Prediksi Tingkat Motivasi Belajar Siswa Menggunakan Metode Backpropagation
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
Motivation has an important role in the teaching and learning process for both teachers and students. For teachers, knowing students' learning motivation is very necessary. maintain and increase students' enthusiasm for learning. For students, learning motivation can foster enthusiasm for learning so that students are encouraged to carry out learning actions. Students carry out learning activities happily because they are driven by motivation. Currently, many students are less motivated to study. Backpropagation is a supervised learning algorithm and is usually used by perceptrons with many layers to change the weights connected to neurons in the hidden layer. Based on the learning rate and maximum epoch values, artificial neural networks using the backpropagation method can predict the level of student learning motivation with convergent results or the target error is achieved with an epoch of 11 iterations and a training process time (time) of 0.00.08 seconds. From the student learning motivation criteria data which is used as training data, the training targets can be identified. Yes and no input which is transformed into 0 and 1 can predict the level of student learning motivation with low, medium and high student motivation targets with reslt testing 80%.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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