Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis

  • Siti Ramadhani UIN Sultan Syarif Kasim
  • Dini Azzahra
  • Tomi Z
Keywords: Davies Bouldin Index, K-Means, K-Medoids, Thesis, Computed Time

Abstract

The thesis is one of the scientific works based on the conclusions of field research or observations compiled and developed by students as well as research carried out according to the topic containing the study program which is carried out as a final project compiled in the last stage of formal study. A large number of theses, of course, will be sought in looking for categories of thesis topics, or the titles raised have different relevance. However, the student thesis can be by topics that are almost relevant to other topics so that it can make it easier to find topics that are relevant to the group. One of the uses of techniques in machine learning is to find text processing (Text Mining). In-text mining, there is a method that can be used, namely the Clustering method. Clustering processing techniques can group objects into the number of clusters formed. In addition, there are several methods used in clustering processing. This study aims to compare 2 cluster algorithms, namely the K-Means and K-Medoids algorithms to obtain an appropriate evaluation in the case of thesis grouping so that the relevant topics in the formed groups have better accuracy. The evaluation stage used is the Davies Bouldin Index (DBI) evaluation which is one of the testing techniques on the cluster. In addition, another indicator for comparison is the computation time of the two algorithms. According to the DBI value test carried out on algorithm 2, the K-Medoids algorithm is superior to K-Means, where the average DBI value produced by K-Medoids is 1,56 while K-Means is 2,79. In addition, the computational time required in classifying documents is also a reference. In testing the computational time required to group 50 documents, K-Means is superior to K-Medoids. K-Means has an average computation time for grouping documents, which is 1 second, while K-Medoids provide a computation time of 26,7778 seconds.

Downloads

Download data is not yet available.

References

R. Siti, “Sistem Pencegahan Plagiarisme Tugas Akhir Menggunakan Algoritma Rabin-Karp (Studi Kasus: Sekolah Tinggi Teknik Payakumbuh),” J. Teknol. Inf. Komun. Digit. Zo., vol. 6, no. 1, pp. 44–52, 2015.

S. Andika and S. Ramadhani, “Rancang Bangun Sistem Informasi Pendayagunaan Aset Dinas Perkebunan Provinsi Riau,” J. Teknol. Dan Sist. Inf. Bisnis - JTEKSIS, vol. 3, no. 2, pp. 387–394, Jul. 2021, doi: 10.47233/JTEKSIS.V3I2.298.

S. R. Alvin Anzas Islami, “Rancang Bangun Sistem Pendataan Hardware,” J. Teknol. Dan Sist. Inf. Bisnis - JTEKSIS, vol. 3, no. 2, pp. 412–418, 2021.

Syah Maulana Ramadhan; Siti Ramadhani; Tomi Z;, “Perancangan Website Masyarakat Peduli Sampah Kelurahan Ratu Sima,” J. Has. Penelit. dan Pengkaj. Ilm. Eksakta, vol. 01, no. 01, pp. 40–49, 2022.

R. Simaremare, “Implikasi Perkembangan Dan Internet Diindonesia,” Teknologi Informasi Dan Dunia Pendidikan, Vol. 2, 2009.

D. Ayu Et Al., “Pengukuran Kemiripan Dokumen Teks Bahasa Indonesia Menggunakan Metode Cosine Similarity,” E-Journal Teknik Informatika, Vol. 9, No. 1, Pp. 1–8, 2016.

F. Nur, M. Zarlis, And B. B. Nasution, “Penerapan Algoritma K-Means Pada Siswa Baru Sekolah Menengah Kejuruan Untuk Clustering Jurusan,” No. 9, Pp. 100–105, 2015.

S. Sindi, W. R. O. Ningse, I. A. Sihombing, F. Ilmi R.H.Zer, And D. Hartama, “Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia,” Jti (Jurnal Teknologi Informasi), Vol. 4, No. 1, Pp. 166–173, 2020.

E. Muningsih, “Komparasi Metode Clustering K-Means Dan K-Medoids Dengan Model Fuzzy Rfm Untuk Pengelompokan Pelanggan,” Evolusi : Jurnal Sains Dan Manajemen, Vol. 6, No. 2, 2018, Doi: 10.31294/Evolusi.V6i2.4600.

D. Azzahra dan S. Ramadhani, “Pengembangan Aplikasi Online Public Access Catalog (Opac) Berbasis Web Pada Stai Auliaurrasyiddin Tembilanan,” Jurnal Teknologi Dan Sistem Informasi Bisnis, Vol. 2, No. 2, Pp. 152–160, 2020.

R. A. Atmala And S. Ramadhani, “Rancang Bangun Sistem Informasi Surat Menyurat Di Kementrian Agama Kabupaten Kampar,” Jurnal Intra Tech, Vol. 11, No. 2, Pp. 56–62, 2018.

R. Nazwita, Siti, “Analisis Sistem Keamanan Web Server Dan Database Server Menggunakan Suricata,” In Seminar Nasional Teknologi Informasi, Komunikasi Dan Industri (Sntiki) 9, 2017, Pp. 308–317.

S. Ramadhani, S. Saide, And R. E. Indrajit, “Improving Creativity Of Graphic Design For Deaf Students Using Contextual Teaching Learning Method (Ctl),” In Acm International Conference Proceeding Series, 2018, Pp. 136–140. Doi: 10.1145/3206098.3206128.

M. R. Saputra, S. Ramadhani, And S. Baru, “Sistem Informasi Bantuan Dana Hibah Operasional Rumah Ibadah Kabupaten Bengs,” Jurnal Teknologi Dan Informasi Bisnis, Vol. 3, No. 1, P. 148, 2021.

S. Ramadhani, “A Review Comparative Mammography Image Analysis On Modified Cnn Deep Learning Method,” Indonesian Journal Of Artificial Intelligence (Ijaidm), Vol. 4, No. 1, Pp. 54–61, 2021.

M. R. Asyari And S. Ramadhani, “Sistem Informasi Arsip Surat Menyurat,” Jurnal Teknologi Dan Informasi Bisnis, Vol. 3, No. 1, Pp. 175–184, 2021.

Published
2022-05-27
How to Cite
Ramadhani, S., Azzahra, D., & Z, T. (2022). Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 13(1), 24-33. https://doi.org/10.31849/digitalzone.v13i1.9292
Abstract viewed = 251 times
PDF downloaded = 266 times