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All Journal Information Technology and Telematics Dinamik Jupiter Publikasi Eksternal Jurnal Buana Informatika Pixel : Jurnal Ilmiah Komputer Grafis JUITA : Jurnal Informatika Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Aiti: Jurnal Teknologi Informasi Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) International Journal of Social Learning (IJSL) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Informatika Teknologi dan Sains (Jinteks) Maritime Park: Journal Of Maritime Technology and Socienty Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Eduvest - Journal of Universal Studies Seminar Nasional Teknologi dan Multidisiplin Ilmu Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) INOVTEK Polbeng - Seri Informatika
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Journal : JURNAL ILMIAH INFORMATIKA

SISTEM PENERIMAAN SISWA BARU DI SMKN 3 PATI BERDASAR JALUR PRESTASI MENGGUNAKAN ALGORITMA KLASTERING K-MEANS BERBASIS WEB Yassin Achmad Nur Aziz; Eri Zuliarso
JURNAL ILMIAH INFORMATIKA Vol 10 No 02 (2022): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v10i02.5555

Abstract

The new student admission system that uses the K-Means algorithm data grouping is the simplest clustering pattern compared to other algorithms. This algorithm is one of the data mining. K-Means groups them into several clusters that have similarities and separates each cluster based on the differences between each cluster.The research of the K-Means Clustering algorithm aims to minimize the functions set during the Clustering process.The implementation of the K-Means Clustering algorithm into the clustering information system provides the results of an effective data grouping classification and the process of each literacy rotation of the Centroid distance, the determination of the Cluster point is formed, student data as a reference object saves more time on clustering the superior class. The application of this web-based clustering information system results in more flexible information that can be accessed at any time by users who are given access rights to utilize the data. The application of the K-Means Clustering Algorithm to get the results of the Superior Class clarification requires an information system implementation to form 3 clusters for each class, namely M1, M2 and M3. M1 as a high score with a criterion value of 85 to 100, M2 as a medium value with a criterion value of 75 to 80 and M3 as a low value with a criterion value of 10 to 70.
KLASIFIKASI TEKNIK BULUTANGKIS BERDASARKAN POSE DENGAN CONVULUTIONAL NEURAL NETWORK Aditya Bobby Rizki; Eri Zuliarso
JURNAL ILMIAH INFORMATIKA Vol 10 No 02 (2022): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v10i02.5559

Abstract

Convolutional Neural Network (CNN) is a deep learning algorithm which is the development of Multilayer Perception (MLP) which is designed to process data in two-dimensional form. At the stage of making the system there are several stages including sample data, data sources and data analysis methods. The dataset that is processed is the Badminton Technique, namely the Forehand Technique which consists of 374 images, the Service Technique consisting of 369 images and the smash technique consisting of 420 images with outliers of 146 images. After the data is cleaned of outliers, bootrapping is carried out again to unite all data from each separate class into one again. The results of this study say that the Classification of Badminton Techniques Based on Pose with Convolutional Neural Networks, it can be concluded that the process of testing pose classification with test data using several methods such as logistic regression, random forest, and KNN produces significant accuracy. values ranging from 80% to 90%.