SISTEM INFORMASI PERAMALAN PERSEDIAAN BARANG MENGGUNAKAN METODE WEIGHTED MOVING AVERAGE
Bibliographic record
Abstract
ABSTRACTPT. Surya Cemerlang Niaga Abadi is a company engaged in the distribution of imported beef. There are constraints experienced by companies, such demand with supply is not balanced and recording of inventory is still manually by hand. Determination of the inventory is still manually by hand so as to determine how the merchandise will be provided the company must first compare the number of items that came out with a comparison of data before the data is also of recent expenditures.Based on these problems, this research aims to design and build information systems that can assist in data processing and forecasting inventory items for the next month. Web-based information system is built with the Weighted Moving Average method for inventory forecasting process. The data used in forecasting is the last three months of data.The results of this study indicate successful information system designed and built. Based on testing with Black box testing system functionality information obtained is in conformity with the designs. Based on the calculation error last three months weights 0.1, 0.2, 0.7 is obtained MSE (Mean Squared Error) is 0.00834 that shows the smallest value and proper use for forecasting.Keywords: Information Systems, Inventory, Forecasting, Weighted Moving AverageABSTRAKPT. Surya Cemerlang Niaga Abadi merupakan sebuah perusahaan yang bergerak dibidang distribusi daging import. Terdapat kendala yang dialami oleh perusahaan antara lain permintaan dengan persediaan tidak seimbang dan pencatatan persediaan barang juga masih manual dengan tulisan tangan. Penentuan persediaan barang itu sendiri masih dilakukan secara manual dengan tulisan tangan jadi untuk menentukan berapa jumlah persediaan barang yang akan disediakan perusahaan harus terlebih dahulu membandingkan jumlah barang yang keluar dengan perbandingan data sebelumnya juga data pengeluaran barang yang baru terjadi. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk merancang dan membangun Sistem Informasi yang dapat membantu dalam pengolahan data barang dan peramalan persediaan barang untuk bulan berikutnya. Sistem informasi dibangung berbasis web dengan metode Weighted Moving Average untuk proses peramalan persediaan barang. Data yang digunakan dalam peramalan adalah data tiga bulan terakhir. Hasil penelitian ini menunjukkan sistem informasi berhasil dirancang dan dibangun. Berdasarkan pengujian dengan Black box testing didapatkan fungsionalitas sistem informasi sudah sesuai dengan rancangan yang dibuat. Berdasarkan hasil perhitungan error bobot tiga bulan terakhir 0.1, 0.2, 0.7 diperoleh nilai MSE (Mean Squared Error) adalah 0.00834 yang menunjukkan nilai terkecil dan tepat digunakan untuk peramalan.Kata Kunci : Sistem Informasi, Persediaan, Peramalan, Weighted Moving Average
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".