Modifikasi Algoritma Semut untuk Optimasi Probabilitas Pemilihan Node dalam Penentuan Jalur Terpendek

Erlin Windia Ambarsari(1*)

(1) Program Studi Informatika, Universitas Indraprasta PGRI
(*) Corresponding Author

Abstract


LPPM Unindra's Dipa Research Grantprogress report on the searching of the shortest route previously made by the researcher with the title Analysis of the Effectiveness of the Shortest Route Using Ant Algorithm Method and LeadTime Approach: Case Study of Toko Gamis Murah Jakarta in 2014 showsan imperfect route selection, especially the one which is randomly conducted, leading to the chance of output route to be selected is once in a cycle. This causes the iteration conductedtosearch the shortest route will take a longer time. The purpose of this study is to modify the algorithm for finding the shortest route by inserting the Fuzzy C-Means Algorithm into node selection probability of the ant algorithm to allow the route chosen during the cycle to continuouslyperform that iteration conducted in the shortest route searching calculation is not too long because the route value convergence is more quickly obtained. This research collects qualitative data from goods delivery route to Toko Gamis Murah Jakarta's customers, with the result showing CGFEDBA route (32.5 km) with 100% probability, DFEGCBA (34.6 km) with 93. 75% probability and FGEDCBA (37.5 km) with 93.75% probability.

Keywords


Shortest Route, Ant Algorithm, Fuzzy C-Means Algorithm, Probability

Full Text:

PDF (Indonesian)

References


Ambarsari, E.W., Marlina, D., dan Susanto, A. Analisa Efektifitas Jalur Terpendek Dengan Menggunakan Algoritma Semut Dan Pendekatan LeadTime : Studi Kasus Toko Gamis Murah Jakarta. Laporan PertanggungJawaban Penelitian Hibah Dipa LPPM Unindra.Jakarta : LPPM Unindra. 2014.

Mutakhiroh, I., Indrato.,dan Hidayat, T. Pencarian Jalur Terpendek Menggunakan Algoritma Semut.Seminar Nasional Aplikasi Teknologi Informasi 2007 (SNATI 2007). B-81-B-85. 2007.

Chen, E.dan Liu, X. Multi-Colony Ant Algorithm. In Ant Colony Optimization-Methods and Applications. Pengeditanoleh Avi Ostfeld. InTech. 2011.

Ismail, A.A., Herdjunanto, S., dan Priyatmadi. Penerapan Algoritma Ant System Dalam Menemukan Jalur Optimal Pada Traveling Salesman Problem (TSP) Dengan Kekangan Kondisi Jalan. JNTETI. 1(3) : 43-48. 2012.

Astria, D., dan Suprayogi. Penerapan Algoritma Fuzzy C-Means Untuk Clustering Pelanggan Pada CV. Mataram Jaya Bawen, Eksplora Informatika. 6(2) : 169-178. 2017.

Kanade, P. M., dan Hall, L. O. Fuzzy ants as a clustering concept. In Fuzzy Information Processing Society. 22nd International Conference of the North American. NAFIPS. IEEE. 227-232. 2003.

Krishnan, P.H., dan Ramamoorthy, DR. P. Fuzzy Clustering Based Ant Colony Optimization Algorithm For Mr Brain Image Segmentation, Journal of Theoretical and Applied Information Technology. 65(3) : 644-649. 2014.




DOI: http://dx.doi.org/10.30998/string.v2i2.2106

Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 STRING (Satuan Tulisan Riset dan Inovasi Teknologi)

 

STRING (Satuan Tulisan Riset dan Inovasi Teknologi) indexed by:



Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi 4.0 Internasional.
View My Stats

Flag Counter