SAMOSIR, VIRNA ULI (2020) PREDIKSI JUMLAH ARMADA BUSWAY TRANSJAKARTA MENGGUNAKAN JARINGAN SYARAF TIRUAN. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
ABSTRAK Penggunaan transportasi umum dianjurkan oleh pemerintah dalam upaya untuk mengurangi kemacetan di ibukota , Jakarta. Pemerintah ibukota Jakarta membentuk sistem transportasi Bus Rapid Transit (BRT) yang beroperasi sejak tahun 2004 yang disebut dengan Transjakarta. Pengguna transportasi ini semakin banyak setiap tahunnya, sehingga PT. Transjakarta perlu memperbanyak jumlah armada busway pada koridor-koridor dengan penumpang dengan kuota yang banyak. Jaringan syaraf tiruan dapat untuk memprediksi jumlah armada busway dengan cara pola data jumlah penumpang busway periode masa lalu yang dimasukkan ke dalam sistem dilakukan proses pelatihan menggunakan jaringan syaraf tiruan dan algoritma pembelajaran backpropagation. Dengan menggunakan 365 data, prediksi jumlah armada busway menggunakan metode backpropagation mampu memprediksi dengan tingkat akurasi 94,34% dan rata-rata error sebesar 2,42. Minimum error sebesar 0,05 dan maksimum error sebesar 9,51. Model arsitektur jaringan terdiri dari dua tahap, untuk tahap pertama adalah 9 input, 10 nodes hidden layer, dan satu output. Kemudian tahap kedua adalah 3 input, 5 nodes hidden layer, dan satu output. Kata kunci: busway, prediksi, jaringan syaraf tiruan, backpropagation ABSTRACT The use of public transportation is recommended by the government in an effort to reduce congestion in the capital, Jakarta. The capital city of Jakarta has established a Bus Rapid Transit (BRT) transportation system that has been operating since 2004, called Transjakarta. These transportation users are increasing every year, so that PT. Transjakarta needs to increase the number of buses in corridors with passengers with large quotas. Artificial neural network can predict the number of busway fleets by means of the pattern of data on the number of buses in the past period that is entered into the system by the training process using artificial neural networks and backpropagation learning algorithms. By using 365 data, the prediction of the number of buses using the backpropagation method is able to predict with an accuracy rate of 94.34% and an average error of 2.42. The minimum error is 0.05 and the maximum error is 9.51. The network architecture model consists of two stages, for the first stage are 9 inputs, 10 hidden layer nodes, and one output. Then the second stage is 3 inputs, 5 hidden layer nodes, and one output. Keywords: busway, prediction, artificial neural network, backpropagation
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FE/ELK 20 036 |
| NIM/NIDN Creators: | 41418320025 |
| Uncontrolled Keywords: | Kata kunci: busway, prediksi, jaringan syaraf tiruan, backpropagation |
| Subjects: | 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan > 621.3 Electrical Engineering, Lighting, Superconductivity, Magnetic Engineering, Applied Optics, Paraphotic Technology, Electronics Communications Engineering, Computers/Teknik Elektro, Pencahayaan, Superkonduktivitas, Teknik Magnetik, Optik Terapan, Tekn |
| Divisions: | Fakultas Teknik > Teknik Elektro |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 16 Jun 2022 01:52 |
| Last Modified: | 16 Jun 2022 01:52 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/63437 |
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