ANALISIS KEPUASAN MAHASISWA TERHADAP SISTEM PEMBELAJARAN ONLINE PADA MASA PANDEMIC COVID 19 DI STMIK AKAKOM DENGAN METODE NAIVE BAYES
Bibliographic record
Abstract
In accordance with the circular of the Minister of Education and Culture of the Republic of Indonesia regarding Circular Letter Number 4 of 2020 concerning the Implementation of Educational Policies in the Emergency Period for the Spread of Corona Virus Disease (COVID-19), the learning system implemented is online. STMIK AKAKOM is one of the universities that has also implemented online lectures since the government's policy for the home learning system was established. Various efforts have been made by STMIK AKAKOM to carry out the online learning process during this covid-19 pandemic. Therefore, researchers conducted a study that aims to analyze the success of the online learning system for students conducted at STMIK AKAKOM with the Naive Bayes method approach, using 4 (four) criteria, namely Communication, Building a Learning Atmosphere, Assessment of Students, and Delivery of Lecture Materials. The level of satisfaction assessment using a Likert scale (likert scale) 5 points with the same interval. starting from point 1 (one) which states very less, to point 5 (five) which states very well. Based on the results of calculations that have been carried out, it can be seen that the classification of testing data from respondents numbered R89 to R93 for online learning systems during the Covid 19 pandemic is satisfied.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".