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All Journal Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Computer Science and Information Technology (CoSciTech)
Yasin, Fakhriyal Riyandi
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Computer Science & IT Engineering

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Construction Of Railway Door Automation Prototypes Using Arduino, Servo Motors and Ultrasonic Sensors : Konstruksi Prototipe Otomatisasi Pintu Kereta Api Menggunakan Arduino, Motor Servo, dan Sensor Ultrasonik Kharisma, Ivana Lucia; Kamdan; Firdaus, Asep Rizki; Prayoga , Rizki Haddi; Yasin, Fakhriyal Riyandi; Tresna Ati, Mutiara Annisa
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 1 (2023): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v14i1.13584

Abstract

The Indonesian railway system is currently experiencing a lot of developments in terms of technology. The Indonesian railway system has been integrated with technology 4.0, where all transaction processes, whether payment, ordering tickets or monitoring trains, can be monitored via electronic media. Of course, this technology must be supported by adequate infrastructure, one of which is the railroad crossing. In Indonesia, several railroad lines have been constructed, and many portal or crossbar railroads have also been constructed. The railroad gate is part of the railway system which has a very important role, especially in regulating the safety of train travel. The rail gateway has been a problem and a source of accidents in recent years. This is because there are no security facilities at any rail portals, causing drivers to continue to break traffic laws. The making of this automatic railroad doorstop uses the Prototype method, namely a simulation that uses Arduino UNO R3, servo motors, HC-SR04 ultrasonic sensors and other components that can support the manufacture of this railroad doorstop prototype. The prototype of this automatic train doorstop is equipped with many sensors that works automatically according to what is ordered, so that its use can be easily controlled and implemented in real life. This automatic railroad crossing system is expected to optimize the task of the railroad crossing guard by providing automation for the process of opening and closing the railway door and providing additional warning information for the community around the railway door location so as to reduce the potential for accidents caused by drivers or people who break traffic laws.
The IMPLEMENTASI YOLOV8 NANO PADA SISTEM MONITORING BUDIDAYA JAMUR TIRAM BERBASIS IOT Nopiandi, Andi; Yasin, Fakhriyal Riyandi; Prayoga, Rizki Haddi; Somantri, Somantri; Kharisma, Ivana Lucia
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10673

Abstract

Oyster mushrooms are one of the agricultural commodities with high economic value and are widely cultivated in Indonesia. However, the conventional process of monitoring their growth is still carried out manually, which requires considerable time and labor while also being prone to errors in decision-making. To address this issue, this study developed an automatic oyster mushroom growth monitoring system using Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The system uses a DHT22 sensor to measure temperature and humidity, a BH1750 sensor to measure light intensity, and an ESP32-CAM module to capture mushroom images. The data is transmitted through the ESP32 and analyzed using Python, while the images are processed by a YOLOv8 Nano model to classify mushroom growth stages into baglog, young mushrooms, and ready-to-harvest mushrooms. The monitoring results are displayed in real time on a dashboard and stored in a MySQL database. The model training results show fairly good performance, with an average precision of 0.69, recall of 0.78, and a mean Average Precision (mAP@0.5) of 0.71. Further testing was conducted on 15 test images for each mushroom stage, and all images were successfully detected according to their actual classes. Additionally, tests conducted on 10 negative images (without mushroom objects) also supported the system’s reliability. The success of the system is further supported by stable network connectivity for data transmission, adequate lighting in the cultivation room during image capture, and automatic adjustment of temperature and humidity according to the mushroom growth phase. This system demonstrates its capability to monitor mushroom growth conditions automatically and accurately, offering a practical solution for supporting more modern and efficient mushroom cultivation practices.
Co-Authors Firdaus, Asep Rizki Kamdan Kharisma, Ivana Lucia Nopiandi, Andi Prayoga , Rizki Haddi Prayoga, Rizki Haddi Somantri, Somantri Tresna Ati, Mutiara Annisa