Pelacakan Dan Segmentasi Objek Bergerak Menggunakan Metode K-Means Clustering Berbasis Variasi Jarak

Prabowo, Dwi Puji; Latifah, Khoiriya; Pramunendar, Ricardus Anggi

doi:10.26877/jiu.v5i1.2818

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…Bagi semua orang ingin memperbaiki kondisi jalan rusak dengan cepat. Namun kondisi jalan harus diamati kemudian diidentifikasi lebih dulu (Prabowo D, Latifah K, 2019). Dari hasil identifikasi kemudian direncanakan perbaikan yang tepat untuk permukaan jalan berlubang.…”

Section: Pendahuluanunclassified

Segmentasi Jalan Berlubang Citra Jalan Raya Menggunakan Metode Thresholding Dan K-Means

Sukmawati,

Sadikin

2023

JTK

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- One of the important meanings for human life is the road. The road is used as a means of transportation which has very useful planning in the development of human life. In 2018, based on statistical data, the number of motor vehicle users in Indonesia is increasing every year, reaching 146,858,759 units. Seeing the number of the enhancement of motor vehicle users, the impact occured. Many roads are damaged and the conditions are very unsettling and concerned for the road users. One of the most common damages is that there are many holes in the road, which can increase the accidents’ risk. Based on science and technology development, one of the causes for the lengthy time to repair road damage is due to calculating the area of each damage manually and it takes a relatively long time. For this reason, this study aims to perform image processing in the form of segmentation on the image of a potholed road on the highway using the Thresholding algorithm and K-Means, followed by the calculation of the area and perimeter of the hole. The results of this study were obtained with 106 images of potholed roads, then segmented and calculated the area and circumference can be read properly using a program that has been designed. Keywords: Segmentation, Thresholding, K-Means

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Section: Pendahuluanunclassified

Segmentasi Jalan Berlubang Citra Jalan Raya Menggunakan Metode Thresholding Dan K-Means

Sukmawati,

Sadikin

2023

JTK

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“…Klasterisasi yang ada pada K-Means dilakukan dengan cara memisahkan data ke dalam kelompok yang berbeda atau biasa disebut dengan cara partitioning. Cara ini mampu meminimalkan rata-rata jarak setiap data ke klasternya dan mampu melakukan komputasi lebih cepat dari pengelompokan hirarkis [12]. Berikut tahapan K-Means:…”

Section: H K-meansunclassified

Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa

Pratama¹,

Prasetiadi²,

Usada³

2020

JuTISI

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The current presence system can be done with a computerized system, one of which is the face biometric system. This study focuses on the application of position estimation and tracking based on clustering on people's faces to determine the position in three dimensions. Position estimation can be obtained by making a kernel that is ready to be used to predict three-dimensional coordinates of faces based on two-dimensional coordinates of two images. Position estimation can be done by utilizing the Machine Learning algorithm family. In this study, Least Absolute Shrinkage and Selection Operators (LASSO) is used to perform the position estimation. Meanwhile, clustering in this study uses the K-Means algorithm. Based on the test results, the kernel error obtained in estimating the face location is 9.23 cm. The tracking accuracy of an object based on clustering is 100%.

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Pelacakan Dan Segmentasi Objek Bergerak Menggunakan Metode K-Means Clustering Berbasis Variasi Jarak

Cited by 2 publications

References 1 publication

Segmentasi Jalan Berlubang Citra Jalan Raya Menggunakan Metode Thresholding Dan K-Means

Segmentasi Jalan Berlubang Citra Jalan Raya Menggunakan Metode Thresholding Dan K-Means

Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa

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