Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Teknika: Jurnal Sains dan Teknologi
Majahar Ali, Majid Khan
Universiti Sains Malaysia
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Hierarchical clustering algorithm-dendogram using Euclidean and Manhattan distance Mukhtar, Mukhtar; Majahar Ali, Majid Khan; Arina, Faula; Wicaksono, Agung Satrio; Ikhsan, Aulia; Budiaji, Weksi; Abdullah, Syarif; Pertiwi, Dinda Dwi Anugrah; Zidny, Robby; Oktarisa, Yuvita; Sukarna, Royan Habibie
Jurnal Teknika Vol 20, No 1 (2024): Available Online in June 2024
Publisher : Faculty of Engineering, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjst.v20i1.23187

Abstract

This paper presents the outcomes of a research experiment on the drying process of seaweed. There are numerous approaches to clustering data, such as partitioning and the Hierarchical Clustering Algorithm (HCA). The HCA has been implemented in binary tree structures to visualize data clustering. We conducted a comparative analysis of the four primary methodologies utilized in HCA, namely: 1) single linkage, 2) complete linkage, 3) average linkage, and 4) Ward's linkage. Clustering validation is widely recognized as a crucial issue that significantly impacts the effectiveness of clustering algorithms. Clustering validation can be identified, such as internal and external validation. Internal clustering validation, in particular, holds significant importance in the realm of data science. With this article, the main goal is to do an empirical evaluation of the traits that a representative set of internal clustering validation indices, namely Connectivity, Dunn, and Silhouette, show. In this paper, the HCA applies two distance functions between Euclidean and Manhattan distances to analyze the entanglement function and internal validity.
Co-Authors Arina, Faula Budiaji, Weksi Ikhsan, Aulia Mukhtar, Mukhtar Oktarisa, Yuvita Pertiwi, Dinda Dwi Anugrah Robby Zidny Sukarna, Royan Habibie Syarif Abdullah Wicaksono, Agung Satrio