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Buletin Ilmiah Sarjana Teknik Elektro
Published by Universitas Ahmad Dahlan
ISSN : 26857936     EISSN : 26859572     DOI : 10.12928
Core Subject : Engineering,
Electrical & Electronics Engineering
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
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Articles 10 Documents
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Search results for , issue "Vol. 6 No. 2 (2024): June" : 10 Documents clear
One-Shot Pulse Boost Converter-Based Inductor-Synchronized Piezoelectric Energy Harvester Sutikno, Tole; Pradana, Muhammad Sukmadika; Pamungkas, Anggit
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10020

Abstract

In this paper, we aim to review current methods of energy harvesting, focusing on piezoelectric energy. To optimize the use of piezoelectric devices in applications, a model is needed to observe the performance generated from piezoelectricity. To achieve better performance, the rectifier and capacitor systems are connected to a boost converter circuit. Another method is to use the Synchronized Switch Harvesting Inductor (SSHI) method. This method implements a stand-alone switching technique based on transistors and rectifier diodes and does not require an external power supply. This research creates an electric energy harvesting floor device by utilizing piezoelectricity in the form of a harvester, which aims to find out how piezoelectric works and to obtain a circuit with the most efficient characteristics as a piezoelectric power generator using SSHI and a boost converter. This study compares the characteristics of series, parallel, and series-parallel circuits on the floor of the most optimal piezoelectric energy harvester to generate voltage. The results of the data collection were based on the number of steps on the floor of the 16-piece piezoelectric energy harvester with a series-parallel circuit configuration connected with an SSHI circuit and without an SSHI circuit. In this test, the resulting voltage output is a DC voltage with an input step of 60 times the step on piezoelectricity. In this paper, an energy harvester using the SSHI circuit provides a more stable voltage on the harvester floor than a boost converter by providing 16 piezoelectric pieces arranged in series parallel. A floor energy harvester with a series-parallel configuration connected to SSHI gets the most optimal result compared to using a boost converter.
Early Detection of Disease in Chicks Using CNN on Bangkok Chicken Health Dwicahyo, Agung; Mufandi, Ilham; Nurfadila, Agustin Rani; Ardani, Much. Taufik; Dzilhilmi, Ubaid
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10245

Abstract

Bangkok Chicken (Gallus Gallus Domesticus) is a type of chicken in Indonesia that has a high source of protein and supports the community's economy. The growth and development phase of chicks is a critical period because chicks are very vulnerable to attacks by infectious and non-infectious diseases. These diseases can cause high mortality rates and cause significant economic losses for farmers. This study aimed to investigate the potential for using CNN technology in the early detection of disease in Bangkok chicks in the Ponorogo district. As an artificial neural network, CNN can recognize patterns in visual data with high accuracy. The use of CNN technology in the agricultural sector, including animal husbandry, has shown promising results in supporting early disease detection systems in livestock. This study aims to investigate the potential of using CNN technology in the early detection of disease in Bangkok chicks in the Ponorogo district. By processing visual data from chicken images, CNN will be trained to identify early signs of disease in chicks. The result of this research is that this research can help maintain the availability and security of animal food supplies, which is an essential component of overall food security. In addition, by reducing losses caused by disease, this research can contribute to sustainable agriculture by ensuring the continuation of stable and sustainable animal food production.
Automatic Satay Grill Using 5 Volt Stepper Motor for Home Business Prayoga, Dimas Dwi; Anshory, Izza; Wicaksono, Arief; Syahrorini, Syamsudduha
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10519

Abstract

Increased consumer demand for satay dishes as one of the traditional culinary demands innovation in the manufacturing process to improve efficiency and consistency. This research aims to design and implement a dc stepper motor-based automatic satay grill system as a solution for small businesses. This system uses a DC stepper motor as the main component, using an Arduino UNO microcontroller programmed to regulate the temperature and automatic rotation of the satay. The measurement uses a built-in LM35 temperature sensor to monitor the temperature during grilling to ensure optimal doneness. In addition, the system is equipped with an interface using an LCD screen connected to the Arduino UNO microcontroller to monitor the temperature and condition of the satay grill, allowing the user to easily set the grilling parameters according to preference. During testing of the prototype, results showed that this automatic satay grill system is capable of producing satay with uniform doneness and meeting quality standards. In addition, time and labor efficiency improved, making a positive contribution to the productivity of small and medium enterprises. Overall, this development provides an innovative and effective solution in improving the satay production process, positively impacting traditional culinary businesses.
Random Multi-Augmentation to Improve TensorFlow-Based Vehicle Plate Detection Kirana, Kartika Candra; Abdulrahman, Salah Abdullah Khalil
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10542

Abstract

In the development of the "Machine Learning" education kit, vehicle plate recognition was created using TensorFlow with SSD MobileNetV2. The detection failure rate in the training process with varying distances and lighting from the camera is high if the training data is insufficient. Addressing that notable gap in research, we proposed Random Multi-Augmentation to Improve TensorFlow-Based Vehicle Plate Detection. Augmentation techniques are expected to train data that is manipulated at varying lighting and distance. The proposed method consists of two combining augmentation approaches, namely: position augmentation and lighting augmentation. Position augmentation which consists of Flip, Crop, Rotate, Shift, and Crop is used to enrich the visualization of distance and viewing angle, while Lighting augmentation which consists of Greyscale, Hue, Saturation, Brightness, Exposure, and Blur is used to enrich the visualization of lighting. Variations in values were determined randomly based on variations in values from several previous studies. The comparison of TensorFlow SSD MobileNetV2 and Augmentation were tested using one video Roboflow. TensorFlow without augmentation exhibited an accuracy of 60%, precision of 100%, recall of 60%, and an F1 score of 75%, whereas TensorFlow within augmentation achieved a higher accuracy of 70%, precision of 100%, recall of 70%, and an F1 score of 82.3%. Based on precision measurement, Tensorflow can be claimed to prevent false positives, which indicates that the algorithm did not detect non-plate objects as vehicle plates. Furthermore, a comparison of the use of augmentation shows an increase in plate detection capabilities when using augmentation as Tensorflow preprocessing, which is indicated by an increase in recall and accuracy values. These results emphasize that augmentation is the pre-processing optimizer for vehicle number plate systems.
Estimation of Crowd Density Using Image Processing Techniques with Background Pixel Model and Visual Geometry Group Trung, Ha Duyen
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10785

Abstract

Crowd density estimation in complex backgrounds using a single image has garnered significant attention in automatic monitoring systems. In this paper, we propose a novel approach to enhance crowd estimation by leveraging the Bayesian Loss algorithm in conjunction with monitoring points and datasets such as UCF-QNRF, UCF_CC_50, and ShanghaiTech. The proposed method is evaluated using standard metrics including Mean Square Error (MSE) and Mean Absolute Error (MAE). Experimental results demonstrate that the proposed method achieves significantly improved accuracy compared to existing estimation techniques. Specifically, the proposed technique showcases a 106.0 reduction in MSE and a 91.6 reduction in MAE over state-of-the-art methods, thereby validating its effectiveness in challenging crowd density estimation scenarios.
Tracking Ball Using YOLOv8 Method on Wheeled Soccer Robot with Omnidirectional Camera Julianda, Refli Rezka; Puriyanto , Riky Dwi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10816

Abstract

Object detection is very we often find in everyday life that facilitates every activities in the object recognition process, for example in the military field, intelligent transportation, face detection, robotics, and others. Detection target detection is one of the hotspots of research in the field of computer vision. The location and category of the target can be determined by using target detection. Currently, target detection has been applied in many fields, one of which includes image segmentation.You only look once (YOLO) is an algorithm that can perform object detection in realtime, YOLO itself always gets development and improvement from previous versions. YOLOv8 is a type of YOLO from the latest version. YOLOv8 is a new implementation of Deep Learning that connects the input (original image) with the output. This type of YOLOv8 algorithm uses A deep dive architecture, assisted by CNN and a new backbone which uses convolutional layers for pixels which when described will be shaped like a pyramid. YOLOv8 is a stable object detection processing method with 80% higher than the previous version of YOLO, which makes YOLOv8 a type of YOLO that is better at processing object data faster and more efficiently in Real-Time.The camera with omnidirectional system is able to detect spherical objects and other objects using the YOLOV8 model used. In performance testing with 320×320 and 416×416 frames, because it fits the grid structure of the YOLO architecture. YOLOv8 has a higher mAP value with a value of 95,5% compared to previous versions of YOLO. In the detection test, YOLOv8 has a better average object detection than the previous version of YOLO which is indicated by the number of objects detected more stable.
Human Movement Detection System Based on the Internet of Things Syah, Nur Ifan; Sunardi, Sunardi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10829

Abstract

This research refers to making an IoT-based movement system tool. This system is used to calculate the number of human movements entering a room and also people leaving the room. In this study, a Passive Infrared Receiver (PIR) sensor is used to detect the movement of people when entering a room. The sensor will trigger a calculation of the number of people passing through the device, equipped with NodeMCU ESP 8266 as a microcontroller, with NodeMCU ESP 8266 it is more efficient to connect to the WiFi module on the Internet of Things system without the need for other modules. The tool is also equipped with an LED light as a notification of movement, an indicator buzzer if the room is fully filled, and there is also a Liquid Crystal Display (LCD) which is used to display the number of human movements entering and leaving the room, as well as the people who are in the room. The sensitivity of the PIR sensor depends on the distance of the object to the sensor. The research is running well. Notifications from the LED light go according to their duties, if someone enters the room then LED 1 will light ON, and if someone leaves the room then LED 2 will light ON. The results of the application in detecting objects of people or visitors one by one are conditioned based on walking movements and based on a person's height posture from 150 cm to 170 cm the accuracy of the object is accurate, because the distance from the object to the The tool is not too far so the tool can easily detect its movement.
Precision Agriculture 4.0: Implementation of IoT, AI, and Sensor Networks for Tomato Crop Prediction Pérez, Miguel Ángel Giménez; González, Antonio Guerrero; Rodríguez, Francisco Javier Cánovas; Leon, Inocencia María Martínez; Abrisqueta, Francisco Antonio Lloret
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.10954

Abstract

Precision agriculture introduces an innovative approach to farm management by involving the use of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and sensor networks to optimize resources and increase crop yields. In this context, the present study aimed to develop a tomato crop prediction system using IoT, AI, and sensor networks. A system architecture was designed, including distributed sensors, IoT gateways, and a cloud platform running AI models based on recurrent neural networks. These AI models were trained with environmental data and validated using actual harvest data. The results showed up that the model could predict weekly harvest volumes with an average error of 3.2% during the best 4-week period. The integration of IoT, AI, and sensor networks proved to be effective for accurate crop prediction and has potential for other applications in precision agriculture.
Design and Implementation of an IoT System for Indoor Measurement and Monitoring Fire and Gas Warning Tri, Nguyen Van; Manh, Le Hung; Trung, Ha Duyen
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.11254

Abstract

Early detection and warning of fires occurring in homes is crucial to prevent loss of life and property. Fires can happen anywhere and at any time, but the presence of fire alarms helps keep homes safe. Therefore, early detection of fires will prove to be crucial as it could mean the difference between life and death. Most recently, the Internet of Things (IoT) technology has been deploying for data collection, transmission, storage and processing of large amounts of data from various sensor devices. Through the Internet, these sensors can be linked and help us manipulate or collect data from them. In this paper, we will use various types of sensors to sense the presence of fire and gas in the design and implementation of a completed IoT system. The designed IoT system aims to alert and assist homeowners, building guards, and firefighters about the presence of fire and gas leaks. Additionally, a common preventive solution is to install a sprinkler to spray water when the smoke sensor detects a fire. The designed system has been successfully implemented and tested in a variety of circumstances in an education bulding of University.
Internet of Things (IoT) Based Speed Monitoring System for Electric Cars Putra, Rean Andhika; Ma’arif , Alfian
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i2.11317

Abstract

The electric cars that have been made are also being competed at national events such as the FESC IIMS 2022 event in Jakarta and the Inter-College Electric Car Joint Training by the Ministry of Public Works and Public Housing (PUPR) in the context of the 2022 Road Day and the Indonesian Electric Car Contest (KMLI) at the Bandung State Polytechnic in Bandung until now. For the sustainability of electric cars, various research still needs to be carried out to achieve optimal electrical system design. Here, Ahmad Dahlan University's electric cars can operate at speeds above 30,000 rpm when monitored using a speedometer, but here there is no design for a long-distance speed monitoring system, therefore team colleagues who are in the paddock during the competition are not yet able to monitor the speed from inside the paddock. This system needs to be used during the race so that team mates in the paddock during the race can also monitor the speed of the Electric Car during the race when the race starts. This system is used using the Internet of Things (IoT) method because IoT can display speed data via a laptop and can implement a remote monitoring system. Therefore, in this final report, we will discuss how to design and implement a speed monitoring system for electric cars based on the Internet of Things (IoT). To get the best results and as expected, the design of this system refers to various sources. Where the input component is the detection from the Optocoupler Sensor after detecting the wheel speed, then the data is processed via Arduino using program initiation on the Arduino ide, after that the output will be generated on the I2C LCD and also the output will be displayed on IoT Things Speak because IoT itself, you have to use an internet signal, so here we add the sim800l component which is used to send Arduino data to Thingspeak via the sim800l internet intermediary. This tool was created with the aim of ensuring that the Al-Qorni UAD electric car continues to develop and has an advanced technological system.

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