Implementasi Bee Colony Optimization Pada Pemilihan Centroid (Klaster Pusat) Dalam Algoritma K-Means
-
Ika Arfiani
*
Universitas Ahmad Dahlan, Yogyakarta,
Indonesia
- Herman Yuliansyah Universitas Ahmad Dahlan, Yogyakarta, Indonesia
- Muhammad Dzikrullah Suratin Universitas Muhammadiyah Maluku Utara, Ternate, Indonesia (*) Corresponding Author
Abstract
Clustering is a method that is used to divide the data into several groups of parts. K-means (KM) is an algorithm that is often used in clustering, only just the result of KM often times get stuck in local optima i.e. the optimal solution (both maximum or minimal) on the candidate solution in the nearest neighbor only, not the whole of all existing solutions or what is commonly called the global optima. In this study aims to do improve the cluster determination process on the Kmeans algorithm using the Bee Colony Optimization (BCO) algorithm. BCO is an algorithm that works based on the way the bees search for food , BCO is famous for being able to escape from the local optima trap by recognizing which results are best from a series of optimal results . Combining BCO with KM begins with selecting a source of food early in random and using KM to resolve all the problems of clustering at every step BCO next and keep sources of food best in each iteration. The result of this research is that the BCOKM method has been proven to be able to solve the problem of data sharing, where the BCOKM method is able to form a good cluster, as shown by the resulting fitness value (the lowest value is 1221.53 and the highest value is 1233.28) all of which are better than the fitness value using K-means (1251.42). Likewise in terms of accuracy, where the use of BCOKM all showed better results (83.16%-83.30%) than the use of only K-means (83.09%)
Downloads
References
Prasetyo, E., 2012. Data Mining Konsep dan Aplikasi Menggunakan Matlab, Andi Offset, Yogyakarta.
Pham, D. T., Otri, S., Afifiy, A., Mahmuddin, M., & Al-Jabbouli, H. 2007. Data Clustering Using the Bees Algorithm.
Nainggolan, Rena., 2014. Algoritma Modified K-Means Clustering Pada Penentuan Cluster Centre Berbasis Sum Of Squared Error (SSE). Thesis. Program Studi S2 Teknik Informatika Fakultas Ilmu Komputer Dan Teknologi Informasi, Universitas Sumatera Utara, Medan.
Aggarwal, C. C. (2015). Data Mining: The Textbook. New York.
Nugroho, B. Ario,. Purwitasari, D., Fatichah, C., 2016. Implementasi Artificial Bee Colony Untuk Pemilihan Titik Pusat pada Algoritma K-means. Jurnal Teknik ITS, Vol. 5, No. 2, pp. A608-A613, ISSN:2337-3539
Armano, G., & Farmani, M. R. 2014. Clustering Analysis with Combination of Artificial Bee Colony Algoritihm and k-Means Technique. s.l. : International Journal of Computer Theory and Engineering.
Bonabeau, E., Dorigo, M., dan Theraulaz, G., 1999, Swarm Intelligence from Natural to Artificial Systems,
Oxford University Press, New York
P.-N. Tan, M. Steinbach, and V. Kumar. 2005. Introduction to Data Mining. Boston, MA, USA : Addison-Wesley Longman Publishing Co., Inc., 2005.
Pratama, J. Danar., 2019. Penerapan Algoritma Bee Colony Optimization Dalam Pencarian Rute Tercepat Rumah Sakit di Kota Bogor. Jurnal Riset Komputer (JURIKOM), Vol. 99 No. 99, ISSN 2407-389X (Media Cetak) Hal: 999-999.
Kaur, A., Goyal, S., 2011. A Survey on the Applications of Bee Colony Optimization Techniques, International Journal on Computer Science and Engineering(IJCSE), India, 8 Agustus 2011, vol. 3, 3037 –3046, ISSN : 0975 –3397.
Karaboga, D., & Ozturk, C. 2009. A Novel Clustering Approach: Bee Colony Optimization(BCO) algorithm.
R. E. Funderlic, M. T. Chu, N. Orlowski, D.Schlor, and J. Blevins, 2019. “Convergence andOther Aspects of the k-modes Algorithm forClustering Categorical data,”
Novianto, Rizki., 2017. Optimasi K-Means Dengan Artificial Bee Colony Untuk Pengelompokan Resolusi Tahun Baru Dari Data Twitter. Undergraduate Thesis. Institute Teknologi Sepuluh November.
E. Rahmandha, R. Efendi, and D. Puspitaningrum, 2016. “Aplikasi Pencarian Lokasi Fasilitas Peayanan Umum Terdekat Menggunakan Metode Artificial Bee Colony Di Kota Bengkulu Berbasis Webview Android,” J. Teknol. Inf., vol. 12, pp. 141–154.
Nugroho, Fajar 2019 Sistem Rekomendasi Kata Kunci Untuk Website Menggunakan Hybrid Semantic Relatedness Dan Associative Neural Network. Masters thesis, Universitas Komputer Indonesia.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Bee Colony Optimization Pada Pemilihan Centroid (Klaster Pusat) Dalam Algoritma K-Means
Pages: 756-763
Copyright (c) 2022 Ika Arfiani, Herman Yuliansyah, Muhammad Dzikrullah Suratin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
