Pengembangan modul mahir ejaan bahasa indonesia berbasis kearifan lokal untuk siswa sekolah dasar
Bibliographic record
Abstract
This study aims to design the MEBI (Mahir Spelling Indonesian) module based on local wisdom in Rembang Regency as an alternative learning resource for sixth grade elementary school students. The R&D research method on Sugiyono's theory was modified in seven research steps, namely: 1) potential and problems, 2) data collection, 3) product design, 4) design validation, 5) design revision, 6) product trial, and 7) revision. product. Data collection techniques were carried out through observation and questionnaires. Observations during the preliminary study, while the data from expert validation and student responses were obtained from an assessment questionnaire with a Likert scale. The design of the MEBI module development begins with analyzing the material that aims to help students find the contents of the book according to their needs, namely alternative learning resources for editing materials for words/terms, phrases, sentences, paragraphs, spelling, and punctuation. The MEBI module consists of five discussion materials, namely about PUEBI, the use of capital letters, punctuation marks, prepositions, and standard words and effective sentences. Each subject is equipped with Let's Practice which encourages students to think actively and Come on Testing Ability which gives students the opportunity to measure learning achievement. The MEBI module is equipped with attractive illustrations and delivered in communicative language by the two main characters, namely Azzam and Diva. The MEBI module is printed in the form of a book with a size of 25 cm x 17.5 cm. In addition, the MEBI module is also made in the form of a flipbook application that can be operated on an Android-based smartphone. This means that the MEBI Module can be used anywhere and anytime. The conclusion of this research is the MEBI (Mahir Spelling Indonesian) Module Design in the form of printed books and flipbook applications that can be operated on Android smartphones.Keywords: Local Wisdom, Spelling Module, Elementary School
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".