E-SIP: Website-Based Scheduling Information System to Increase the Effectivity of Lecturer's Performance and Learning Process
Bibliographic record
Abstract
The development of internet and information technology made a system to the practical, effective, and efficient. One of the impacts is ease to make scheduling information system in university. This research is research and development (R&D) models which have aim to develop the website-based scheduling information system to increase the effectivity of lecturer’s performance and learning process in IAIN Tulungagung. E-SIP program develop by using software development life cycle in term of waterfall model. Waterfall model was selected because it was easy and efficient. This system development consisted of system need analysis, system design, implementation, testing, dissemination, and maintenance. Data collecting system using literature review, field study, and interview. Furthermore, data also collected from questionnaire scores of E-SIP validations conducted by the validator and respondents, in terms of admins. After doing some development phase and trial, the E-SIP was developed and ready to use to make scheduling process in IAIN Tulungagung. E-SIP possibly runs with using any browser supporting the java-script system. The development result of E-SIP is in terms of login page for users in three levels. The first level is Super Admin which is responsible to input all databases needed for E-SIP. The second level is Department Admin, which is responsible to arrange the individual schedule of departments. The third level is Faculty Admin, which only able to see the schedule arranged by Department Admin. The similarity of all levels is to print and see the schedule. The advantage of E-SIP is that the scheduling system is able to be controlled by online and performed simultaneously at one time as well as to check overlapping or nonautomatic schedules in the website. Besides, the disadvantage happens when there is a bug or an error resulting in that the system does not function properly and needs to be immediately resolved.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".