Fatwa, Rosintan (2020) Penerapan Metode Extreme Learning Machine Untuk Prediksi Konsumsi Batubara Sektor Pembangkit Listrik Tenaga Uap. Sarjana thesis, Universitas Brawijaya.
Abstract
Pembangkit Listrik Tenaga Uap (PLTU) merupakan pembangkit listrik yang memanfaatkan batubara sebagai bahan bakar. Sektor PLTU menjadi sektor pendominasi penyerapan batubara domestik. Selama periode 2010 – 2015, konsumsi batubara terus meningkat seiring adanya proyek pembangkit listrik 35.000 MW yang dirancang pada periode 2015-2019, dimana sejumlah 19.940 MW (56%) merupakan pembangkit dengan jenis PLTU berbahan bakar batubara. Berdasarkan data dari Direktur Jendral Mineral dan batubara pada kementerian ESDM, menyebutkan bahwa kenaikan konsumsi batbara dikarenakan PLTU yang beroperasi bertambah dan perkembangan ekonomi yang berbanding lurus dengan peningkatan konsumsi batubara nasional. Berdasarkan permasalahan tersebut maka prediksi konsumsi batubara pada sektor PLTU diperlukan agar konsumsi batubara bisa dikendalikan sesuai dengan produksinya. Pada penelitian ini, proses prediksi dilakukan dalam beberapa proses, yaitu normalisasi data, perhitungan prediksi menggunakan Extreme Learning Machine, denormalisasi data, dan nilai error menggunakan MAPE. Berdasarkan hasil pengujian yang telah dilakukan terhadap data konsumsi batubara harian selama tahun 2018 di PLTU Tanjung Jati B Unit 1 & 2 diperoleh nilai MAPE terkecil sebesar 6,603% dengan banyak fitur 2, jumlah hidden neuron sebanyak 4, dan perbandingan persentase data training dan data testing 70%:30% dengan menggunakan fungsi aktivasi Sigmoid.
English Abstract
PLTU is a power station that utilizes coal as fuel. The PLTU sector is a dominant sector in absorbing domestic coal. During the period 2010 - 2015, coal consumption continued to increase along with the 35,000 MW power plant project which was designed in the 2015-2019 period, of which 19,940 MW (56%) was a coal-fired power plant. Based on data from the Director General of Mineral and Coal at the Ministry of Energy and Mineral Resources, said that the increase in coal consumption is due to the growing PLTU and the economic development which is directly proportional to the increase in national coal consumption. Based on these problems, the prediction of coal consumption in the power plant sector is needed so that coal consumption can be controlled in accordance with its production. In this study, the prediction process is carried out in several processes, namely data normalization, prediction calculation using Extreme Learning Machine, data denormalization, and error values using MAPE. Based on the results of tests conducted on daily coal consumption data for 2018 at the Tanjung Jati B PLTU Unit 1 & 2 obtained the smallest MAPE value of 6.603% with many features 2, the number of hidden neurons as much as 4, and the comparison of the percentage of training data and testing data 70 %: 30% using the Sigmoid activation function
Other obstract
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| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | SKR/FILKOM/2020/157/052003348 |
| Uncontrolled Keywords: | prediksi, konsumsi batubara, extreme learning machine, MAPE, prediction, coal consumtion, extreme learning machine, MAPE |
| Subjects: | 000 Computer science, information and general works > 003 Systems > 003.2 Forecasting and forecasts |
| Divisions: | Fakultas Ilmu Komputer > Teknik Informatika |
| Depositing User: | Budi Wahyono Wahyono |
| Date Deposited: | 10 Aug 2020 06:58 |
| Last Modified: | 14 Apr 2023 01:30 |
| URI: | http://repository.ub.ac.id/id/eprint/180897 |
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