Hermawan, Yusuf Dimas (2017) Implementasi Algoritma K-Nearest Neighbors dengan Particle Swarm Optimization dalam Klasifikasi Trouble pada Base Transceiver Station (BTS). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Teknologi Informasi adalah suatu teknologi yang digunakan untuk mengolah data, termasuk memproses, mendapatkan, menyusun, menyimpan, atau memanipulasi data dalam berbagai cara untuk menghasilkan informasi yang berkualitas yaitu informasi yang relevan, akurat dan tepat waktu Maka demikian, trafik kebutuhan akses data, voice maupun SMS membuat banyak pengembang operator seluler mulai mengembangkan fitur-fitur canggih yang dapat menjawab berbagai kebutuhan pengguna. Salah satu fitur yang dimaksud adalah berupa pengembangan teknologi jaringan berupa layanan 1G, 2G, 3G hingga 4G. Akan tetapi teknologi-teknologi yang dianggap semakin canggih ini tetap memiliki keterbatasan sehingga tak jarang jika mengalami beberapa kerusakan (error) pada sistem.Pada tugas akhir ini penulis berupaya melakukan klasifikasi error pada Base Transceiver Station (BTS) secara otomatis. Menggunakan K- Nearest Neighbors (KNN) yang dioptimasi dengan Particle Swarm Optimization (PSO).Dataset yang digunakan dalam proses uji coba merupakan 8 dataset dari UCI machine learning dan 1 dataset dari salah satu perusahaan Telekomunikasi di Indonesia yang dibagi menjadi 4 dan 5 bagian sebagai data testing dengan menggunakan K-Fold Validation. Akurasi terbaik sebesar 100%.
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Information Technology is a technology used to process data, including processing, obtaining, compiling, storing, or manipulating data in various ways to produce quality information that is relevant information, accurate and timely Thus, traffic needs data access, voice and SMS makes many developers mobile operators begin to develop advanced features that can answer the various needs of users. One of the features in question is the development of network technology in the form of services 1G, 2G, 3G up to 4G. However, these technologies are considered increasingly sophisticated still have limitations so that not infrequently if you experience some damage (error) on the system.
In this final project the author attempts to classify the error on Base Transceiver Station (BTS) automatically. Using K- Nearest Neighbors (KNN) optimized with Particle Swarm Optimization (PSO).
The dataset used in the process is 8 datasets from UCI machine learning and 1 dataset from one Telecommunication company in Indonesia which is divided into 4 and 5 sections as data testing using K-Fold Validation. Best accuracy of 100%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSIf 006.312 Her i-1 3100018074291 |
| Uncontrolled Keywords: | K-Nearest Neighbors; Particle Swarm Optimization; BTS; Classification. |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Q Science > QA Mathematics > QA76.9 Computer algorithms. Virtual Reality. Computer simulation. Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics) |
| Divisions: | Faculty of Information Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Yusuf Dimas Hermawan |
| Date Deposited: | 08 Feb 2018 02:11 |
| Last Modified: | 11 Jun 2020 07:06 |
| URI: | http://repository.its.ac.id/id/eprint/49526 |
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