Penerapan Algoritma Semut Dalam Penentuan Distribusi Jalur Pipa Pengolahan Air Bersih

*Agus Perdana Windarto -  STIKOM Tunas Bangsa Pematangsiantar, Indonesia
Sudirman Sudirman -  Universitas Bung Karno Jakarta, Indonesia
Received: 12 Apr 2018; Published: 25 Oct 2018.
Open Access
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Section: Research Articles
Language: EN
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Abstract

In general, the shortest path search can be divided into two methods, namely conventional methods and heuristic methods. Conventional methods tend to be more easily understood than heuristic methods, but when compared to the results obtained, heuristic methods are more varied and the time required for calculation is shorter. In the heuristic method there are several algorithms, one of which is the ant algorithm. An ant algorithm is an algorithm that is adopted from the behavior of ant colonies. Naturally ant colonies are able to find the shortest route on the way from the nest to the food sources. Ant colonies can find the shortest route between the nest and the source of food based on footprints on the trajectory that has been passed. The more ants that pass through a path, the more obvious the footprints will be. Ants Algorithms are very appropriate to be applied in solving optimization problems, one of which is to determine the shortest path. This study aims to facilitate the Development of Drinking Water Treatment Performance to make decisions in determining the point where the installation of water distribution pipelines that will be distributed to residents' homes. This study took 8 points of clean water treatment pipeline with starting point A and point N. Based on the calculation of clean water pipeline between A and N by using ant algortima, from two cycles passed, it is proven that the shortest path is only one pipeline, pipe N as destination with route length 4 as V1 → V2 → V3 → V4 → V8.

Keywords
Shortest path search; Heuristic; Ant Algorithm; Water pipe

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  1. Ambarsari, E.W., 2017. Modifikasi algoritma semut untuk optimasi probabilitas pemilihan node dalam penentuan jalur terpendek. Jurnal String, 2(2), 193–200.
  2. Biggs, N., 1986. The traveling salesman problem a guided tour of combinatorial optimization. Bulletin of the London Mathematical Society, 18(5), 514–515.
  3. Bronson, R., 1882. Theory and Problems of Operations Research. New York: McGraw Hill.
  4. Greco, F., 2008. Algoritma semut untuk penyelesaian travelling salesman problem. Jurnal Ilmiah MATRIK 10(2), 183–194.
  5. Ikhsan, J., 2016. Penerapan algoritma semut untuk optimisasi rute penjemputan barang pada tempat jasa penitipan sementara lion express, Jurnal Ilmiah Sarjana Mahasiswa UMRAH, (Vol. 1).
  6. Ismail, A.A., Herdjunanto, S., 2012. Penerapan algoritma ant system dalam menemukan jalur optimal pada traveling salesman problem (tsp) dengan kekangan kondisi jalan. Jnteti, 1(3),43-47.
  7. Munir, R., 2005. Matematika Diskrit. Edisi ketiga. In Bandung: Informatika.
  8. Nugroho, K., 2015. Penggunaan algoritma semut untuk penentuan optimisasi jalur tim marketing. INFOKAM, 2(9), 92–95.
  9. Putrama, A., Windarto, A.P., 2018. Analisis dalam menentukan produk bri syariah terbaik berdasarkan dana pihak ketiga menggunakan AHP. CESS (Journal of Computer Engineering System and Science), 3(1), 60–64.
  10. Sadewo, M. G., Windarto, A. P., Hartama, D., 2017. Penerapan datamining pada populasi daging ayam ras pedaging di indonesia berdasarkan provinsi menggunakan k-means clustering. Jurnal Nasional Informatika dan Teknologi Jaringan, 2(1), 60–67.
  11. Siregar, M.N.H., 2017. Neural network analysis with backpropogation in predicting human development index ( HDI ) component by regency / city in North Sumatera. Internatinal Journal Of Informations Systems and Technologyy (IJISTECH), 1(1), 22–33.
  12. Solikhun, Windarto, A.P., Handrizal, M.Fauzan., 2017. Jaringan saraf tiruan dalam memprediksi sukuk negara ritel berdasarkan kelompok profesi dengan backpropogation dalam mendorong laju pertumbuhan ekonomi. Kumpulan Jurnal Ilmu Komputer (KLIK), 4(2), 184–197.
  13. Sumijan, Windarto, A.P., Muhammad, A., Budiharjo, 2016. Implementation of neural networks in predicting the understanding level of students subject. Internat. Journal of Software Engineering and Its Applications, 10(10), 189–204.
  14. Triandi, B., 2012. Penemuan Jalur Terpendek Dengan Algoritma Ant Colony. Csrid, 4(2), 73–80.
  15. Windarto, A.P., 2017a. Implementation of data mining on rice imports by major country of origin using algorithm using k-means clustering method. International Journal of Artificial Intelligence Research, 1(2), 26–33.
  16. Windarto, A.P., 2017b. Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering. Techno.COM, 16(4), 348–357.
  17. Windarto, A.P., Dewi, L.S., Hartama, D., 2017. Implementation of artificial intelligence in predicting the value of indonesian oil and gas exports with bp algorithm. International Journal of Recent Trends in Engineering & Research (IJRTER), 3(10), 1–12.
  18. Yuwono, B., Aribowo, A.S., Wardoyo, S.B., 2009. Implementasi algoritma koloni semut pada proses pencarian jalur terpendek jalan protokol di Kota Yogyakarta. Seminar Nasional Informatika 2009, 111–120.
  19. Zer, P. P. P. A. N. W. F. I. R. H., Windarto, A.P., 2018. Analisis pemilihan rekomendasi produk terbaik prudential berdasarkan jenis asuransi jiwa berjangka untuk kecelakaan menggunakan metode analytic hierarchy process. Journal of Computer Engineering System and Science 3(1), 78-82.
  20. Zukhri, Z., 2005. Analisis algoritma semut untuk pemecahan masalah penugasan. SNASTI 2005, 47–51.