Particle Swarm Optimizitation Untuk Optimasi Bobot Extreme Learning Machine Dalam Memprediksi Produksi Gula Kristal Putih Pabrik Gula Candi Baru-Sidoarjo

Darmayanti, Eka Yuni (2018) Particle Swarm Optimizitation Untuk Optimasi Bobot Extreme Learning Machine Dalam Memprediksi Produksi Gula Kristal Putih Pabrik Gula Candi Baru-Sidoarjo. Sarjana thesis, Universitas Brawijaya.

Abstract

Permintaan gula akan meningkat seiring dengan peningkatan jumlah penduduk, pendapatan masyarakat, dan pertumbuhan industri pengolahan makanan dan minuman. Oleh karena itu agar proses produksi gula selalu meningkat sesuai dengan kebutuhan gula itu sendiri, maka diperlukannya perencanaan produksi. Peramalan yang akurat dapat membantu perusahaan dalam mengambil keputusan untuk menentukan jumlah gula yang akan diproduksi, bahan yang dibutuhkan dan menetukan harga barang. Salah satu metode yang dapat digunakan untuk melakukan prediksi adalah algoritme Extreme Learning Machine. Akan tetapi metode tersebut dalam pemilihan input weight dan bias dipilih secara acak, hal ini dapat menyebabkan hasil yang didapat dalam perhitungan kurang maksimal. Diperlukannya kombinasi dengan algoritme Particle Swarm Optimization yang dapat melakukan optimasi nilai input weight dan bias secara optimal. Penelitian ini menggunakan 45 data giling produksi gula dengan 5 fitur. Berdasarkan penelitian yang telah dilakukan, didapatkan parameter yang optimal yaitu jumlah ukuran populasi 50, perbandingan data training 80% (36 data), jumlah hidden neuron 10, bobot inersia 0.5, dan iterasi maksimal 250. dari parameter tersebut didapatkan nilai rata-rata MAPE sebesar 0.59%. Dari hasil rata-rata MAPE yang didapat, menunjukkan bahwa penambahan algoritme PSO pada ELM dapat menentukan nilai input weight dan bias yang optimal.

English Abstract

Sugar demand will increase in line with increase in population, income, and growth in food and beverage processing industry. Therefore, in order for the sugar production process is always increasing in accordance with needs of the sugar itself, hence need for production planning. Accurate forecasting can help companies in taking decisions to determine the amount of sugar to be produced, the materials needed and determine the price of the goods. One method that can be used to do the prediction algorithm is Extreme Learning Machine. But that method in selection of input and weight bias is chosen randomly, this can lead to the results obtained in the calculation less maximum. This need for a combination with Particle Swarm Optimization algorithms that can perform optimization the input value weight and bias optimally. This research uses data 45 milled sugar production with 5 features. Based on the research that has been performed, the obtained optimal parameters, namely the number of population size 50, 80% training data comparison (36), the number of hidden neuron 10, weigh inertia 0.5, and a maximum of iterations 250. The parameter value is obtained from the average MAPE of 0.59%. From the average MAPE results obtained, shows that the addition of the PSO algorithm on ELM can determine the value of input of weight and optimal bias.

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTIK/2018/335/051805696
Uncontrolled Keywords: prediksi, optimasi, Extreme Learning Machine, Particle Swarm Optimization prediction, optimization, Extreme Learning Machine, Particle Swarm Optimization
Subjects: 000 Computer science, information and general works > 006 Special computer methods > 006.3 Artificial intelligence
Divisions: Fakultas Ilmu Komputer > Teknik Informatika
Depositing User: Budi Wahyono Wahyono
Date Deposited: 15 Nov 2018 06:51
Last Modified: 18 Oct 2021 01:39
URI: http://repository.ub.ac.id/id/eprint/13423
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