PENGGUNAAN SISTEM INFORMASI GEOGRAFIS DALAM PENENTUAN KESESUAIAN LOKASI SARANA PENDIDIKAN MENENGAH DI KECAMATAN MIJEN
Bibliographic record
Abstract
Mijen District based on Semarang City Regional Regulation Number 14 of 2011 concerning Spatial Planning for Semarang City 2011-2031 is one of the educational areas in Semarang City which is planned to further develop vocational education facilities into favorite educational facilities. Education is an important aspect in the development of people's lives because it is the basis for the development of a constructive and creative mindset. The population that continues to increase needs to be balanced with the optimization of the function of public service facilities, one of which is education. To make Mijen District have superior secondary education facilities, it is necessary to fulfill the need for education in Mijen District, one of which is by paying attention to the location of education. Location of educational facilities that are safe, comfortable, strategic, and easily accessible is one of the important factors that excellent educational facilities must have. Therefore, it is necessary to identify the suitability of the location for secondary education facilities in Mijen District by taking into account factors such as area function, disaster-prone, accessibility, and service coverage. This analysis uses quantitative research methods with geographic information system tools in the form of scoring, overlay and network analysis. Geographic information systems can be used to identify the suitability of the location of secondary education facilities. The application of geographic information systems supported by further studies such as identification of demand and availability of educational facilities as well as the hierarchy of services can play a role in determining the location of appropriate and targeted secondary education facilities.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".