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All Journal JURNAL INTEGRASI Journal of Applied Electrical Engineering Community Development Journal: Jurnal Pengabdian Masyarakat CARE: Jurnal Pengabdian Multi Disiplin Journal of Sustainable Community Development Jurnal Pemikiran dan Pengembangan Pembelajaran
Diono, Diono
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Journal : JURNAL INTEGRASI

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Pendeteksian Objek Hasil Pengepresan Kaleng dan Botol dengan Metode You Only Look Once (YOLO) yang Diaplikasikan pada Mesin Sortir Pembelajaran PBL Diono, Diono; Wicaksono, M. Jaka Wimbang; Jefiza, Adlian; Prayudha, Dimas Rama
JURNAL INTEGRASI Vol. 16 No. 1 (2024): Jurnal Integrasi - April 2024
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v16i1.4598

Abstract

Image Processing is a technique of processing images with the input of an image and producing an image as well. One of the functions of Image Processing that the author wants to apply is the detection of an object from a still image or a moving image. In this application, the object to be identified by the author will be applied to the PBL sorting machine in the form of cans and bottles. In designing this system, the You Only Look Once (YOLO) method is used and several libraries. YOLO is an algorithm for detecting an object using an artificial neural network (ANN) from an image where this network divides the image into several regions and predicts each bounding box and probability for each region of the image. The author also uses a webcam to detect the object and a Servo Motor as a sorter on the PBL Sorting Machine. The result of this final project is that the system can detect objects cans and bottles properly and produce precise accuracy and is able to move the sorter based on the output data from the detection results.
Klasifikasi Wajah Manusia Menggunakan Multi Layer Perceptron Jefiza, Adlian; Diono, Diono; Putra, Irwanto Zarma; Budiana, Budiana; Nur Suciningtyas, Ika Karlina Laila; Siregar, Lindawani; Puspita, Widya Rika; Harlan, Fandy Bestario; Assegaf, Iqchan; Marpaung, Roy Hitmen
JURNAL INTEGRASI Vol. 15 No. 2 (2023): Jurnal Integrasi - Oktober 2023
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v15i2.4843

Abstract

The problem of data security at a time when it is needed in the world of technology. The use of biometrics as data security is very necessary. This study aims to detect human biometrics using the Kinect sensor. The biometric that is detected is the face. The face image is captured by the Kinect sensor. For data feature extraction using Gray Level Co-Occurrence Matrix (GLCM. The parameters used are Contrast, Energy, Homogenity, and Correlation. The data obtained will be classified using Multi Layer Perceptron. Face classification is based on race. There are 3 races studied namely Indonesian, Chinese and African Native Races. The total data used are 100 photos of faces. The classification results show an accuracy of 86.7% using Multi Layer Perceptron
Prototype Sistem Elevator Menggunakan Motor Stepper Berbasis Atmega 16 Wicaksono, Muhammad Jaka Wimbang; Diono, Diono; Sani, Abdullah; Dzulfiqar, Mohamad Alif; Budiana, Budiana; Aryeni, Illa; Oktani, Dessy; Kamarudin, Kamarudin; Futra, Asrizal Deri; Darmoyono, Aditya Gautama; Mahdaliza, Rahmi; Hasnira, Hasnira; Maulidiah, Hana Mutialif; Gusnam, Mu'thiana
JURNAL INTEGRASI Vol. 15 No. 2 (2023): Jurnal Integrasi - Oktober 2023
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v15i2.6058

Abstract

Elevator are one of the most important transportation these days. Elevator is a link in a tall building that has many floors. The role of the elevator which is always used by the public requires a high level of precision. In this study, researchers made a prototype elevator system using a stepper motor and obtained test results with a maximum error rate of 2% on the 10 cm elevator input using a ruler as a testing tool. The largest comparison of testing result using a ruler and using a rotary encoder is 0.2 cm or 2 mm at the 40 cm elevator input. The precision of the elevator prototype still can be improved by using half step mode when controlling the stepper motor.
Pengklasifikasian Warna dan Bentuk Produk Menggunakan Kamera ELP- USB8MP02G-MFV dengan Berbasis YOLOV7 Diono, Diono; Muhammad Syafei Gozali; Yohannes Ridho Soru
JURNAL INTEGRASI Vol. 17 No. 1 (2025): Jurnal Integrasi - April 2025
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v17i1.9266

Abstract

The development of artificial intelligence technology allows the system to detect various objects. In the research on the classification of color and shape of products using the ELP-USB8MP02G-MFV camera based on YOLOV7, it aims to modify the conveyor on the molding machine. Because the conveyor only has the function of distributing goods from the molding machine to the bin and the length of time used to wait for the bin to be full is the reason why this conveyor is modified. Modifications are made by adding a camera that has been connected to the Raspberry Pi 4B on the conveyor, the camera functions to take pictures of passing product objects then the image is detected by the system on the Raspberry Pi 4B so that this conveyor machine can classify the objects produced by the molding machine. The system detects objects using the YOLOv7 algorithm. This study was carried out with three tests, namely object model detection testing, color detection testing and program and relay output testing where 98.11% was for object model detection testing, 97.37% for color detection and 100% for program and relay output testing.  The results of this research will contribute to the development of object detection, especially product object detection and the results of molding machines.
Co-Authors Abdullah Sani Al attas, Said Usman Sulaiman Amri, Juhrial Aryeni, Illa Assegaf, Iqchan BUDIANA, BUDIANA Dalimunthe, Juwita Marwah Darmoyono, Aditya Gautama Destiana, Rizka Deviana, Yesi Dwijotomo, Abdurahman Dzulfiqar, Mohamad Alif Fajri, Nuzul Febri Yanti Firdaus, Fadli Futra, Asrizal Deri Gautama, Aditya Ghozali, Muhammad Syafei Gozali, Muhammad Syafei Gusnam, Mu'thiana Hardi, Moh. Nabil Khairy Harlan, Fandy Bestario Hasnira Hasnira, Hasnira Jefiza, Adlian Kamarudin Kamarudin Mahdaliza, Rahmi Mahmud, Aqil Fadhurahman Marpaung, Roy Hitmen Maulidiah, Hana Mutialif Muhammad Syafei Gozali Mulyadi, Indra Hardian Naibaho, Adventus Nur Suciningtyas, Ika Karlina Laila Oktani, Dessy Prayudha, Dimas Rama Puspita, Widya Rika Putra, Irwanto Zarma Rahmaini, Vika Rahmana, Henry Siregar, Lindawani Wahyuningsih, Asriani Rosalina Wicaksono, M. Jaka Wimbang Wicaksono, Muhammad Jaka Wimbang Winandari, Rahmi Agustin Wulandari, Febrina Yangu, Juliansah Yangu, Juliansyah Yangu, Juliasyah Yohannes Ridho Soru