Pribadi, Arif (2016) Skenario Distribusi CCTV Untuk Smart City Menggunakan Decision Tree. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Smart City telah berkembang ke beberapa negara, termasuk Indonesia. Dengan Smart City kebutuhan penduduk atas kesehatan, pendidikan, transportasi, komunikasi, keamanan akan mudah terwujud. CCTV sebagai salah satu pendukung Smart City banyak dipasang di berbagai sudut kota. Tujuannya agar pemerintah dapat memantau kondisi lingkungan. Perempatan, Pertigaan, bundaran merupakan daerah rawan kemacetan yang biasa dipantau oleh CCTV. Begitu juga dengan jalan sepi dan gelap berpotensi rawan tindak kejahatan. Pemilihan jenis CCTV akan berbeda untuk masing-masing daerah. misalnya daerah gelap akan diberikan dengan CCTV Infrared.
Penelitian ini akan menerapkan teknologi klasifikasi untuk membangun model prediksi. Model Prediksi akan digunakan untuk pemilihan CCTV yang tepat. Penelitian ini menggunakan 108 sampel yang telah dipasang di sebuah kota. Dengan algoritma decision tree (C4.5) diperoleh tingkat akurasi 89,81% model prediksi.
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Smart City has grown to several countries, including Indonesia. Smart City brings hope to ensure the welfare of the population. Smart City with the needs of the population's health, education, transport, communications, security will be easily realized. CCTV as one of the supporting Smart City widely installed in various corners of the city. The goal for the government is to monitor environmental conditions. Intersection, T-junction, the roundabout is an area prone to congestion commonly monitored by CCTV. as well as residents and dark lonely road it will be prone to crime. Selection of the type of CCTV is different for each region. eg dark areas will be given with Infrared CCTV.
This study will apply the classification technology to build predictive models. This prediction of the model will be used for the selection of the right CCTV. This study using 108 samples that have been installed in a city. With Decision Tree algorithm obtained accuracy rate of 89,81% prediction models
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Decision Tree , CCTV, Smart City |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science. EDP T Technology > T Technology (General) |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | - ARIF PRIBADI |
| Date Deposited: | 17 Mar 2017 03:05 |
| Last Modified: | 27 Dec 2018 04:21 |
| URI: | http://repository.its.ac.id/id/eprint/1902 |
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