Prediksi Nilai Calon Mahasiswa dengan Algoritma Backpropagation (Studi Kasus: Data Kaggle)

Nafi’iyah, Nur; Ahmad, Rizki Ardhian; Mujilahwati, Siti

doi:10.32672/jnkti.v3i1.1945

Cited by 6 publications

(6 citation statements)

References 2 publications

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“…Dalam pendidikan nilai merupakan sebagai tolak ukur dalam menilai kemampuan siswa dalam memahami apa yang mereka pahami di sekolah. Hal ini bermaksud agar selama proses pembelajaran tercipta hubungan baik antara pengajar dan siswa terutama dalam mata pembelajaran yang di ujikan secara nasional yaitu Ilmu Pengetahuan Alam, Bahasa Indonesia dan Matematika [1]. Prediksi memiliki arti menebak atau forecasting sesuatu yang akan terjadi di masa depan.…”

Section: Pendahuluanunclassified

Backpropagation Artificial Neural Networks Prediction of Elementary School Student Graduation With Examination Practice Score

Sunaryo,

Hastono,

Abelia

2023

Journal of Technology and Health

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Backpropagation method is a computer technique to help predict and sort data. This method is usually used to change the connection between parts of the computer's brain in the hidden layer. Meanwhile, the Nervous System Network (ANN) is an information processing system that is very similar to the function of human brain cells. Value is a benchmark for a student's graduation, if the student'sscore is getting better, the more opportunities for the student's graduation. In predicting this pass using the method of Artificial Neural Networks (ANN), namely Backpropagation and using Matlab software with the MSE (Mean Square Error) result of 0.099512. Keywords: Graduation Prediction, Backpropagation, Examination Practice Value, Artificial Neural Networks, Matlab

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Section: Pendahuluanunclassified

Backpropagation Artificial Neural Networks Prediction of Elementary School Student Graduation With Examination Practice Score

Sunaryo,

Hastono,

Abelia

2023

Journal of Technology and Health

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“…Fungsi transfer sering digunakan dalam machine learning neural network khususnya dalam metode propogasi balik, yaitu: linear purelin, [12] log sigmoid (logsig), [13] sigmoid tangen hiperbolik (tansig). [14] Secara umum algoritma backpropagation standar menggunakan fungsi pelatihan gradient discent, yaitu : traingda, traingdm, traingd , trainngdx, [15] dan banyak lagi fungsi pelatihan yang dapat digunakan dalam optimasi yang mempengaruhi hasil komputasi, misalnya gradien konjugasi berskala (trainscg), [16] fungsi pelatihan urutan acak dengan fungsi pembelajaran (trainr), [17] regulasi bayesian (trainbr), [18] pelatihan bias perintah acak yang tak terawasi (trainru), [19] levenberg marquardt (trainlm), [20], [21] one step secant (trainos), [22] fungsi pembelajaran inkremental berurutan (trains), [23] fungsi pelatihan tanpa pengawasan (trainbu), fungsi batch memakai aturan pembelajaran bias (trainb), [24] fungsi konjugasi gradien (traincgf, traincgb, traingcgp), [25] resilient (trainrp), [26] BFGS quasi newton, [27] alur siklus bias (trainc), [28] BFGS quasi newton referensi adaftif kontrol (trainbfgc). [29], [30] Menggunakan fungsi pelatihan dan transfer menghasilkan tingkat keakuratan peramalan yang berbeda-beda dengan menggunakan parameter atau metode, dan data yang digunakan dalam pengujian.…”

Section: Pendahuluanunclassified

Prediksi Prestasi Mahasiswa Dengan Menggunakan Algoritma Backpropagation

Sonang¹,

Purba²,

Sirait³

2022

Tekinkom

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This study aims to overcome the problems in predicting student achievement at the Polytechnic Business Indonesia Pematangsiantar. To predict student achievement is done by applying Backpropagation algorithm and implement it into Matlab software. Backpropagation algorithm is one of the methods on artificial neural networks that is quite reliable in solving problems including prediction. In this study conducted on the object of students semester One with a lot of data samples 26 samples. The data sample is divided into two parts, 70% of the data is used as training data and 30% of the data is used as testing data. This study uses ten architectural models, namely 9-2-1, 9-3-1, 9-4-1, 9-5-1, 9-6-1, 9-7-1, 9-8-1, 9-9-1, 9-10-1, 9-11-1. Of the ten Backpropagation network architecture models implemented in predicting student achievement in Matlab software obtained the best output is 9-2-1 pattern with epoch 8149, time duration for 17 seconds, and MSE (error rate) value of 2.80 e-05 for training and MSE (error rate) of 0.1248 with accuracy of 87.5% for testing. The best architecture obtained is expected to be used as a picture by the academic Polytechnic Business Indonesia (PBI) in predicting student achievement.

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“…Backpropagation merupakan model JST yang paling banyak digunakan.Salah satu kelebihan JST yaitu mampu mempelajari bagaimana belajar dari data yang diberikan atau melatih diri dari data awal. JST mampu merepresentasikan diri sendiri bahkan membuat suatu organisasi sendiri atau dari pengetahuan yang diterima saat belajar [11]. Oleh sebab itu, penelitian ini memilih metode Backpropagation agar dapat memprediksi tingkatpemahaman siswa terhadap bimbingan TIK dalam pembelajaran dimasa Pandemi Covid-19.…”

Section: Pendahuluanunclassified

Tingkat Pemahaman Siswa dalam Pembelajaran Daring dan Tatap Muka Langsung dalam Masa Pandemi Covid-19

2021

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The Covid-19 pandemic has a major impact on the world of education. Government policies to implement Distance Learning (PJJ) have an impact on learning in schools. Increasing ICT competence is needed to support the smooth running of PJJ. One of them is through ICT guidance activities during the Covid-19 Pandemic. SMP Negeri 1 Lengayang carried out online and face-to-face ICT guidance activities during the Covid-19 Pandemic. However, student learning outcomes in online and face-to-face learning have not shown maximum results. Various obstacles arise that affect student learning outcomes. Teachers have difficulty measuring the level of students' understanding of ICT guidance. Predicting the level of understanding of students is important as a measure of learning success during the Covid-19 Pandemic. This study aims to predict the level of understanding of students in online and face-to-face learning during the Covid-19 period, so that it can also help schools to take the right policies to improve the quality of learning for the future. This study uses the Backpropagation method of Artificial Neural Network (ANN). ANN is a part of artificial intelligence that can be used to predict. The data that is managed is a recap of the value of student cognitive learning outcomes during ICT guidance in online and face-to-face learning during the Covid-19 Pandemic. The results of calculations using the Backpropagation method with the Matlab application produce a percentage value for the level of student understanding, so that the accuracy value in prediction is obtained. With the results of testing the predictive accuracy of the level of understanding online and face-to-face with the 3-10-1 pattern, the best accuracy value is 95%. The prediction results can measure the level of students' understanding of learning during the Covid 19 Pandemic towards ICT guidance

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Prediksi Nilai Calon Mahasiswa dengan Algoritma Backpropagation (Studi Kasus: Data Kaggle)

Cited by 6 publications

References 2 publications

Backpropagation Artificial Neural Networks Prediction of Elementary School Student Graduation With Examination Practice Score

Backpropagation Artificial Neural Networks Prediction of Elementary School Student Graduation With Examination Practice Score

Prediksi Prestasi Mahasiswa Dengan Menggunakan Algoritma Backpropagation

Tingkat Pemahaman Siswa dalam Pembelajaran Daring dan Tatap Muka Langsung dalam Masa Pandemi Covid-19

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