Kismanti, Shinta Tri (2016) Penentuan Jaringan Logistik Pada Transportasi Laut Menggunakan Fuzzy C-Means Dan Minimum Spanning Tree Berbasis Hybrid Genetic Algorithm. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
Preview |
Text
1214201032-Master_Thesis.pdf - Accepted Version Download (1MB) | Preview |
Preview |
Text
1214201032-paper.pdf - Accepted Version Download (657kB) | Preview |
Preview |
Text
1214201032-presentation.pdf - Presentation Download (1MB) | Preview |
Abstract
Indonesia sebagai negara kepulauan dengan lebih dari 17.000 pulau dengan
wilayah perairan menjadi salah satu moda transportasinya. Dengan demikian
sebagian besar aktivitas terjadi di wilayah perairan, diantaranya distribusi logistik.
Pergerakan distribusi logistik tersebut akan menghasilkan pola rute suatu
pergerakan logistik. Penentuan pola jaringan pergerakan logistik yang optimal
dapat mendukung kelancaran dalam sistem pendistribusian. Pada penelitian ini
penentuan pola jaringan logistik dilakukan dengan dua tahapan. Tahap pertama,
akan dilakukan pengklasteran dengan menggunakan Fuzzy C-means,
pengklasteran bertujuan untuk mendapatkan kelompok-kelompok pulau yang
berada pada lokasi yang berdekatan. Tahap kedua setelah didapatkan hasil cluster
yang optimal, dengan menggunakan minimum spanning tree berbasis hybrid
genetic algorithm akan didapatkan pola jaringan yang optimal. Pola jaringan
tersebut akan menghubungkan pulau yang terpilih sebagai titik pusat ke pulaupulau
disekitarnya. Hasil uji coba pada proses pengklasteran menggunakan FCM
didapatkan jumlah cluster optimal sebanyak 3 cluster. Pada proses pembentukkan
MST berbasis hybrid GA digunakan parameter crossover rate 0,2 dan mutation
rate 0,4 dan diperoleh hasil terbaik ketika iterasi minimumnya konvergen pada
solusi optimal. Cluster 1 didapatkan hasil terbaik dengan ukuran populasi 100 dan
generasi maksimum 2000 dengan nilai fitness yaitu 9.41, cluster 2 dengan ukuran
populasi 100 dan generasi maksimum 1000 dengan nilai fitness yaitu 14.97, dan
cluster 3 dengan ukuran populasi 100 dan generasi maksimum 1000 dengan nilai
fitness yaitu 17.46.
=========================================================================================================
Indonesia as the island nation with more than 17.000 islands the the
territorial waters as one of the routes of transportation. For this condition, most of
activities are conducted in marine waters, such as logistic distribution. The
movement of logistics distribution will result in a movement pattern of the
logistics. Determining the pattern of movement of the logistics network that can
support optimal smoothness in the distribution system. In this study, determining
the pattern of the logistics network is done in two stages. The first stage, to be
carried out clustering using Fuzzy C-Means, clustering aims to get the island
groups that are at a nearby location. The second stage after the results obtained
optimal cluster, using the minimum spanning tree genetic algorithm-based hybrid
will get the optimal network pattern. The pattern of the network will connect the
island was chosen as the central point to the nearby islands. The results of trials on
the process of clustering using FCM obtain optimal cluster number as many as
three clusters. In the process of formation of the MST-based hybrid GA used
parameter crossover rate and mutation rate 0.2 0.4 and obtained the best results
when the minimum iteration converges toward the optimal solution. Cluster 1
obtained the best results with a population size of 100 and a maximum generation
in 2000 with a fitness value is 9.41, cluster 2 with a population size of 100 and a
maximum generation in 1000 with a value of fitness is 14.97 and cluster 3 with a
population size of 100 and a maximum generation in 1000 with a value of fitness,
namely 17.46.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RTMa 511.8 Kis p 3100016067087 |
| Uncontrolled Keywords: | Logistik, Minimum Spanning Tree, Fuzzy C-means, Hybrid Genetic Algorithm, Logistic, Minimum Spanning Tree, Fuzzy C-means, Hybrid Genetic Algorithm |
| Subjects: | Q Science > QA Mathematics > QA402.5 Genetic algorithms. Interior-point methods. |
| Divisions: | Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | - Davi Wah |
| Date Deposited: | 03 Mar 2020 03:34 |
| Last Modified: | 03 Mar 2020 03:34 |
| URI: | http://repository.its.ac.id/id/eprint/75185 |
Actions (login required)
 |
View Item |