Karima, Faiz Ainun (2021) Optimalisasi Penempatan Posisi Access point Pada Jaringan Wi-Fi Menggunakan Metode K- Means Clustering. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sering kali terjadi persebaran access point yang tidak merata dimana terdapat area dengan dengan banyak sinyal yang saling bertumbukan, sementara di lain area tidak terdapat sinyal sama sekali (blank spot). Oleh karena itu, penempatan posisi access point yang tepat pada jaringan WI-FI sangat diperlukan untuk mengoptimalkan jumlah access point yang digunakan serta kekuatan sinyal yang diterima tanpa mengorbankan fungsionalitasnya untuk coverage area yang sama.
Pada penelitian ini, pengukuran kekuatan sinyal digunakan untuk memperoleh jarak estimasi menggunakan metode Received Signal Strength Indicator (RSSI). Server menganalisa dengan menggunakan algoritma K-Means clustering untuk melakukan klasterisasi pada area pengamatan. Output dari klasifikasi ini adalah pemetaan area yang padat (traffict) dan area yang longgar sehingga dapat ditentukan coverage area maksimum dari tiap acces points. Hal ini dapat digunakan untuk optimasi penempatan access point dalam hal jumlah dan spesifikasinya.
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Many access points are distributed unevenly where there are areas with lots of signals colliding with each other, while in other areas there is no signal at all (blank spot). Therefore, proper positioning of the access point on the WI-FI network is necessary to optimize the number of access points used and the received signal strength without compromising its functionality for the same coverage area.
In this study, measurement of signal strength is used to obtain the estimated distance using the Received Signal Strength Indicator (RSSI) method. The server analyzes using the K-Means clustering algorithm to cluster the observation area. The output of this classification is the mapping of dense areas (traffic) and loose areas so that the maximum coverage area can be determined from each access point. This can be used to optimize the placement of access points in terms of their number and specifications.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Clustering, Classification, RSSI, K-Means, Clustering, Classification |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD108 Classification (Theory. Method. Relation to other subjects ) Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics) |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information Technology > 59201-(S1) Undergraduate Thesis |
| Depositing User: | FAIZ AINUN KARIMA |
| Date Deposited: | 10 Feb 2022 03:56 |
| Last Modified: | 04 Jul 2024 07:54 |
| URI: | http://repository.its.ac.id/id/eprint/92900 |
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