Article Per Year (5 Year)
p-Index From 2021 - 2026
1.497
P-Index
This Author published in this journals
All Journal Elektron Jurnal Ilmiah JTSL (Jurnal Tanah dan Sumberdaya Lahan) Journal of Applied Electrical Engineering Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Journal of Golden Generation Abdimas Journal of Golden Generation Engineering
Lalu Kaisar Wisnu Kita
Politeknik Negeri Batam
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : journal of applied electrical engineering

 1 
Alat Pemantau dan Penghitung Jumlah Pengunjung Berbasis Aplikasi Telegram Rindang Alamsyah; Ika Karlina Laila Nur Suciningtyas; Nanta Fakih Prebianto; Iman Fahruzi; Imam Sholihuddin; Abdurahman Dwijotomo; Lalu Kaisar Wisnu Kita; Indra Daulay
Journal of Applied Electrical Engineering Vol. 7 No. 1 (2023): JAEE, June 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v7i1.5459

Abstract

A visitor monitoring and counting device is a tool designed to send information in the form of images and the number of visitors detected through a camera and sensors installed on the device. This device is based on the Telegram application using the ESP32-CAM module as the camera to capture images of each incoming visitor. The device is equipped with an infrared sensor used to detect incoming visitors. The device works by counting the number of visitors when they enter, and then sending a signal to the ESP32-CAM to capture a physical image as evidence. The data from the device's operation is sent through the Telegram application. The research results show that the device functions well at a distance of 60 cm, but it does not function properly at a distance of 90 cm. The average time required for the device to send an image to the application is 2.5 seconds. The fastest time required by the system to read data between one visitor and the next is 15,4 seconds.
Identification Food Nutrition and Weight Prediction using Image Processing Sani, Abdullah; Silitonga, Ricky; Mishthafiyatillah, Mishthafiyatillah; Lalu Kaisar Wisnu Kita; Ika Karlina Laila Nur Suciningtyas; Ririn Humaera; Budiana , Budiana
Journal of Applied Electrical Engineering Vol. 9 No. 1 (2025): JAEE, June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v9i1.9492

Abstract

Obesity is a global issue with rising prevalence each year, driven partly by excess nutrient and calorie intake. Identifying nutrient content in food is vital to prevent obesity. This research employs image processing, specifically the YOLO (You Only Look Once) algorithm, to classify and identify fruits and vegetables quickly and accurately. YOLO is advantageous for its speed and ability to classify multiple objects simultaneously. The goal is to develop a system that recognizes, classifies, and predicts the weight of fruits and vegetables, providing nutritional and calorie information. Tests showed that the system accurately detects produce under various lighting conditions—achieving 100% accuracy with additional ring light (600–650 lux) and 99.2% without extra lighting. Beyond object detection, the system predicts weight with an average error of 5.6% when illuminated. This technology has the potential to aid users in monitoring nutritional intake by providing reliable identification and calorie data, contributing to obesity prevention efforts.
Co-Authors Abdullah Sani Abdurahman Dwijotomo Adlian Jefiza Afif Nuril Mustofa Arif Wahyu Budiarto Arif Wahyu Budiarto Budiana, Budiana Didi Istardi Dwi Suryanto Nababan Ewaldo, Faieziel Fadli Firdaus Fauzun Atabiq Febran Danuarta Febran Danuarta Fitriana Fitriana Hasnira Hasnira Hasnira Ika Karlina Laila Nur Suciningtyas Imam Sholihuddin Iman Fahruzi Indra Daulay Irwanto Zarma Putra Jhon Hericson Purba Jihan Zeinyuta R. Kharisma Nugraha, Tegar M. Naufal A. D. M. Surya Bagus Rangkuti M. Surya Bagus Rangkuti Mishthafiyatillah Mishthafiyatillah Mishthafiyatillah Mishthafiyatillah , Mishthafiyatillah Mishthafiyatillah, Mishthafiyatillah Muhammad Naufal Airlangga Diputra Muhammad Syafei Gozali Mutialif Maulidiah, Hana Nanta Fakih Prebianto Oktavianto Gustin Pitriani, Shelly Sri Rindang Alamsyah Ririn Humaera Robi Kurniawan Silitonga, Ricky Syukur Dwi F. P. Tri Hartanti, Tri Yusiran Yusiran Yusiran, Yusiran