Jaringan Saraf Tiruan Memprediksi Nilai Pemelajaran Siswa Dengan Metode Backpropagation ( Studi kasus : SMP Negeri 1 Salapian)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Backpropagationcial neural networks are one of the artificial representations of the human brain that are always trying to stimulate the learning process of the human brain. Backpropagation is a gradient descent method to minimize the squared of the output error. Backprorpagation works through an iterative process using a set of sample data (training data), comparing the predicted value of the network with each sample data. In each process, the weight of the relation in the network is modified to minimize the Mean Squared Error value between the predicted value from the network and the actual value. The purpose of this thesis is to be able to help teachers at SMP Negeri 1 Salakaran to predict the value of student learning. In the calculation using the maximum epouch = 10000, the target error is 0.01, and the learning rate is 0.3, then there is a calculation result where the need ratio A has a value of 0.7517, which means that the value has decreased and D has a value of 0.9202 which means that this value has increased..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it