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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Journal of Robotics and Control (JRC) Jurnal INFOTEL
Florentinus Budi Setiawan
Soegijapranata Catholic University
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Mechanical Engineering

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Design and Implementation of Smart Forklift for Automatic Guided Vehicle Using Raspberry Pi 4 Florentinus Budi Setiawan; Phoa Marcellino Siva; Leonardus Heru Pratomo; Slamet Riyadi
Journal of Robotics and Control (JRC) Vol 2, No 6 (2021): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.26130

Abstract

Automatic Guided Vehicle (AGV) pallet truck is widely used in the industry. This kind of AGV is such a combination of the ordinary AGV used forklift mechanism. The forklift mechanism is employed for lifted up or carrying things from one place to another place. As the technology has been developed along with the industry revolution 4.0, therefore, the activity could be done automatically by using a robot-like AGV pallet truck. Basically, the working principle of the AGV pallet truck is similar to the ordinary forklift whereas the AGV pallet truck is automatically operated. DC motor is applied as the driving force for the uplifting and down lifting process in the forklift mechanism of the AGV pallet truck. DC motor is chosen because it has large torque which is advantageous for lifting loads. Unfortunately, DC motor also owns some disadvantages such as high maintenance fees and less precision. This study proposes a smart forklift mechanism for AGV pallet trucks that utilizes a stepper motor and ultrasonic distance sensor. This smart forklift mechanism is equipped with raspberry pi model B as the main microcontroller and combined with an ultrasonic distance sensor. The result of the ultrasonic distance sensor has an error approaching zero percent so the precision of the height can be fully controlled. Step / Revolution (SPR) method makes the stepper motor can move smoothly like micro-step and also the number of rotations can be controlled as we want.
Fruit Ripeness Classification System Using Convolutional Neural Network (CNN) Method Florentinus Budi Setiawan; Christophorus Bramantya Adipradana; Leonardus Heru Pratomo
PROtek : Jurnal Ilmiah Teknik Elektro Vol 10, No 1 (2023): PROtek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v10i1.5549

Abstract

The increasing consumer demand in the fruit industry has also demanded that various sectors of the fruit processing industry be able to adapt to this situation. The demand for good quality and fresh fruit requires technological advances and supporting systems that can be used in the fruit processing industry to produce the best quality fruit. Referring to this, this study aims to detect the type and maturity of fruit using machine learning with the CNN (Convolutional Neural Network) method using the function of a camera that is integrated with the program algorithm. This research is a refinement of previous research that has been made at the university by increasing the ability to read objects based on color with different methods. In this programming language, Python also requires several additional libraries to carry out the object detection process, namely by using the cvzone library as the main library. This study shows that the detection of fruit and ripeness using the CNN method was successful in detecting the type and maturity of the fruit. In the design and trial of this research, it can run well according to the algorithm created by the researcher. The success rate and accuracy of the detection of the type and maturity of this fruit reach 90%.
Implementation of line detection self-driving car using HSV method based on raspberry pi 4 Florentinus Budi Setiawan; Eric Pratama Putra; Leonardus Heru Pratomo; Slamet Riyadi
JURNAL INFOTEL Vol 14 No 4 (2022): November 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i4.801

Abstract

With the development of technology, especially in the field of robotics, daily human activities can be carried out with artificial intelligence. One of the artificial intelligence technologies that help ease the burden on humans, especially in terms of driving, is self-driving cars. In this case, self-driving cars have several methods with GPS systems, radar, lidar, or cameras. In this study, a self-driving car system was designed on a navigation path model using a street mark detector with an intermediary sensor, namely a camera as a vision sensor. This self-driving car system uses a prototype called an autonomous car to walk on a path which is a self-driving car navigation direction based on the detected line to be able to detect camera sensors that process line images from the camera using HSV. method. In this study, a self-driving car system has been successfully designed using a microcontroller, namely Raspberry Pi 4 as a programmer and L298n motor driver, BTS7960 as a driver for a self-driving car. The Raspberry Pi 4 sends real-time images through the camera as a vision sensor which then detects a line to navigate the movement of this self-driving car. By using image processing, the resulting level of precision can reach the average value according to the direction of the self-driving car.
Co-Authors Christophorus Bramantya Adipradana Eric Pratama Putra Leonardus Heru Pratomo Phoa Marcellino Siva Slamet Riyadi