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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Rizky Rahmatullah
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
Computer Science & IT Electrical & Electronics Engineering

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Analysis of IoT-LoRa to Improve LoRa Performance for Vaname Shrimp Farming Monitoring System Puput Dani Prasetyo Adi; Idil Ardi; Nicco Plamonia; Yuyu Wahyu; Angela Mariana L; Hessy Novita; Dendy Mahabror; Riza Zulkarnain; Adi Wirawan; Yudi Prastiyono; Waryanto Waryanto; Suhardi Atmoko Budi Susilo; Rizky Rahmatullah; Akio Kitagawa
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.27598

Abstract

Shrimp farming requires a touch that must be right on the side of water quality; water is a fundamental factor that must be met to achieve maximum yields. Many factors affect the quality of the water, but some things cause changes in water quality caused by external and internal factors causing death in shrimp. Disease conditions in shrimp can attack at any time, coupled with external factors such as extreme climate change, and cause changes in water components such as water pH, CaMg or hardness, and other factors that cause death in shrimp. Water turbidity oxygen demand (DO) in water determines the life of shrimp. It is coupled with microorganisms that must be maintained to maintain water quality for the growth of a Vaname shrimp. This research raises the Aquaculture System, specifically in the process of intelligent monitoring of water quality in shrimp nurseries to the shrimp harvest process, especially vaname shrimp from the results of observations use three sensors connected to LoRaWAN is able to provide real-time data from pond water and transmit it to LoRa Server or Internet Server, and the realtime data can be read through a Smartphone. This research analyzes in detail the ability of LoRaWAN to send multi-sensor data and Quality of Service LoRaWAN communication at different distances. This research also discusses how the LoRa antenna design can be developed to improve the performance of LoRa as transmitting devices or Radio Frequency 920-923 MHz for sending sensor data for Aquaculture.The contribution of this research is shown in the real-time monitoring system of the water environment, namely water pH, ammonia, turbidity, DO, salinity, water temperature, and nitrate in vaname shrimp ponds. The following contribution is the development of LoRaWAN with Tago IO servers capable of being used in Smart Aquaculture for contributions to The Things Network community or LoRaWAN Community.
Optimization of YOLOv4-Tiny Algorithm for Vehicle Detection and Vehicle Count Detection Embedded System Rachmat Muwardi; Ivan Prasetyo Nugroho; Ketty Siti Salamah; Mirna Yunita; Rizky Rahmatullah; Gregorius Justin Chung
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29693

Abstract

Currently, the implementation of object detection systems in the traffic sector is minimal. CCTV cameras on highways and toll roads are primarily used to monitor traffic conditions and document violations. However, the data recorded by these cameras can be further utilized to enhance traffic management systems. The author proposes a vehicle detection and counting system using YOLOv4-Tiny. The research aims to improve vehicle detection and counting accuracy by employing a median filter and grayscale processing, which simplify object detection. The proposed YOLOv4-Tiny algorithm has shown impressive results on various datasets, including MAVD, GRAM-RTM, and author dataset. The system achieved a detection accuracy of 98.95% on the MAVD dataset, 99.5% on the GRAM-RTM dataset (comparable to YOLOv4), and 99.1% on the author dataset. Furthermore, the system operates at 25 frames per second (FPS), a notably high rate compared to other methods. While the system demonstrates excellent accuracy in counting cars, it encounters some accuracy loss with other vehicle classifications. The author concludes that the system is highly suitable for real-world applications but notes that inaccurate labeling can lead to vehicle counting errors.
Raspberry Pi 4 and Ultrasonic Sensor for Real-Time Waste Classification and Monitoring with Capacity Alert System Yuliza Yuliza; Rachmat Muwardi; Prima Wijaya Kusuma; Lenni Lenni; Rizky Rahmatullah; Mirna Yunita; Akhmad Wahyu Dani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30036

Abstract

The problem of waste management creates daily rubbish buildup due to thorough sorting. garbage sometimes accumulates in public garbage receptacles due to officials' ignorance of bin capacity and collectors' schedules, causing unclean conditions and the development of deadly diseases. Internet of Things technology was used to create a smart waste classification system with a notification mechanism in this study. This system classifies waste into plastic, metal, B3, and organic using a Raspberry Pi 4, camera module, and deep learning model. The classification uses a Convolutional Neural Network to speed up waste processing and separation. This research can be linked with research on separating trash types in one container and then allocated to garbage bins by type. Ultrasonic sensors and Raspberry Pi 4 can continuously monitor waste levels by sending data to the Ubidots IoT platform over HTTP. Based on experimental device data, system analysis shows 90% classification accuracy for all four waste categories. A Wireshark network analysis showed 61,098 bytes/s of throughput, 16 ms of delay, and zero data loss, demonstrating the system's ability for real-time monitoring and alerting. This research provides a realistic, cost-effective, and minimal solution to improve garbage classification and reduce collection costs to promote sustainability.
Co-Authors Adi Wirawan Akhmad Wahyu Dani Akio Kitagawa Angela Mariana L Dendy Mahabror Gregorius Justin Chung Hessy Novita Idil Ardi Ivan Prasetyo Nugroho Ketty Siti Salamah Lenni Lenni Mirna Yunita Mirna Yunita Nicco Plamonia Prima Wijaya Kusuma Puput Dani Prasetyo Adi Rachmat Muwardi Riza Zulkarnain Suhardi Atmoko Budi Susilo Waryanto Waryanto Yudi Prastiyono Yuliza Yuliza Yuyu Wahyu