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p-Index From 2019 - 2024
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This Author published in this journals
All Journal International Journal of Artificial Intelligence Research Indonesian Journal of Electrical Engineering and Computer Science
Jatmiko Endro Suseno
Diponegoro University
Computer Science & IT Electrical & Electronics Engineering

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Volume Determination of Symmetrical Object with Distance Parameter Using Linear Regression Method Jatmiko Endro Suseno; Agus Setyawan; Isnain Gunadi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.906

Abstract

The object’s volume is a consideration in determining the quantity of products such as eggs, fruit, piles of rice, or sand. This research aims to obtain a system for determining the volume of a symmetrical object using the linear regression method in real-time, faster, more effective, and more enjoyable. This research uses segmentation methods and linear regression to determine the volume of a symmetrical object. The objects are a pile of rice and eggs which have symmetrical shapes. The shape of the symmetry in each object is a cone for a pile of rice and an oval for an egg. The results of this research are a system of symmetrical object volume determination using the linear regression method with an accuracy score of 96.48% for piles of rice and 97.84% for an egg. This system has limitations, there are the volume value must be in the data range that has been trained and the camera phone must be the same.
Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm Wiwit Agus Triyanto; Kusworo Adi; Jatmiko Endro Suseno
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp407-418

Abstract

The chicken farming industry is one of the biggest food industries that supports the achievement of food security internationally. Farmers need an independent tool that can monitor the welfare conditions of chickens in cages. Using their tools, farmers can ideally detect the condition of chickens. Lameness chickens, can be known for activity and dredging of their location in the cage. Occlusion, and background in the cage are interesting challenges. By observing behavior, image handling practices can be used to identify tainted chicks and provide an early warning of sickness in chickens. In this study, you only look once, version 8 (YOLOv8) which is a convolutional neural network (CNN) network model was chosen to perform the detection, tracking, and mapping of chicken locations. YOLOv8 was combined with various algorithm optimizers to improve training performance, such as root mean square (RMS) Prop, stochastic gradient descent (SGD), ADAM, and ADAMW. Multi-object tracking algorithms such as BOT-sort and ByteTrack are also used to improve tracking performance. Based on the results, YOLOv8 with combinations of optimizer algorithms ADAMW has the best mAP, support, precision and F1-score values compared to the others, with 0.936, 0.993, 0.990, 0.991. Meanwhile, for multi object tracking, ByteTrack is faster in inference time(s) values compared to the others, with 0.2.
Co-Authors Agus Setyawan Isnain Gunadi Kusworo Adi Wiwit Agus Triyanto