Implementasi Metode Improved K-Means Untuk Mengelompokkan Titik Panas Bumi

Shoolihah, Al-mar’atush (2017) Implementasi Metode Improved K-Means Untuk Mengelompokkan Titik Panas Bumi. Sarjana thesis, Universitas Brawijaya.

Abstract

Bencana adalah peristiwa atau rangkaian peristiwa yang mengancam dan mengganggu kehidupan dan penghidupan masyarakat yang disebabkan baik oleh faktor alam dan/atau faktor non alam maupun faktor. Salah satu bencana yang terjadi yaitu kebakaran. Kebakaran adalah nyala api yang terjadi baik itu kecil ataupun besar, menyala pada suatu daerah yang tidak diinginkan dan sulit untuk dikendalikan. Oleh karena itu harus dilakukan pencegahan dini, salah satu cara yaitu dengan titik panas bumi yang terdeteksi oleh satelit. Hal tersebut digunakan sebagai indikator kebakaran lahan dan hutan di suatu wilayah, sehingga semakin banyak titik hotspot, semakin banyak pula potensi kejadian kebakaran lahan di suatu wilayah. Oleh sebab itu diperlukan penerapan sistem yang dapat melakukan klasterisasi data titik panas bumi guna untuk menghasilkan keluaran sistem yang dapat melakukan pengelompokan data titik panas berisi informasi titik panas yang berpotensi menyebabkan kebakaran dengan berbagai status, seperti berpotensi tinggi, sedang, dan lemah. Improved K-Means merupakan salah satu metode clustering yang paling popular dan dapat digunakan untuk pengelompokan titik panas bumi. Algoritma ini melakukan proses clustering berdasarkan jarak maksimal sebagai pusat klaster dan pusat klaster tersebut akan dihitung jaraknya dengan data lain untuk dikelompokan. Hal tersebut dilakukan terus menerus sampai pengelompokan data kedalam klaster tidak berubah. Hal tersebut dibuktikan dalam penelitian ini dimana hasil dari evaluasi menggunakan silhouette coefficient memberikan nilai tertinggi 0.908000874 untuk nilai cluster 2 dan jumlah data 700.

English Abstract

Disaster is an incident or a series of incidents that threaten and disturb people's lives and livelihoods caused by both natural and / or non-natural factors. One of the disasters that happen is fire. Fire is a flame that occur either in small or large size, burning in an unexpected area and difficult to control. Therefore, early prevention is needed. one of the way is with geothermal point which is detected by the satellite. It is used as the indicator of land and forest fires in a region, so that the more geothermal point exist, the more potential for landfill incidents in a region. Hence, it is necessary to implement a system that can cluster the geothermal point data that has the potential in causing fire with farious status such as high, middle, and low potential. Improved K-Means is one of the most popular clustering methods and it can be used for geothermal point grouping. This algorithm performs clustering process based on the maximum distance as the cluster center and the cluster center distance will be calculated with the other data to be grouped. The calculation is done continuously until the data clustering does not change. That case is proven in this research where the evaluation result that uses silhouette coefficient give the highest point of 0.908000874 for the value of cluster 2 and the amount of data 700.

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTIK/2017/309/051705672
Uncontrolled Keywords: Titik Panas, Data Mining, Clustering, Improved K-Means, Silhouette Coefficient
Subjects: 000 Computer science, information and general works > 004 Computer science > 004.015 1 Finite mathematic
Divisions: Fakultas Ilmu Komputer > Teknik Informatika
Depositing User: Yusuf Dwi N.
Date Deposited: 31 Jul 2017 07:02
Last Modified: 10 Dec 2020 15:15
URI: http://repository.ub.ac.id/id/eprint/871
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