Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Aini, Hijratul; Haviluddin, Haviluddin; Budiman, Edy; Wati, Masna; Puspitasari, Novianti

doi:10.30872/jsakti.v1i1.2261

Cited by 11 publications

(13 citation statements)

References 10 publications

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“…Neuron adalah bagian dasar pemrosesan informasi yang terdapat di dalam jaringan saraf tiruan [8]. Backpropagation mulai dikenal pada tahun 1974 oleh Paul Werbos, tahun 1982 David Parker melakukan pengembangan, dan Rumelhart dan McCelland mengembangkan lagi pada tahun 1986 [9]. Backpropagation merupakan salah satu metode yang cukup baik dalam melakukan peramalan.…”

Section: Pendahuluanunclassified

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Prediksi Pencapaian Target Peserta Keluarga Berencana Pasca Persalinan Menggunakan Algoritma Backpropagation

2021

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Population growth in Indonesia continues to increase, so the government makes a program to control the rate of growth of the population, namely the Family Planning Program (KB). The implementation of family planning also has another objective, namely to reduce the risk of maternal death after childbirth. To measure the level of increasing target achievement of postpartum family planning participants. So that it can be a reference for the DPPKBP3A in carrying out the postpartum family planning program. Data from the Population Control, Family Planning, Women Empowerment and Child Protection (DPPKBP3A) District Lima Puluh Kota data processed in this study is data on the achievement of postpartum family planning participants from 2018 to 2020. Data processing uses the Backpropagation algorithm through several stages, namely the stage initialization, activation stage, weight training (weight change) and iteration stage. One of the results obtained from the calculation is the comparison of the target with the output gradient error in Suliki District in 2018, namely the target of 0.11311 and the result of the error gradient output is -0.1171. The prediction results obtained from this process become a reference for the Population Control, Family Planning and Women Empowerment and Child Protection Agency (DPPKBP3A) of District Lima Puluh Kota to implement the implementation of postpartum family planning programs to the community the following year.

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Section: Pendahuluanunclassified

“…Nilai maksimal dalam fungsi aktivasi sigmoid biner adalah 1, sehingga data awal berupa bilangan bulat diubah menjadi bilangan berkoma dengan range [0,1]. [0,9] menggunakan rumus yang disajikan pada Persamaan (1).…”

Section: Normalisasi Dataunclassified

Prediksi Pencapaian Target Peserta Keluarga Berencana Pasca Persalinan Menggunakan Algoritma Backpropagation

2021

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“…Metode JST merupakan metode yang sangat baik untuk mendapatkan hasil peramalan yang lebih baik (Aini et al, 2019;Haviluddin & Dengen, 2016;Ma et al, 2016;Majhi et al, 2014;Purnawansyah & Haviluddin, 2014;Sakinah et al, 2018;Simanungkalit et al, 2020;Syafiq et al, 2020). Model akurasi peramalan terbaik yang digunakan dalam JST adalah MSE Simanungkalit et al, 2020).…”

Section: Pendahuluanunclassified

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Puspitasari

Haviluddin

Kustiawan

et al. 2021

JISKa

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The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.

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“…Persamaan penelitian ini dengan penelitian yang dilakukan oleh penulis adalah membahas tentang kelapa sawit. Perbedaannya adalah penelitian diatas memprediksi jumlah produksi dari minyak kelapa sawit menggunakan metode Backpropagation Neural Network, sedangkan penelitian penulis menentukan harga TBS Kelapa Sawit menggunakan metode Fuzzy Logic [7].…”

Section: Pendahuluanunclassified

Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic

2020

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The purpose of this study is to create a system for determining the price of oil palm fresh fruit bunches using the Fuzzy Logic method. Oil palm plantations are plantations that have natural resources that can generate large profits, which are used as cooking oil and much more. Palm oil FFB consists of Crude Palm Oil (CPO), which is fruit flesh oil and Palm Kernel (PK), which is the kernel of palm oil. This research makes the system use 5 input variables that is CPO price, IKS price, K Index, CPO Rendemen and IKS Rendemen, and one output variable which is the price of oil palm FFB. This system uses the Fuzzy Logic method because in this study there is fuzzy that can be determined, namely the price of oil palm-based TBS. This system uses 100 data to request using MAPE so that a percentage value is 85.75%. While the average value of the difference between the actual data and the results of the fuzzy Mamdani data is 17852 with an average percentage of errors of the fuzzy Mamdani data is 14.25%. The result of this system is the variable price of oil palm FFB output based on 5 variable inputs. The Mamdani Fuzzy Logic method determines the outcome of an uncertain value. Fuzzy logic starts from the formation of fuzzy sets, then implications, then rules, and then defuzzification.This is an open access article under the CC-BY-SA license.

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Prediksi Produksi Minyak Kelapa Sawit Menggunakan Metode Backpropagation Neural Network

Cited by 11 publications

References 10 publications

Prediksi Pencapaian Target Peserta Keluarga Berencana Pasca Persalinan Menggunakan Algoritma Backpropagation

Prediksi Pencapaian Target Peserta Keluarga Berencana Pasca Persalinan Menggunakan Algoritma Backpropagation

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Penentuan Harga Tandan Buah Segar (TBS) Kelapa Sawit Menggunakan Metode Fuzzy Logic

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