JARINGAN SYARAF TIRUAN PREDIKSI JUMLAH PENGIRIMAN BARANG MENGGUNAKAN METODE BACKPROPAGATION ( STUDIKASUS: KANTOR POS BINJAI )
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
Artificial neural networks are a branch of AI (Artificial Intelligence). Artificial neural network is an information processing paradigm which is inspired by the human brain system in receiving information and solving problems by carrying out the learning process through changes in the weight of its synapses. Pos Indonesia is an Indonesian state-owned company engaged in postal services. Currently, the form of Pos Indonesia business entity is a Limited Liability Company and is often referred to as PT. Indonesian post. This research was conducted to obtain a time benchmark when the delivery process occurs so that it can be used as a reference in shipping management control. The number of shipments of goods can be predicted by one method for prediction, namely the Backpropagation method. The Backpropagation method is a learning algorithm to reduce the error rate by adjusting the weight based on the difference in output and the desired target. This study uses unemployment data from the previous 3 years as training test data and training target data. After conducting the discussion, it produces an error value of 0.020043915 in iteration I. The results cannot be used because the error rate has not reached the target, which is 0.01.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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