Implementasi Algoritma Backpropagation Untuk Memprediksi Kelulusan Mahasiswa

Yalidhan, Muhammad Dedek

doi:10.20527/klik.v5i2.152

Cited by 10 publications

(10 citation statements)

References 1 publication

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“…The Backpropagation algorithm is a supervised learning algorithm. The backpropagation algorithm uses a perceptron that has many layers to change the weights connected to the neurons in the hidden layer [11].The backpropagation algorithm is the development of the multilayer perceptron (MLP) algorithm [12]. Backpropagation is a simple and clear iterative algorithm that usually works well even with complex data [13].…”

Section: Backpropagationmentioning

confidence: 99%

Comparison of Performance Metrics Level of Restricted Boltzmann Machine and Backpropagation Algorithms in Detecting Diabetes Mellitus Disease

Anggoro

Hajiati²

2023

Iraqi Journal of Science

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Diabetes is a disease caused by high sugar levels. Currently, diabetes is one of the most common diseases in the number of people with diabetes worldwide. The increase in diabetes is caused by the delay in establishing the diagnosis of the disease. Therefore, an initial action is needed as a solution that requires the most appropriate and accurate data mining to manage diabetes mellitus. The algorithms used are artificial neural network algorithms, namely Restricted Boltzmann Machine and Backpropagation. This research aims to compare the two algorithms to find which algorithm can produce high accuracy, and determine which algorithm is more accurate in detecting diabetes mellitus. Several stages were involved in this research, including data collection, data pre-processing, data processing, and evaluation models. This research shows that the Restricted Boltzmann Machine algorithm achieved accuracy of 82.02% while the Backpropagation algorithm reached87.01% when using the normalization method. Thus, the diabetes mellitus dataset used can be said to have a better value for the backpropagation algorithm than the restricted Boltzmann machine algorithm.

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Section: Backpropagationmentioning

confidence: 99%

Comparison of Performance Metrics Level of Restricted Boltzmann Machine and Backpropagation Algorithms in Detecting Diabetes Mellitus Disease

Anggoro

Hajiati²

2023

Iraqi Journal of Science

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“…Bacpropogation pada riset terdahulu dipakai untuk mengenali jenis buku yang sangat banyak dipinjam oleh murid serta prediksi jumlah persediaan dan informasi pengelompokan jenis buku, hasilnya bisa menggambarkan rekemondasi tipe buku apa saja yang hendak direstock bersumber pada jenis buku. Hasil dari pengujian didapatkan tingkat error dari hasil data klasifikasi dan target, selanjutknya dalam uji regresi diketahui siqnifikasi hubungan klasifikasi, target, dan prediksi [9]. Penelitian lain yaitu tentang prediksi ketidaktepatan waktu lulus mahasiswa hal tersebut menyebabkan jumlah kelulusan tepat waktu lebih rendah tiap tahunnya.…”

Section: Pendahuluanunclassified

Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation

Rahmiyanti

Defit

Yunus

2021

jidt

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Students of SMP Negeri 2 Lengayang have different interests in determining the books they are interested in, so that the library often has difficulty determining the books that are most entered by students, this is because they have not used the right system in determining the type and number of books, only based on the estimated number. Students and subjects only, as a result school students stock books of the books they want to borrow. Based on the above, a method is needed to predict and classify the amount of book stock in the future. The data used is a recap of monthly book lending, from 2018 to 2020 in the third month, with a total of 1653 transactions and 5 types of books processed, then the data is analyzed using the Backpropogation method. The results obtained are using a 5-3-1 pattern with a learning rate of 0.01, a goal of 0.01, the number of input units for the Weapon layer 5, the number of units in the hidden layer and the number of output layer units that are placed on 1 layer, and to carry out training using two phases namely feedforward and backpropagation phases. It is removed from this research that the backpropagation method can provide a classification prediction of the number of books that must be provided in the following year based on the number of data entered or the number of data entered.

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“…Hal ini memungkinkan neuron yang membentuk jaringan akan digunakan sebagai pemecahan masalah dari "program itu sendiri". (Yalidhan, 2018) e.…”

Section: Artificial Neural Networkunclassified

Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network

Hadianto¹,

Novitasari²,

Rahmawati³

2019

pilar

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Payment of loans that experience difficulties in repayment or often called bad credit is a very detrimental thing for the bank, with the occurrence of bad credit the bank does not have the maximum ability to make money for investment. Choosing the right customer must go through the right analysis because the decision to approve or disagree with the loan is the main point that determines the possibility of bad credit. This study aims to classify eligible customers to obtain loans by taking into account existing parameters such as age, total income, number of families, monthly expenditure average, education level and others. This study uses a data mining classification method with a neural network model, to assess the accuracy of data processing using rapid miners then proceed with measurements using confusion matrix, ROC curve. The results of the neural network algorithm after going through confusion matrix testing, the ROC curve shows a very high accuracy value, and the dominant value of AUC and algorithm. The accuracy value is 98.24% with AUC of 0.979.

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Implementasi Algoritma Backpropagation Untuk Memprediksi Kelulusan Mahasiswa

Cited by 10 publications

References 1 publication

Comparison of Performance Metrics Level of Restricted Boltzmann Machine and Backpropagation Algorithms in Detecting Diabetes Mellitus Disease

Comparison of Performance Metrics Level of Restricted Boltzmann Machine and Backpropagation Algorithms in Detecting Diabetes Mellitus Disease

Prediksi dan Klasifikasi Buku Menggunakan Metode Backpropagation

Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network

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