co-Authors Abdul Karim Adinda, Saskia Ahyuna Ahyuna, Ahyuna Aris Gunaryati, Aris Budiman, Tegar Fauziah Fauziah Fifto Nugroho Gustian, Yonathan Wijaya Handayani, Adelia Putri Hindarto, Djarot I Wayan Sugianta Nirawana Iskandar Fitri, Iskandar Jayanti, Riza Dwi Muhammad Syaifudin Putra, Abdul Rahman Wijaya Santoso, Handri Saputra, Adi Dwifana Septi Andryana Shakaramiro, Muhammad Ariel Sjah, William Surya Subagja , Mifta Suherman, Endang Teddy Mantoro Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa