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Journal articles on the topic 'Algorytm Kruskala'

Author: Grafiati

Published: 24 April 2022

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1

Paryati and Krit Salahddine. "The Implementation of Kruskal’s Algorithm for Minimum Spanning Tree in a Graph." E3S Web of Conferences 297 (2021): 01062. http://dx.doi.org/10.1051/e3sconf/202129701062.

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Kruskal’s Algorithm is an algorithm used to find the minimum spanning tree in graphical connectivity that provides the option to continue processing the least-weighted margins. In the Kruskal algorithm, ordering the weight of the ribs makes it easy to find the shortest path. This algorithm is independent in nature which will facilitate and improve path creation. Based on the results of the application system trials that have been carried out in testing and comparisons between the Kruskal algorithm and the Dijkstra algorithm, the following conclusions can be drawn: that a strength that is the existence of weight sorting will facilitate the search for the shortest path. And considering the characteristics of Kruskal’s independent algorithm, it will facilitate and improve the formation of the path. The weakness of the Kruskal algorithm is that if the number of nodes is very large, it will be slower than Dijkstra’s algorithm because it has to sort thousands of vertices first, then form a path.

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2

Paryati and Krit Salahddine. "The Implementation of Kruskal’s Algorithm for Minimum Spanning Tree in a Graph." MATEC Web of Conferences 348 (2021): 01001. http://dx.doi.org/10.1051/matecconf/202134801001.

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Kruskal’s Algorithm is an algorithm used to find the minimum spanning tree in graphical connectivity that provides the option to continue processing the least-weighted margins. In the Kruskal algorithm, ordering the weight of the ribs makes it easy to find the shortest path. This algorithm is independent in nature which will facilitate and improve path creation. Based on the results of the application system trials that have been carried out in testing and comparisons between the Kruskal algorithm and the Dijkstra algorithm, the following conclusions can be drawn: that a strength that is the existence of weight sorting will facilitate the search for the shortest path. And considering the characteristics of Kruskal’s independent algorithm, it will facilitate and improve the formation of the path. The weakness of the Kruskal algorithm is that if the number of nodes is very large, it will be slower than Dijkstra’s algorithm because it has to sort thousands of vertices first, then form a path.

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3

Choi, Myeong-Bok, and Sang-Un Lee. "An Efficient Implementation of Kruskal's and Reverse-Delete Minimum Spanning Tree Algorithm." Journal of the Institute of Webcasting, Internet and Telecommunication 13, no. 2 (2013): 103–14. http://dx.doi.org/10.7236/jiibc.2013.13.2.103.

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4

Li, Haiming, Qiyang Xia, and Yong Wang. "Research and Improvement of Kruskal Algorithm." Journal of Computer and Communications 05, no. 12 (2017): 63–69. http://dx.doi.org/10.4236/jcc.2017.512007.

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5

Zakiah, Nina. "DIAGRAM ALIR ALGORITMA KRUSKAL DALAM MENENTUKAN MINIMUM SPANNING TREE." JURNAL MATHEMATIC PAEDAGOGIC 5, no. 2 (2021): 151–57. http://dx.doi.org/10.36294/jmp.v5i2.1977.

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AbstractThe flow chart of the minimum spanning tree is represented in a weight matrix as a form of development of the link matrix. Determining the minimum spanning tree in this journal uses the Kruskal algorithm and is implemented in the form of a flow chart. This journal is a study of theories regarding the minimum spanning tree determination problem. With the problem approach, namely: graph theory, trees, determination of Minimum spanning tree, Kruskal's algorithm and flow diagrams. Furthermore, it is implemented in the flowchart of determining the Minimum spanning tree. Keywords: Minimum spanning tree, Kruskal's Algorithm, flow chart

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6

Wattimena, Abraham Z., and Sandro Lawalatta. "APLIKASI ALGORITMA KRUSKAL DALAM PENGOTIMALAN PANJANG PIPA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 7, no. 2 (2013): 13–18. http://dx.doi.org/10.30598/barekengvol7iss2pp13-18.

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Algoritma Kruskal dalam pencarian pohon merentang minimum dapat di aplikasikan pada jaringan pipa yang terpasang di lokasi jalan Ina Tuni Karang Panjang Ambon. Skripsi ini adalah penelitian yang bersifat studi kasus dan merupakan suatu aplikasi graf dalam kehidupan sehari-hari. Permasalahan yang dibahas dalam skripsi ini adalah panjang pipa yang terpasang pada jalan Ina Tuni Ambon sepanjang 1448 meter sedangkan panjang pipa dengan menggunakan algoritma Kruskal sepanjang 1026 meter. Dalam skripsi ini dititik beratkan pada pengoptimalan panjang pipa yang merupakan salah satu masalah pohon merentang minimum pada graf. Jaringan pipa akan direpresentasikan ke dalam bentuk graf terhubung, tak berarah dan berbobot.

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7

Aissa, Mohamed, Adel Ben Mnaouer, Rion Murray, and Abdelfettah Belghith. "New Strategies and Extensions in Kruskal’s Algorithm in Multicast Routing." International Journal of Business Data Communications and Networking 7, no. 4 (2011): 32–51. http://dx.doi.org/10.4018/jbdcn.2011100103.

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Multimedia applications are expected to guarantee end-to-end quality of service (QoS) and are characterized by stringent constraints on delay, delay-jitter, bandwidth, cost, and so forth. The authors observe that Kruskal’s algorithm is limited to minimal (maximal) spanning unconstrained tree. As such, the authors extend Kruskal’s algorithm to incorporate the delay bound constraint. Consequently, a novel algorithm is proposed, called EKRUS (Extended Kruskal), for constructing multicast trees. The EKRUS’ distinguishing features consists of a better management of Kruskal’s priority queues, and in the provision of edge priority aggregation. Preliminary results show that the proposed EKRUS algorithm performs as well as the best-known algorithms (such as the DDMC, DMCTc algorithms) while exhibiting reduced complexity. The authors conducted an intensive analysis and evaluations of different strategies of assigning edges into the classes of the queue as well as edge selection. As a result, the EKRUS algorithm was further extended with different edge assignment and selection strategies. Through extensive simulations, the authors have evaluated various versions of the EKRUS and analyzed their performance under different load conditions.

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8

Xiao, Hai. "RGV dynamic scheduling model based on kruskal algorithm." IOP Conference Series: Materials Science and Engineering 612 (October 19, 2019): 032028. http://dx.doi.org/10.1088/1757-899x/612/3/032028.

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9

Granera, Julia Argentina, Victor Manuel Valdivia, and Maria Elena Blandón Dávila. "Aplicación informática KPTS (Kruskal, Prim, Tabu Search)." Revista Científica de FAREM-Estelí, no. 17 (May 9, 2016): 81–90. http://dx.doi.org/10.5377/farem.v0i17.2616.

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En este artículo se muestra la aplicación de una herramienta informática basada en teoría de grafos para analizar y resolver problemas de las rutas más cortas, utilizando los algoritmos de Prim, Kruskal y de búsqueda local de Tabú Search. Para el desarrollo de esta aplicación se utilizaron los siguientes elementos: Visual Studio 2010, librería GraphSharp y librería QuickGraph. Para la creación de esta herramienta, se estableció una estructura de clases que diera soporte a los gráficos: 1) PocGraph: representa el grafo; 2) PocEdge: representa las aristas del grafo; y 3) PocVertex: representa los nodos o vértices del grafo. Tanto el método de Kruskal como Prim generan un árbol mínimo recubridor del grafo, el cual consiste en un subgrafo del original. El algoritmo de Prim se trabajó con el objetivo de encontrar el árbol recubridor más corto; mientras que el algoritmo de Kruskal, con la finalidad de hallar el árbol minimal a partir de instancias TSP. El método de Tabú Search se aplica para encontrar el mínimo camino cerrado que une todos los vértices o nodos. Se diseñó el algoritmo de Tabú Search para minimizar las rutas partiendo de una solución inicial la cual se va modificando hasta obtener el resultado.

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10

Mahardika, Fajar. "Penerapan Teori Graf Pada Jaringan Komputer Dengan Algoritma Kruskal." Jurnal Informatika: Jurnal Pengembangan IT 4, no. 1 (2019): 48–53. http://dx.doi.org/10.30591/jpit.v4i1.1032.

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11

Zhang, Bo, Ji Fang, and Zhan Xiong. "Work-flow Push System Based on Kruskal Algorithm Location." Journal of Physics: Conference Series 1087 (September 2018): 022023. http://dx.doi.org/10.1088/1742-6596/1087/2/022023.

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12

Sholikhatin, Siti Alvi, Adi Budi Prasetyo, and Ade Nurhopipah. "IMPLEMENTASI ALGORITMA KRUSKAL DAN ALGORITMA PRIM SUATU GRAPH DENGAN APLIKASI BERBASIS DESKTOP." Jurnal RESISTOR (Rekayasa Sistem Komputer) 3, no. 2 (2020): 89–93. http://dx.doi.org/10.31598/jurnalresistor.v3i2.638.

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A graph has several algorithms in its solution, including the Kruskal algorithm and Prim algorithm, both of which are greedy algorithms for determining the minimum spanning tree. Completion of graphs is useful in various fields of life, so an accurate graph calculation is important. Making an application to solve a graph, especially the Kruskal algorithm and Prim algorithm aims to facilitate the work of the graph so as to produce an accurate final result. The flow of research carried out are: a background review of research, study of literature and relevant literature, application design, building desktop-based applications, as well as implementation and application tests. The level of technological readiness or TKT in this research is based on self-assessment which is at level 7, meaning the prototype demonstration system in the actual environment, with details of the TKT indicators as follows: TKT indicator 1 is met, TKT indicator 2 is met, TKT indicator 3 is not met, TKT indicator 4, TKT indicator 5 are met, TKT indicator 6 are met, TKT indicator 7 is met, TKT indicator 8 and 9 are not met. The application that has been built is useful for completing a graph with the Kruskal algorithm and Prim algorithm. This research was conducted to answer the crucial needs of a weighted graph settlement application.

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13

Broutin, Nicolas, Luc Devroye, and Erin McLeish. "Note on the Structure of Kruskal’s Algorithm." Algorithmica 56, no. 2 (2008): 141–59. http://dx.doi.org/10.1007/s00453-008-9164-4.

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14

Park, Mee-Jeong, Hyun Heo, Tae-Gon Kim, Kyo Suh, and Jeong-Jae Lee. "Watershed Delineation Algorithm Using Kruskal's Algorithm and Triangulated Irregular Network." Journal of The Korean Society of Agricultural Engineers 48, no. 4 (2006): 3–12. http://dx.doi.org/10.5389/ksae.2006.48.4.003.

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15

R.Yadav, Sonam, and Ravi P. Patki. "Extraction of Best Attribute Subset using Kruskal's Algorithm." International Journal of Computer Applications 122, no. 16 (2015): 1–5. http://dx.doi.org/10.5120/21781-5058.

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16

Rathnasamy, Santhakumar, and Amutha Nagarajan. "Channel and Relay Assignment Using Kruskal Algorithm in LTE Networks." International Journal of Intelligent Engineering and Systems 11, no. 3 (2018): 280–87. http://dx.doi.org/10.22266/ijies2018.0630.30.

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17

Zhang, Jinpu, Guozhong Cao, Qingjin Peng, Runhua Tan, and Huangao Zheng. "Path Planning for Product Function Transformation based on Kruskal Algorithm." Computer-Aided Design and Applications 19, no. 4 (2021): 741–54. http://dx.doi.org/10.14733/cadaps.2022.741-754.

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18

Lastri, Devi, Masriani, Nadia W, Parizal Hidayatullah, Wahyu Ulfayandhie Misuki, and Mamika Ujianita Romdhini. "Meminimalisir Biaya Pembuatan Saluran Air PDAM Di Wilayah KLU Menggunakan Algoritma Kruskal." EIGEN MATHEMATICS JOURNAL 1, no. 1 (2019): 27. http://dx.doi.org/10.29303/emj.v1i1.22.

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The kruskal algorithm is an algorithm to search for minimum spanning trees directly based on the general MST (Minimum Spanning Tree) algorithm. In the kruskal algorithm, the sides in the graph are sorted first based on their weight from small to large. The kruskal algorithm in the search for a minimum spanning tree can be applied to the distribution of clean water of PDAM in North Lombok district. This problem is intended to get the shortest route for PDAM water distribution in North Lombok district in order to minimize costs.

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19

Kamyab, Gholam-Reza. "Optimal Feeder Routing and DG Placement Using Kruskal’s Algorithm." European Journal of Electrical Engineering 22, no. 1 (2020): 71–78. http://dx.doi.org/10.18280/ejee.220109.

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20

Choi, Won, Jae Won Lee, Myung-Hoe Huh, and Seung-Ho Kang. "An Algorithm for Computing the Exact Distribution of the Kruskal–Wallis Test." Communications in Statistics - Simulation and Computation 32, no. 4 (2003): 1029–40. http://dx.doi.org/10.1081/sac-120023876.

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21

Wibisono, Yuniarto Bagas, Yuniarto Bagas Wibisono, and YB Dwi Setianto. "PENCARIAN POHON PERENTANG MINIMUM PADA JARINGAN LISTRIK BANGUNAN DENGAN MENGGUNAKAN ALGORITMA KRUSKAL." Proxies : Jurnal Informatika 2, no. 1 (2021): 27. http://dx.doi.org/10.24167/proxies.v2i1.3198.

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22

Natarajan, Gowrishankar Subramaniam, R. Krishnaveni, and Frank J. Vijay. "Software Defined Networking (SDN) Based Video Service Using Djikstra’s Algorithm and Kruskal’s Algorithm." Journal of Computational and Theoretical Nanoscience 14, no. 9 (2017): 4539–44. http://dx.doi.org/10.1166/jctn.2017.6806.

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23

Dili, Yusufiani Nurlinawati. "PENYELESAIAN MASALAH TRANSPORTASI UNTUK MENCARI SOLUSI OPTIMAL DENGAN PENDEKATAN MINIMUM SPANNING TREE (MST) MENGGUNAKAN ALGORITMA KRUSKAL DAN ALGORITMA PRIM." KUBIK: Jurnal Publikasi Ilmiah Matematika 6, no. 1 (2021): 44–50. http://dx.doi.org/10.15575/kubik.v6i1.13907.

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Penelitian ini membahas tentang penyelesaian masalah transportasi dengan pendekatan Minimum Spanning Tree (MST) menggunakan algoritma Kruskal dan algoritma Prim untuk mencari solusi optimal. Algoritma Kruskal dan algoritma Prim merupakan algoritma dalam teori graf untuk mencari Minimum Spanning Tree (MST). Langkah algoritma Kruskal yaitu mengurutkan biaya dari yang terkecil hingga terbesar. Selanjutnya, pilih biaya yang paling terkecil. Kemudian, lakukan perhitungan dengan melihat sumber persediaan dan permintaan di setiap tujuan sampai semuanya terpenuhi, sehingga terlihat bentuk Minimum Spanning Tree (MST) dari algoritma Kruskal. Sedangkan langkah algoritma Prim yaitu dengan memilih sembarang titik atau sumber. Selanjutnya, pilih active edge dengan biaya terkecil. Kemudian, lakukan perhitungan dengan melihat sumber persediaan dan permintaan di setiap tujuan sampai semuanya terpenuhi, sehingga terlihat bentuk Minimum Spanning Tree (MST) dari algoritma Prim. Bentuk dari Minimum Spanning Tree (MST) menghasilkan solusi yang optimal. Dari hasil penelitian ini, pendekatan Minimum Spanning Tree (MST) dengan algoritma Prim yang lebih unggul.

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24

Ali Khan, Sajid, Ayyaz Hussain, Abdul Basit, and Sheeraz Akram. "Kruskal-Wallis-Based Computationally Efficient Feature Selection for Face Recognition." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/672630.

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Face recognition in today’s technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.

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25

Zhang, Yan, Xing-yi Wu, and Oh-kyoung Kwon. "Research on Kruskal Crossover Genetic Algorithm for Multi-Objective Logistics Distribution Path Optimization." International Journal of Multimedia and Ubiquitous Engineering 10, no. 8 (2015): 367–78. http://dx.doi.org/10.14257/ijmue.2015.10.8.36.

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26

Wulandari, Diah Ayu Retnani, and Fajrin Nurman Arifin. "Penentuan Rute Terpendek Jalur Distribusi Air Artesis Menggunakan Kruskal." J-SAKTI (Jurnal Sains Komputer dan Informatika) 2, no. 2 (2018): 121. http://dx.doi.org/10.30645/j-sakti.v2i2.72.

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Water flow distribution to home residents from the artesian well is affected by infrastructure. The more houses that distributed makes decreased amount supply of artesian water in every house. the longer of pipe leight and many branches traversed makes decrease of water supply because there will be many possible pipeline leaks. The more pipes used make the more expensive infrastructure cost. This problem occurs in Jubung area. These problems is one variation of the minimum Spanning Tree problems. This problem can be solved by the shortest path optimization route. It uses network method by implementing graph theory through kruskal algorithm. The first step is determined the nodes and sides. Nodes represent house and the sides represents the connecting pipes between houses. Kruskal is chosen because the kruskal focuses on the side and the graph is incomplete. focus of this study is the length optimation of the connecting pipe that is represented by side. In the pipe infrastructure figure map is representing of an incomplete graph because there are several nodes that are not connected to all nodes because it is adapted to the contour of the land that is not possible traversed the pipe. The results is there are several paths that are changed, especially the side to connect between node 1-4, 4-12, 19-20, 21-6 dispensed because forming cycles. The result of this research is kruskal can make decreasing infrastrukture cost Rp.7.535.500 with length of 201,5 meter so can save Rp.4.401.000 from Rp Rp.11.936.500.

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27

Made Ayu Ulandari, Ni, Amrullah Amrullah, Junaidi Junaidi, and Sri Subarinah. "Implementasi Algoritma Kruskal Dalam Menentukan Rute Terdekat Pada Tempat Pariwisata di Daerah Lombok Tengah." Griya Journal of Mathematics Education and Application 1, no. 4 (2021): 578–89. http://dx.doi.org/10.29303/griya.v1i4.117.

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When visiting several tourist attractions at once with limited time and an irregular schedule, of course, visitors choose the closest route to be able to visit all the places to be visited at once. This study uses an algorithm, namely the Kruskal algorithm, which aims to find the closest route to visit the five tourist attractions at once in Central Lombok Regency. The tourist attractions in question are Tanjung Aan Beach, Kuta Mandalika Beach, Sukarara Village, Sade Hamlet, Benang Kelambu Waterfall. This type of research is applied research which is generally a type of descriptive research that encourages further research. This study applies a modified Kruskal algorithm so as to produce 4 alternative closest routes with different starting points to be able to visit the five tourist attractions at once in Central Lombok Regency, namely 1) If the journey starts from Tanjung Aan Beach or Benang Kelambu Waterfall, the closest route is 60,1 km; 2) If the journey starts from Kuta Mandalika Beach, the closest route is 73.6 km; 3) If the journey starts from Sade Hamlet, the closest route is 73.6 km; 4) If the journey starts from Sukarara Village, the closest route is 82.9 km

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28

Didiharyono, Didiharyono, and Siti Soraya. "PENERAPAN ALGORITMA GREEDY DALAM MENENTUKAN MINIMUM SPANNING TREES PADA OPTIMISASI JARINGAN LISTRIK JALA." Jurnal VARIAN 1, no. 2 (2018): 1–10. http://dx.doi.org/10.30812/varian.v1i2.66.

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This article discusses the applied of greedy algorithm principle in finding the optimum solution in determine minimum spanning tree on graph. Graph theory is one of the studies in discrete mathematics that are widely applied in various scope. This article is a literature study and applied of nets electricity network optimization using Prims algorithm and Kruskal algorithm. Network Nets System is one type of electrical network system construction. Based on results of the study and discussion can be concluded that the application of greedy algorithm using Prims algorithm and Kruskal algorithm in determine minimum spanning tree on its principle is the same. However, after a comparison between the two algorithms we consider that the ideal algorithm used to optimize the nets electric network is the Kruskal algorithm because in the case of the electric network has few sides and many vertices.

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29

Swathika, O. V. Gnana, Nilabhra Banerjee, and S. K. Pranesh. "Hybrid Kruskal’s-Dijkstra’s Algorithm for Shortest Path Identification in Reconfigurable Microgrid." Advanced Science Letters 23, no. 5 (2017): 4215–18. http://dx.doi.org/10.1166/asl.2017.8310.

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30

Lee, Ju-Young. "An Efficient Implementation of Kruskal's Algorithm for A Minimum Spanning Tree." Journal of the Korea Society of Computer and Information 19, no. 7 (2014): 131–40. http://dx.doi.org/10.9708/jksci.2014.19.7.131.

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31

Afrianti, Afrianti, Effendi Effendi, and Des Welyyanti. "MENENTUKAN MINIMUM SPANNING TREE MENGGUNAKAN ALGORITMA MODIFIKASI DARI ALGORITMA PRIM DAN KRUSKAL DALAM PERENCANAAN RUTE WISATA YANG EFISIEN." Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam 3, no. 2 (2021): 103. http://dx.doi.org/10.32493/jsmu.v3i2.6706.

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Gunawan, Gunawan, and Melia Indah Cahyani. "Penerapan Algoritma Kruskal Dalam Mencari Lokasi Anjungan Tunai Mandiri Bank Rakyat Indonesia Cabang Bengkulu Berbasis Android." Journal of Technopreneurship and Information System (JTIS) 1, no. 2 (2018): 44–49. http://dx.doi.org/10.36085/jtis.v1i2.31.

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Seiring dengan usaha peningkatan kinerja dan pelayanan, penggunaan dan pemanfaatan teknologi informasi semakin diperlukan untuk menggantikan peran teknologi manual. Hal ini dikarenakan kemudahan yang dapat diperoleh dari penggunaan teknologi informasi untuk pengolahan data maupun penyajian informasi yang cepat dan akurat sangat membantu untuk meningkatkan kinerja organisasi agar lebih efisien dan efektif. Namun, seringkali banyak organisasi yang mengabaikan atau kurang memaksimalkan pemakaian teknologi informasi di dalam kegiatan operasionalnya. Hal ini dapat diatasi dengan menggunakan suatu sistem informasi geografis (SIG). Dimana pengguna bisa memasukkan nama ATM bank melalui input pencarian, kemudian sistem akan memberikan informasi yang diinginkan oleh pengguna. Bank Rakyat Indonesia (BRI) merupakan bank swasta terbesar di Indonesia saat ini yang memiliki banyak jaringan kantor cabang dan ATM (Anjungan Tunai Mandiri). Tujuan penelitian ini adalah membuat sistem informasi geografis lokasi ATM BRI Bengkulu dengan algoritma kruskal Menggunakan Android, sehingga dapat memberikan informasi letak lokasi ATM BRI pada Provinsi Bengkulu menggunakan algoritma kruskal berdasarkan latitude dan longitude sehingga menghasilkan informasi jarak yang akurat.Kata Kunci: Sistem Informasi Geografis,ATM, Algoritma Kruskal

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33

Mohamad, Hasmaini, Wan Iqmal Faezy Wan Zalnidzham, Nur Ashida Salim, Shahrani Shahbudin, and Zuhaila Mat Yasin. "Power system restoration in distribution network using minimum spanning tree - Kruskal’s algorithm." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (2019): 1. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp1-8.

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<span>Events such as natural and manmade interference, line, transformer and feeder outages that occur in electric power distribution system negatively impact the continuity of power supply, thus affecting the power demand supply as well as customer’s satisfaction. In that cases, the restoration of power needs to be carried out immediately in order to guarantee the system’s reliability. The power flow path identification is considered as a difficult task especially in a huge system due to large number of switches. Kruskal’s algorithm is presented in this paper to find the minimum power flow path in a power distribution network. The comparison of performance between presented Kruskal’s algorithm and Binary Particle Swarm Optimization (BPSO) was made in solving a problem regarding network reconfiguration. The proposed load restoration approach is tested on IEEE 33-bus single feeder radial distribution system using MATLAB software. From the results, it is found that the presented Kruskal’s algorithm was able to search for the minimal power flow path that contribute to loss reduction for power restoration after the occurrence of fault.</span>

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34

., Pavana D. "POWER SYSTEM RESTORATION IN DISTRIBUTION NETWORK THROUGH RECONFIGURATION USING MST-KRUSKAL’S ALGORITHM." International Journal of Research in Engineering and Technology 04, no. 07 (2015): 295–99. http://dx.doi.org/10.15623/ijret.2015.0407049.

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35

Headrick, Todd C. "JMASM6: An Algorithm For Generating Exact Critical Values For the Kruskal-Wallis One-Way ANOVA." Journal of Modern Applied Statistical Methods 2, no. 1 (2003): 268–71. http://dx.doi.org/10.22237/jmasm/1051748880.

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36

Putu, Diah Ni, and Muhammad Noor Fitriyanto. "PERBANDINGAN ALGORITMA PRIM, KRUSKAL, DJIKSTRA, DAN FLOYD-WARSHALL UNTUK MEMECAHKAN MASALAH MINIMUM SPANNING TRESS: Perancangan Pemasangan Pipa Saluran Air Bersih." Bitnet: Jurnal Pendidikan Teknologi Informasi 6, no. 2 (2021): 17–24. http://dx.doi.org/10.33084/bitnet.v6i2.2636.

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Kehidupan sehari-hari terdapat permasalahan mengenai optimasi yang dapat diselesaikan menggunakan pohon rentang minimum, atau dikenal dengan istilah Minimum Spanning Tree (MST), misalnya masalah mencari biaya termurah dalam perancangan pemasangan pipa saluran air bersih. Ilmu perancangan pipa termasuk dalam ilmu plumbing yang merupakan bagian dari ilmu teknik sipil. Peneliti tertarik untuk menggunakan algorima Prim, Kruskal, Djikstra, dan Floyd-Warshall untuk menyelesaikan masalah pohon merentang minimum pada pemasangan pipa saluran air dengan meneliti biaya minimumnya. hasil penelitian pohon rentang minimum dari jalur distribusi pipa saluran air bersih di Perumahan “X” dengan menggunakan algoritma Prim, Kruskal, dan Floyd-Warshall diperoleh total biaya minimum yang sama yaitu sebesar Rp. 5.600.000,-. Sedangkan algoritma Djikstra memperoleh total biaya sebesar Rp. 7.200.000,-. Ini berarti bila ingin mendapatkan biaya yang paling murah dalam merancang instalasi pipa untuk air bersih, seorang estimator bisa menggunakan algoritma prim, kruskal, ataupun Floyd-Warshall.

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Dutta, Joydeep, Partha Sarathi Barma, Samarjit Kar, and Tanmay De. "A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem." International Journal of Business Analytics 6, no. 1 (2019): 55–76. http://dx.doi.org/10.4018/ijban.2019010104.

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This article has proposed a modified Kruskal's method to increase the efficiency of a genetic algorithm to determine the path of least distance starting from a central point to solve the open vehicle routing problem. In a vehicle routing problem, vehicles start from a central point and several customers placed in different locations to serve their demands and return to the central point. In the case of the open vehicle routing problem, the vehicles do not go back to the central point after serving the customers. The challenge is to reduce the number of vehicles used and the distance travelled simultaneously. The proposed method applies genetic algorithms to find the set of customers those are covered by a particular vehicle and the authors have applied the proposed modified Kruskal's method for local routing optimization. The results of the new method are analyzed in comparison with some of the evolutionary methods.

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Maskar, Sugama, and Nicky Dwi Puspaningtyas. "CLUSTERING SUBJECTS IN LAMPUNG PROVINCIAL NATIONAL EXAMINATION OF JUNIOR HIGH SCHOOL THROUGH MAXIMUM SPANNING TREE." AKSIOMA: Jurnal Program Studi Pendidikan Matematika 10, no. 4 (2021): 2268. http://dx.doi.org/10.24127/ajpm.v10i4.4111.

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AbstrakUjian Nasional yang dilaksanakan setiap tahun di Indonesia telah menghasilkan banyak data, termasuk di Provinsi Lampung. Pada rentang tahun 2015 sampai dengan tahun 2018, telah terkumpul kurang lebih 11 juta data hasil ujian nasional. Data tersebut tentu dapat memberikan banyak informasi untuk perbaikan Pendidikan Indonesia di masa mendatang. Tulisan ini membahas tentang pengelompokan mata pelajaran pada Ujian Nasional SMP di Provinsi Lampung dengan tujuan untuk memetakan hubungan hasil belajar Matematika, IPA, Bahasa Indonesia, dan Bahasa Inggris. Tujuan pengelompokan tersebut sebagai analisa lebih jauh agar perbaikan pendidikan juga dapat mulai masuk pada ranah non-teknis, seperti penjadwalan, penyeusaian waktu belajar di kelas sampai dengan waktu pengerjaan tugas, serta perbaikan pada penentuan mata pelajaran yang menjadi syarat pelajaran lainnya. Analisis klusterisasi dilakukan dengan menggunakan Algoritma Kruskal berdasarkan graf maximum spanning tree (MST) dengan bantuan teknik statistik deskriptif dan inferensial. Data yang digunakan adalah hasil Ujian Nasional di Provinsi Lampung tahun 2017 dan 2018 sebanyak 15876 data. Graf MST ditentukan menggunakan algoritma Kruskal dan koefisien korelasi ditentukan menggunakan nilai koefisien korelasi Pearson. Hasil eksperimen menunjukkan bahwa Matematika, IPA, dan Bahasa Inggris memiliki korelasi yang paling kuat. Mata pelajaran yang paling kuat korelasinya dengan Bahasa Indonesia adalah Bahasa Inggris. Oleh karena itu, pemetaan mata pelajaran dapat disusun menjadi dua kelompok yaitu Matematika-IPA-Bahasa Inggris dan Bahasa Inggris- Bahasa Indonesia- IPA. Hasil tersebut dapat bermanfaat sebagai dasar pengambilan keputusan pada berbagai aspek teknis untuk mengoptimalkan hasil belajar siswa SMP khususnya di Provinsi Lampung. Kata kunci: Klusterisasi, Lampung, Ujian Nasional AbstractNational exams carried out every year in Indonesia have produced a lot of data, including in Lampung Province. In the span of 2015 to 2018, approximately 11 million national exam results have been collected. This data can certainly provide a lot of information for the improvement of Indonesian education. This paper discusses the clustering of subjects on junior high school national exams in Lampung Province with the aim of mapping the relationship of learning outcomes in Mathematics, Sciences, Indonesian, and English. The purpose of the clustering is as a further analysis so that educational improvements can also begin to enter the non-technical realm, such as scheduling, adjusting class and assignment time, and also determine prerequisite subjects. Clustering analysis is performed using an algorithm based on maximum spanning tree (MST) graphs with the help of descriptive and inferential statistical techniques. The data used are the results of national exams in Lampung province in 2017 and 2018 totaling 15.876 data. The MST Graph is determined using Kruskal’s algorithm and the coefficient of correlation is determined using Pearson coefficient of correlation value. The experimental results show that Mathematics, Natural Sciences, and English have the strongest correlation. Subject that has the strongest correlation with Indonesian is English. Therefore, the mapping of subjects can be arranged into two groups namely Mathematics- Sciences- English and English-Indonesian- Sains. These results can be useful as a basis for decision making on various technical aspects to optimize junior high school student learning outcomes, especially in Lampung Province.Keywords: Clusterization, Lampung, National Examination

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Zakiah, Nina. "Program Penentuan Pohon rentang minimum pada aplikasi Microsoft Visual Basic 6.0." JURNAL MATHEMATIC PAEDAGOGIC 5, no. 2 (2021): 145–50. http://dx.doi.org/10.36294/jmp.v5i2.1971.

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Pohon rentang minimum ini aplikasinya dapat direpresentasikan matriks bobot sebagai bentuk perkembangan dari matriks hubung. Menentukan pohon rentang minimum pada jurnal ini menggunakan algoritma kruskal dan diimplementasikan dalam bentuk pemrograman. Dalam tulisan ini digunakan bahasa pemrograman Visual basic Basic 6.0. Jurnal ini merupakan mengkaji teori-teori mengenai masalah Penentuan Pohon rentang minimum. Dengan pendekatan masalah yaitu : teori graf, pohon, penentuan pohon rentang minimum, algoritma Kruskal serta Microsoft Visual Basic 6.0. Selanjutnya di implementasikan pada program penentuan pohon rentang minimum. Kata kunci: Pohon Rentang Minimum, visual basic 6.0

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Bagui, Sikha, and Subhash Bagui. "An Algorithm And Code For Computing Exact Critical Values For The Kruskal-Wallis Nonparametric One-Way ANOVA." Journal of Modern Applied Statistical Methods 3, no. 2 (2004): 498–503. http://dx.doi.org/10.22237/jmasm/1099268400.

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Sarkar, Dipu, Abhinandan De, Chandan Kumar Chanda, and Sanjay Goswami. "Kruskal's Maximal Spanning Tree Algorithm for Optimizing Distribution Network Topology to Improve Voltage Stability." Electric Power Components and Systems 43, no. 17 (2015): 1921–30. http://dx.doi.org/10.1080/15325008.2015.1062818.

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M., Lady Silk. "Sistem Pendukung Keputusan Optimasi Biaya Dan Rute Pengiriman Barang Dengan Multimoda Menggunakan Algoritma Kruskal Dalam Optimasi Single Moda." Jurnal Penelitian 2, no. 3 (2017): 170–77. http://dx.doi.org/10.46491/jp.v2e3.93.170-177.

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Indonesia merupakan negara kepulauan terbesar didunia dengan lebih dari 17 ribu pulau. Untuk Negara yang terdiri dari kepulauan dan lautan ini dibutuhkan sistem angkutan antar moda, yang menghubungkan antar pulau yang tersebar, demi menciptakan sistem transportasi yang aman, efektif dan efisien. Pengiriman barang merupakan salah satu proses logistik yang utama, sehingga penting untuk dikaji lebih dalam. Dalam proses pengiriman barang, dibutuhkan suatu sistem yang dapat memberi gambaran tentang waktu dan biaya angkutan multimoda yang paling efektif dan efisien. Pada penelitian ini, dibangun Sistem Informasi yang dapat membantu user dalam mengambil keputusan untuk mengoptimalkan waktu dan biaya pengiriman barang, sehingga dapat menunjukan segala kemungkinan multimoda yang dapat digunakan dalam proses pengiriman barang. Sistem Multimoda yang diterapkan yaitu angkutan dari pabrik menuju ke pangkal jalur utama pertama pada suatu kota asal (stasiun kereta api, kargo pesawat udara, kargo pelabuhan kapal laut) menuju lokasi tujuan/transit. Dan angkutan dari jalur utama pada kota tujuan/transit menuju gudang, atau menuju jalur utama selanjutnya. Pada sistem informasi ini, hal yang dipertimbangkan adalah biaya dan waktu perjalanannya dari 4 moda, yaitu Darat, Laut, Udara dan Kereta Api, dengan mempertimbangkan segala kemungkinan multimoda yang dapat digunakan. Sistem ini juga dapat menghitung untuk lokasi tujuan yang hanya dapat dilalui moda tertentu saja. Pada perhitungan single moda, digunakan Algoritma Kruskal dalam perhitungan rute terpendek.

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Çakır, Esra, and Ziya Ulukan. "A Hybrid Kruskal’s Algorithm Based on Intuitionistic Fuzzy with Hamacher Aggregation Operator for Road Planning." International Journal of Fuzzy Systems 23, no. 4 (2021): 1003–16. http://dx.doi.org/10.1007/s40815-020-01007-4.

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Sun, Gang Ming, Hong Li, and Pin Wang. "An Algorithm to Find the First K Spanning Trees with Minimum Weights." Applied Mechanics and Materials 721 (December 2014): 535–38. http://dx.doi.org/10.4028/www.scientific.net/amm.721.535.

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By improving and extended Kruskal algorithm and using the backtracking method, we obtain an algorithm to find the first k spanning trees with minimum weights. Experiments show that for various types of randomly generated undirected connected graphs of which the number of nodes is 10,000, while the number of edges can be up to millions, this algorithm can obtain results within a few seconds.

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Munier, Badri, Muhammad Aleem, Muhammad Arshad Islam, Muhammad Azhar Iqbal, and Waqar Mehmood. "A Fast Implementation of Minimum Spanning Tree Method and Applying it to Kruskal’s and Prim’s Algorithms." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 58. http://dx.doi.org/10.30537/sjcms.v1i1.8.

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In last decade, application developers attained improved performances by merely employing the machines based on higher-clocked processors. However, in 2003 multi-core processors emerged and eradicated the old processor manufacturing technology based on increasing processor’s clock frequencies. After emergence of new parallel processor architectures, serial applications must be re-engineered into parallel versions to exploit the computing power of the existing hardware. In this paper, we present an efficient parallel implementation of minimum spanning tree algorithm to take advantage of the computing power of multi-core machines. Computer network routing, civil infrastructure planning and cluster analysis are typically use-cases of spanning tree problem. The experimental results show that the proposed algorithm is scalable for different machine and graph sizes. The methodology is simple and can easily be implemented using different shared-memory parallel programming models.

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Mora Gutiérrez, Román Anselmo, Javier Ramírez Rodríguez, Eric A. Rincón García, Antonin Ponsich, and Ana Lilia Laureano Cruces. "A HYBRID ALGORITHM FOR THE ROBUST GRAPH COLORING PROBLEM." Revista de Matemática: Teoría y Aplicaciones 23, no. 2 (2016): 421. http://dx.doi.org/10.15517/rmta.v23i2.25269.

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En este artículo se propone un algoritmo híbrido que combina técnicas de programación matemática (algoritmo de Kruskal y la estrategia de mantener consistencia de arcos para resolver el problema de satisfacción de restricciones) y métodos heurísticos (método de composición musical y DSATUR) para resolver el problema de coloración robusta de gráﬁcas (RGCP). Resultados experimentales muestran que este algorimo da mejores resultados que otros presentados en la literatura.

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Melnikov, B. F., and Y. Y. Terentyeva. "Building communication networks: on the application of the Kruskal’s algorithm in the problems of large dimensions." IOP Conference Series: Materials Science and Engineering 1047, no. 1 (2021): 012089. http://dx.doi.org/10.1088/1757-899x/1047/1/012089.

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Fan, Ya Qin, Chi Li, and Jing Ya Wang. "Research on PTN Networking Planning Technology." Advanced Materials Research 1070-1072 (December 2014): 2029–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.2029.

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This paper studied the technology of PTN networking structure and networking planning technology. We studied the key technology of PTN, the relevant algorithm to optimize PTN networking. Using matlab simulation software we realize kruskal algorithm to construct minimum spanning tree. The results of this study can improve the efficiency of the network, and reduce network construction, reduce the operation cost. It has good social and economic significance.

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Kheddam, Radja, Youcef Boudissa, and Aichouche Belhadj-Aissa. "Kruskal’s algorithm combined to multinet Bayesian network classifier for mapping Algiers urban areas using ALSAT2-A imagery." Journal of Applied Remote Sensing 12, no. 03 (2018): 1. http://dx.doi.org/10.1117/1.jrs.12.036007.

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Chen, Bang Ze, and Xiao Bo Yang. "Minimum Spanning Tree Dynamic Demonstration System Implementation." Applied Mechanics and Materials 397-400 (September 2013): 2526–30. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2526.

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The graph vertices design into classes, for each vertex in the design of the abscissa, ordinate and in-degree members, realizes the dynamic demonstration minimum spanning tree. Dynamic visualize Prime algorithm and kruskal algorithm implementation process. Around two window synchronization of animation, " in order to find the minimum edge " list box list the minimum edge of a minimum spanning tree ,with thick line in the left window drawing the found minimum edge and On the edge of the vertex, in the right box demo the process of algorithm dynamic execution.

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