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All Journal Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering)
Rufiyanto, Arief
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Control & Systems Engineering Electrical & Electronics Engineering

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An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route Trihatmo, Sardjono; Hendrantoro, Gamantyo; Septiawan, Reza; Setijadi, Eko; Rufiyanto, Arief
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 8, No 1 (2024): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v8i1.387

Abstract

Electromagnetic Interference (EMI) can cause a malfunction of on-board electronic circuits in an autonomous electric vehicle and supporting electronic devices located in the environment of autonomous electric vehicles as well. In order to navigate an autonomous electric vehicle safely, it is important to have electromagnetic field characteristic in the environment. Since the information of electromagnetic field characteristic is hard to find, it needs to be modeled. This paper presents a model of electromagnetic field characteristic that is generated by using autoregression in order to estimate potential EMI. The EMI estimation is based on electromagnetic characteristic in an environment. Unlike other applications that use time history of data to build a model, we present a spatial electromagnetic field strength data in a previous route to estimate the future data in a new route. To obtain historical data for auto-regression process, we measured electric field strengths along a circular route in a campus near Jakarta. This surrounding environment represents a typical area of suburbs. The input variables for auto-regression process are the first 27 correlated data of 155 measured data. The result shows that the use of 13 predictor coefficient produces a variance of prediction error near to zero, with an improvement from maximum prediction error of 15.1257 to prediction error of 0.1862.
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.
Co-Authors Anggraeni, Sakinah Puspa Ardiansyah, Arif Rahmat Diana, Mery Gamantyo Hendrantoro Palokoto, Toto Bachtiar Refdinal Nazir Sardjono Trihatmo Septiawan, Reza Setijadi, Eko Sujatmiko, Wisnu Sulistya, Budi Suwandi, Bondan Taufik, Nashrullah