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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Computer Science
Sulistya, Budi
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Computer Science & IT Electrical & Electronics Engineering

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BFT water color classification in tilapia aquaculture using computer vision Suwandi, Bondan; Anggraeni, Sakinah Puspa; Palokoto, Toto Bachtiar; Sulistya, Budi; Sujatmiko, Wisnu; Septiawan, Reza; Taufik, Nashrullah; Rufiyanto, Arief; Ardiansyah, Arif Rahmat
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp497-508

Abstract

Biofloc technology (BFT) is one of the most promising aquaculture cultivation methods in the modern aquaculture era because of its high efficiency level, especially in water and fodder use. Usually, the general condition of the biofloc can be known from the color of the water. By utilizing the vision sensor, BFT color identification can be done automatically, which helps cultivators find out their BFT system’s condition. In this research, a classification was made for the watercolor of the BFT Tilapia system based on the microbial community color index (MCCI) value and the initial cultivation conditions where algae and nitrifying bacteria had not developed significantly. The color classifications of the bioflocs are clear, green, browngreen, green-brown, and deep-brown. Clear color is the new classification to indicate BFT water conditions in the initial cultivation phase. Further, two computer vision algorithm methods are introduced to classify the color of BFT system water. The first method combines the B/W algorithm and MCCI calculations, while the second algorithm uses the Manhattan distance algorithm approach. From the experiments that have been carried out, both computer vision algorithms methods for classifying biofloc colors have shown promising results.
Design and Implementation of IoT-Based Monitoring Battery and Solar Panel Temperature in Hydroponic System Rahmatullah, Rizky; Kadarina, Trie Maya; Irawan, Bagus Bhakti; Septiawan, Reza; Rufiyanto, Arief; Sulistya, Budi; Santiko, Arief Budi; Adi, Puput Dani Prasetyo; Plamonia, Nicco; Shabajee, Ravindra Kumar; Atmoko, Suhardi; Mahabror, Dendy; Prastiyono, Yudi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26729

Abstract

Hydroponics is currently widely used for the effectiveness of farming in narrow areas and increasing the supply of food, especially vegetables. This hydroponic technology grew until it collaborated with the internet of things technology, allowing users to monitor hydroponic conditions such as temperature and humidity in the surrounding environment. This technology requires electronic systems to obtain cost-effective power coverage and have independent charging systems, such as power systems using solar panels, where the power received by solar panels from the sun is stored in batteries. It must ensure that the condition of the battery and solar panels are in good condition. The research contribution is to create a solar panel temperature monitoring system and battery power using Grafana and Android Application. Apart from several studies, solar panels are greatly affected by temperature, which can cause damage to the panels. If the temperature is too high, the battery and panel temperature monitoring system can help monitor the condition of the device at Grafana and Android application with sensor data such as voltage, current, temperature and humidity that have been tested for accuracy. Accuracy test by comparing AM2302 sensor with Thermohygrometer and INA219 sensor with multimeter and clampmeter, both of which have been calibrated. The sensor data gets good accuracy results up to 98% and the Quality-of-Service value on the internet of things network is categorized as both conform to ITU G.1010 QOS data based on network readings on the wireshark application. QOS results are 0% Packet loss with very good category, 14ms delay with very good category and Throughput 71.85 bytes/s.  With the results of sensor accuracy and QOS, the system can be relied upon with a high level of sensor accuracy so that environmental conditions are monitored accurately and good QOS values so data transmission to the server runs smoothly.
Co-Authors Adi, Puput Dani Prasetyo Anggraeni, Sakinah Puspa Ardiansyah, Arif Rahmat Arief Budi Santiko, Arief Budi Atmoko, Suhardi Irawan, Bagus Bhakti Mahabror, Dendy Palokoto, Toto Bachtiar Plamonia, Nicco Prastiyono, Yudi Rahmatullah, Rizky Rufiyanto, Arief Septiawan, Reza Shabajee, Ravindra Kumar Sujatmiko, Wisnu Suwandi, Bondan Taufik, Nashrullah Trie Maya Kadarina