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All Journal Jurnal Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Techno.Com: Jurnal Teknologi Informasi Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information Systems Engineering and Business Intelligence Riau Journal of Computer Science Jurnal Teknologi dan Sistem Komputer JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal ULTIMA InfoSys Jurnal ULTIMATICS Jurnal Informatika Jurnal Informatika dan Sistem Informasi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Prosiding Seminar Nasional Program Pengabdian Masyarakat Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Atma Inovasia GIAT: Teknologi untuk Masyarakat International Journal of Information Technology and Computer Science Applications (IJITCSA) SmartComp International Journal of Information Technology and Business JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yuan Lukito
Universitas Kristen Duta Wacana
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Riau Journal of Computer Science

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Analisis Kinerja Struktur Data Kd-Tree Pada Metode K-Nearest Neighbors Yuan Lukito
RJOCS (Riau Journal of Computer Science) Vol. 2 No. 2 (2016): Riau Journal of Computer Science
Publisher : RJOCS (Riau Journal of Computer Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (85.654 KB) | DOI: 10.30606/rjocs.v2i2.866

Abstract

K-Nearest Neighbors is a commonly used classification technique that can be categorized into instance-based classification method. The performance of KNN is mostlydetermined by the size of the training data. This research compared and analyzed KD-Tree andArray data structures on KNN implementation. Dataset used in this research has largemultidimensional features. From the experiment conducted we can conclude that KD-Tree datastructure has better and relatively stable performance compared to Array data structure.
Co-Authors Aditya Wikan Mahastama Antonius R. Chrismanto, Antonius R. Antonius Rachmat Chrismanto Argo Wibowo Astuti, Laksmita Widya Budi Susanto Budi Susanto Daniel Felix Setiawan Dody Ivana Erinda Resha Astanti F. Yogiswara Adinugraha P Ginting Pebrindanov Gloria Virginia Halim Budi Santoso Hartono, Arfiananda Dhede Hizkia Juan Suryanto Hizkia Juan Suryanto, Hizkia Juan Katon Wijana Katon Wijana Katon Wijana Kevin Valiant Kristian Hartono, Renaldi Laurentius Kuncoro Probo Saputra Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lukas Chrisantyo Lukas Chrisantyo Naibaho, Yosep Parlindungan Nugroho Agus Haryono Pranatantri, Shalommita Purwadi, Joko Raden Gunawan Santosa Ragil Yoga Irawan Restyandito Rosa Delima Sagara Mahardika Sunaryo Setiawan, Daniel Felix Susilo, Anton Valonia Inge Santoso Verianto, Eko Wahju Satria Wibowo Widi Hapsari Willy Sudiarto Raharjo