Classification of the Period Undergraduate Study Using Back-propagation Neural Network

Prasetyawan, Purwono; Ahmad, Imam; Borman, Rohmat Indra; Ardiansyah, Ardiansyah; Pahlevi, Yogi Aziz; Kurniawan, Dwi Ely

doi:10.1109/incae.2018.8579389

Cited by 38 publications

(23 citation statements)

References 5 publications

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“…Penjualan pada Toko Prima Motor berlangsung setiap hari sehingga data transaksi semakin lama akan semakin banyak. Akan tetapi, data tersebut hanya digunakan sebagai arsip bagi toko sedangkan data transaksi tersebut dapat diolah kembali menjadi suatu informasi baru yang dapat digunakan untuk proses pengambilan keputusan dengan menggunakan data mining [1]- [3].…”

Section: Pendahuluanunclassified

PENERAPAN ALGORITMA APRIORI PADA PENJUALAN SUKU CADANG KENDARAAN RODA DUA (Studi Kasus: Toko Prima Motor Sidomulyo)

Abidin

Amartya

Nurdin³

2022

JTI

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Prima Motor Shop is engaged in the sale of spare parts for two-wheeled vehicles with several brands of spare parts. Sales at Prima Motor Stores take place every day so that the transaction data will increase over time. However, the data is only used as an archive for the store. By using data mining the data will be reprocessed into information that can be used for the decision-making process. Transaction data is processed using association techniques using apriori algorithm. The a priori algorithm will calculate the support value of each item and find the frequent item set that meets the minimum confidence requirements. The minimum value for the support parameter is 25% and the minimum value for the confidence parameter is 50%. The results of the application of the apriori algorithm produce 13 association rules including 2 association rules for the Suzuki brand, 6 association rules for the Honda brand and 5 association rules for the Yamaha brand that meet the minimum requirements of two parameters, namely support and confidence parameters and tested using lift ratio testing to determine whether the resulting association rules are valid or invalid. The most sold item for the Suzuki brand is an item with code B01 namely Cool Starter Satria FU, for the Honda brand is an item with the code C14 namely Seal Shock Front Tiger, for the Yamaha brand is an item with code D01 namely Cool Starter Jupiter Z, Vega ZR, Mio J This can minimize the inventory vacancy of each of the most sold items of each product from the 3 parts brands.

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Section: Pendahuluanunclassified

PENERAPAN ALGORITMA APRIORI PADA PENJUALAN SUKU CADANG KENDARAAN RODA DUA (Studi Kasus: Toko Prima Motor Sidomulyo)

Abidin

Amartya

Nurdin³

2022

JTI

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“…Wei takes manufacturing companies that issue bonds as samples to build a financial early warning model based on decision tree integration, which can effectively improve the correct identification rate of companies in a financial crisis [18]. A big data mining method based on PSO-BPNN for financial risk management of IoT deployment in commercial banks by Zhou and Zhou et al [19,20], e method utilizes Apache Spark and Hadoop HDFS technology to build a Nonlinear Parallel Optimization Model. Huang et al compared the application effects of several common neural network models in the Chinese SME data set to explore enterprise credit risk factors [21].…”

Section: Related Workmentioning

confidence: 99%

A Financial Risk Early Warning of Listed Companies Based on PCA and BP Neural Network

Zhang

Zhou

Wang

2022

Mobile Information Systems

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Financial risk, as one of the most influential and destructive risks in business, will make enterprises unable to escape the fate of bankruptcy if not warned and prevented in time. In the paper, we conducted research on the financial risk early warning of listed companies. A total of 250 companies were randomly selected from the Chinese A-share market from 2019 to 2021. By building the 26 financial indicators of listed companies and constructing the PCA-BP neural network, we compared the financial risk early warning effects among PCA-BPNN, SVM, and Logistic. It is found that the financial data processed by PCA can better adapt to the financial risk early warning model. The PCA-BPNN model improved the prediction accuracy of the financial risk early warning, which has strong generalization ability for the prediction of financial risk. Research findings have certain reference significance for precise judgment on the financial risk of companies.

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“…Setelah objek dapat dikenal cirinya maka proses identifikasi dapat dilakukan dengan mudah. Identifikasi citra merupakan proses mengenali citra dengan melakukan pengelompokkan piksel-piksel pada citra tersebut agar didaptkan kelas-kelas atau kategori berdasarkan karakteristik tertentu [6] [7]. Salah satu parameter yang dapat digunakan untuk identifikasi citra adalah euclidean distance.…”

Section: Pendahuluanunclassified

Identifikasi Citra Tanaman Obat Jenis Rimpang dengan Euclidean Distance Berdasarkan Ciri Bentuk dan Tekstur

Nurnaningsih

Alamsyah

Herdiansah

et al. 2021

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In the midst of the Covid-19 pandemic, increasing the body's immunity is very important. Some experts suggest consuming medicinal plants or herbs to boost immunity. In addition to being used as a cooking spice, this rhizome type plant turns out to have properties and benefits for health, especially to increase immunity. However, many people do not know and it is difficult to distinguish the type of rhizome plant. This type of rhizome plant can be identified based on the characteristics seen from the shape and texture. However, most people judge the type of rhizome has a shape that is difficult to distinguish. This study aims to determine the type of medicinal plant rhizome with Euclidean distance and extraction of shape and texture. Extraction of shape features using metric and eccentricity parameters. This parameter is considered to be able to recognize shape objects and can distinguish them from other objects. Meanwhile, texture feature extraction uses Gray Level Co-occurence Matrix (GLCM) with contrast, correlation, energy, and homogeneity parameters. For the identification process, Euclidean distance is used which serves to represent the level of two images that consider the distance value from Euclidean. From the results of the evaluation using a confusion matrix by calculating precision, recall and accuracy, it gets a precision value of 83%, recal 87% and an accuracy of 85%. These results indicate that the Euclidean distance and extraction of shape and texture features can identify the object image of medicinal plants with rhizome types well

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Classification of the Period Undergraduate Study Using Back-propagation Neural Network

Cited by 38 publications

References 5 publications

PENERAPAN ALGORITMA APRIORI PADA PENJUALAN SUKU CADANG KENDARAAN RODA DUA (Studi Kasus: Toko Prima Motor Sidomulyo)

PENERAPAN ALGORITMA APRIORI PADA PENJUALAN SUKU CADANG KENDARAAN RODA DUA (Studi Kasus: Toko Prima Motor Sidomulyo)

A Financial Risk Early Warning of Listed Companies Based on PCA and BP Neural Network

Identifikasi Citra Tanaman Obat Jenis Rimpang dengan Euclidean Distance Berdasarkan Ciri Bentuk dan Tekstur

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