Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation

Syafiq, Muhammad; Hartama, Dedy; Kirana, Ika Okta; Gunawan, Indra; Wanto, Anjar

doi:10.30865/jurikom.v7i1.1963

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Sale is an activity performed by a company To maintain the growth of its business and to produce desired benefits. Activity distribution is part of purposeful marketing To move products from producer to consumers [17].…”

Section: Salementioning

confidence: 99%

Implementation Network Nerves Imitation Using the Backpropagation Method For Prediction Sale Groceries

Nuraitul Janah,

Hastono,

Nurmala

et al. 2023

aplied.science.technology.research.journal

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Abstract The sale of groceries is one of the important things _ in the retail industry, Predicting the sale of groceries accurately can help the company in planning supplies, and making decisions for business. In implementing network nerves imitation backpropagation method as tool predictions For sale basic food.Method This can used To practice network nerve imitation to recognize patterns and relationships between influencing factors _ _ in the sale of groceries, like price, promotion, and economic factors. Data sale collected groceries _ from periods previously used as a training dataset For practice network nerves imitation. Aside from That For shows that implementation network nerves imitation with backpropagation method can give predictions of sales enough basic necessities accurate predictions This is also used by other companies for optimizing inventory, managing promotional strategies, and responding to change conditions company with more effectively. .

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Section: Salementioning

confidence: 99%

Implementation Network Nerves Imitation Using the Backpropagation Method For Prediction Sale Groceries

Nuraitul Janah,

Hastono,

Nurmala

et al. 2023

aplied.science.technology.research.journal

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“…Produksi susu cenderung berubah sehingga sulit untuk diprediksi. Untuk melakukan pengujian ini digunakan software Matlab R2011b yang dimana ini sangat membantu dalam melakukan penelitian [20].…”

Section: Pendahuluanunclassified

Jaringan Syaraf Tiruan dengan Algoritma Backpropagation dalam Memprediksi Hasil Asesmen Nasional Berbasis Komputer (ANBK) SMP Se Kota Sawahlunto

Yuberta¹

2022

jidt

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Abstract The National Computer-Based Assessment for SMP level is a quality assessment program for all SMP level schools. This has only been simulated in 2019 and in 2021 this is the first stage of testing. Adaptation to ANBK needs to be done quickly so that the School Quality Score becomes good from time to time and the main goal of the education unit, namely the development of student competence and character, is achieved. Finding a solution to improve the quality of SMP in Sawahlunto City based on ANBK using ANN with Backpropagation Algorithm. The data used in this study was sourced from the Education Office of Sawahlunto City where as many as 11 schools participated in ANBK at the junior high school level. Based on the 2021 ANBK simulation data, the results obtained are above the minimum competency of 11 schools. Furthermore, the data is processed using Matlab software. The processing implementation involves four input variables (reading literacy, numeracy, character survey and learning environment survey). Of the 33 data tested using variations of test data and training data, which are then processed using variations in the learning rate and number of epoch parameters. From the test results obtained the level of accuracy of pattern recognition on the backpropagation method with a learning rate variation of 0.2 and the number of epochs 1000. The results of testing this method are as many as 11 junior high schools that have passed. So that the level of accuracy is 99,9987%. The prediction results of ANBK SMP in Sawahlunto City can already describe the quality of education in SMP in Sawahlunto City. With an achievement level above the district/city average of 36.36%, it can become accurate information to improve the quality of teaching and learning and improve student achievement. Keywords: Artificial Neural Networks; Backpropagation; ANBK; Computer-Based National Assessment; Student; Education.

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“…Prediksi diperoleh berdasarkan model terbaik 5-17-1 dengan menggunakan rumus persamaan (2) [38]- [41].…”

Section: Hasil Prediksiunclassified

Prediksi Hasil Produksi Tanaman Tomat di Indonesia Menurut Provinsi Menggunakan Algoritma Fletcher-Reeves

Fajri¹,

Gunawan²,

Batubara³

et al. 2022

bits

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Tomatoes are essential for Indonesians because they have high economic and nutritional value. In addition, as population growth increases, the demand for tomatoes also increases. Based on this, it is essential to research to predict the future development of tomato crop production. The research in this paper uses a dataset of tomato plant production in Indonesia, which is spread across 34 provinces in the last seven years, namely from 2015 to 2021), which is sourced from the Indonesian Central Bureau of Statistics and the District/City Agriculture Service of each Province. The algorithm proposed in this study is the Fletcher-Reeves Conjugate Gradient Algorithm which will be processed with the help of Matlab2011b. Research analysis with three network architectural models: 5-7-1, 5-13-1, and 5-17-1. Based on a network comparison of the three architectural models, the best result is the 5-17-1 model because the MSE value is the smallest compared to the other two models, namely 0.0009915 compared to 0.0010851 and 0.0049764, as well as the highest level of accuracy, namely by 94% versus 91% and 88%. Therefore the 5-17-1 model is used to predict the yield of tomato production in Indonesia for the future (2022 and 2023). Based on the prediction results at the end of 2022 and 2023, there are 18 provinces where tomato crop production has the potential to increase, although not too significantly. The prediction of tomato production using the Fletcher-Reeves algorithm is quite good because it produces a small error rate and high accuracy.

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Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation

Cited by 5 publications

References 3 publications

Implementation Network Nerves Imitation Using the Backpropagation Method For Prediction Sale Groceries

Implementation Network Nerves Imitation Using the Backpropagation Method For Prediction Sale Groceries

Jaringan Syaraf Tiruan dengan Algoritma Backpropagation dalam Memprediksi Hasil Asesmen Nasional Berbasis Komputer (ANBK) SMP Se Kota Sawahlunto

Prediksi Hasil Produksi Tanaman Tomat di Indonesia Menurut Provinsi Menggunakan Algoritma Fletcher-Reeves

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